System and method for deviation settlement management for solar plant grid stability and penalty reduction

Abstract

System and method for deviation settlement management for solar plant grid stability and penalty reduction The present invention discloses a system and method for deviation settlement management for a solar power plant (102). The system comprises at least one weather monitoring unit (108) configured to collect at least one real-time data from the solar power plant (102). Further, the system comprises a deviation settlement server (118) to process the at least one real-time data, wherein the deviation settlement server (118) comprises a deviation settlement processing unit (206) configured to predict real-time power output, detect deviations between the real-time power injected and a scheduled power, managing the detected deviations. Thereafter, the system comprises at least one energy storage unit (120) to store excess electrical power upon receiving an instruction from the deviation settlement server (118) and supply the stored power to the grid (106) during periods of reduced power generation.

Description

System and method for deviation settlement management for solar plant grid stability and penalty reduction FIELD OF INVENTION

[0001] The field of invention generally relates to deviation settlement management in renewable power plants. More specifically, it relates to a system and method for deviation settlement management for solar plant grid stability and penalty reduction. BACKGROUND

[0002] Renewable energy power plants, particularly those utilizing solar and wind, are essential for sustainable development and reducing carbon footprints. The renewable energy power plants significantly contribute to decreasing greenhouse gas emissions and reducing reliance on fossil fuels. Ensuring their efficiency and reliability is vital for meeting global energy needs and environmental goals.

[0003] Efficient management of power generation from renewable sources, especially solar power, is essential for maintaining grid stability and minimizing financial penalties due to power output deviations.

[0004] Renewable energy sources frequently encounter challenges in maintaining consistent power output due to varying environmental conditions.

[0005] Existing systems often fail to precisely manage the power output deviations in real-time, leading to the grid instability and financial penalties. Further, existing systems utilize artificial intelligence and / or machine learning techniques to predict power output and employ battery energy storage systems (BESS) to mitigate the financial penalties.

[0006] Other existing systems have tried to address this problem. However, their scope was limited to broad stability enhancement through reactive and real power modulation based on general grid requirements. Further, the current methods often rely only on historical data and lack comprehensive real-time control mechanisms.

[0007] Thus, in light of the above discussion, it is implied that there is a need for a system and method for precise detection, control, correction and storage ofdeviations in renewable energy solar power plants, which is reliable, efficient and does not suffer from the problems discussed above. OBJECT OF INVENTION

[0008] The principal object of this invention is to provide a system and method for deviation settlement management for solar plant grid stability and penalty reduction.

[0009] A further object of the invention is to provide a system and method to ensure grid stability and reduce penalty payments to regulatory bodies.

[0010] Another object of the invention is to employ a digital twin module for solar power plants that replicates performance of an individual solar power plant.

[0011] Another object of the invention is to provide a system that integrates an advanced energy management unit, that manages power conversion and storage to ensure grid compliance and optimal power flow.

[0012] Another object of the invention is to provide an ability for the system to react to real-time data ensuring immediate adjustments to unforeseen conditions, such as cloud cover or unexpected generation spikes, thereby preventing penalties and maximizing revenue.

[0013] Another object of the invention is to address deviations in power output compared to forecasted schedules.

[0014] Another object of the invention is to generate an accurate output forecast based on at least one real-time data such as irradiation, ambient temperature, wind speed, and module temperature.

[0015] Another object of the invention is to obtain ambient temperature and irradiation data from weather monitoring unit for more precise forecasting.

[0016] Another object of the invention is to predict module temperature using the digital twin module based on environmental data obtained from the weather monitoring unit.

[0017] Another object of the invention is to provide output forecasts that include power generation from solar PV modules to point of interconnection (POI), enhancing grid security.

[0018] Another object of the invention is to facilitate precise power trading by regulatory agencies using accurate output forecasts.

[0019] Another object of the invention is to support grid stabilization through improved power generation forecasting and control, using energy storage units to stabilize the grid during cloud cover or unforeseen circumstances.

[0020] Another object of the invention is to enhance the accuracy of power generation forecasts, contributing to better generation, consumption, and trading processes, thereby increasing energy security.

[0021] Another object of the invention is to optimize the operation and performance of the solar power plants through advanced modelling prediction, and control of active power output using the digital twin module.

[0022] Another object of the invention is to reduce financial risks associated with deviation settlements by providing reliable power output predictions for over and under-injection, using energy management units and energy storage units.

[0023] Another object of the invention is to support regulatory compliance and operational planning through detailed and accurate power generation forecasts.

[0024] Another object of the invention is to provide forecasting to manage and mitigate frequency deviations by employing advanced forecasting algorithms, energy management unit, and energy storage unit. BRIEF DESCRIPTION OF FIGURES

[0025] This invention is illustrated in the accompanying drawings, throughout which, like reference letters indicate corresponding parts in the various figures.

[0026] The embodiments herein will be better understood from the following description with reference to the drawings, in which:

[0027] Figure 1 depicts a system for deviation settlement management for a solar power plant, in accordance with an embodiment of the present disclosure;

[0028] Figure 2 illustrates sub-components of deviation settlement server, in accordance with an embodiment of the present disclosure; and

[0029] Figure 3 illustrates a method for deviation settlement management for a solar power plant, in accordance with an embodiment of the present disclosure.STATEMENT OF INVENTION

[0030] The present invention discloses a system and method for deviation settlement management for at least one solar power plant.

[0031] The system comprises a point of interconnection (POI) operatively connected to at least one solar power plant to transmit power generated by the solar power plant to a grid.

[0032] The grid operatively connected to the point of interconnection (POI) to receive the generated power from the at least one solar power plant via the POI and facilitate transport of the generated power from the at least one solar power plant to consumers.

[0033] The at least one weather monitoring unit is communicatively coupled with the solar power plant, to collect real-time data from the solar power plant.

[0034] The at least one energy management unit is communicatively coupled with the weather monitoring unit, to optimize power generation of the solar power plant based on the real-time data.

[0035] The at least one deviation settlement server is communicatively coupled with the weather monitoring unit, the energy management unit, manage and optimize performance of the solar power plant, and ensure accurate deviation settlement management.

[0036] The at least one deviation settlement server comprises a deviation settlement processing unit to process the real-time data, align a real-time power output of the solar power plant with a scheduled power and predict forecasted power outputs for upcoming time periods and align them with market commitments.DETAILED DESCRIPTION

[0037] 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.

[0038] The present invention discloses a system and method for deviation settlement management for a solar power plant, enhancing grid stability and minimizing regulatory penalties. The system utilizes a digital twin model that simulates the solar power plant's operations based on real-time environmental data like sunlight, temperature, and wind speed to accurately forecast power generation. Further, the power forecast is sent to an on-site energy management unit, which controls power output and manages excess power using an energy storage unit. The energy management unit adjusts power supply in real-time to meet grid demands and maintain compliance with grid codes, ensuring reliable and efficient integration of solar power into the electric grid.

[0039] Figure 1 depicts a system for deviation settlement management for at least one solar power plant 102.

[0040] The system comprises at least one solar power plant 102, a point of interconnection (POI) 104, a grid 106, at least one weather monitoring unit 108, at least one energy management unit 110, at least one solar inverter 112, at least one power conversion unit 114, at least one SCADA (Supervisory Control and Data Acquisition) unit, at least one deviation settlement server 118, at least one energy storage unit 120, at least one load dispatch center 122, and at least one independent power producer 124.

[0041] In a solar photovoltaic (PV) power plant, also referred to as the solar power plant 102, solar energy is converted into electricity using solar panels, also knownas photovoltaic (PV) modules. The PV modules are arranged in series and parallel configurations to form strings and arrays, maximizing the efficiency of energy capture.

[0042] In an embodiment, the solar power plant 102 comprises solar PV modules, inverters, transformers, and associated electrical components and is configured to convert sunlight into electrical power for grid export.

[0043] The solar panels capture sunlight and convert it into direct current (DC) power.

[0044] The generated DC power is collected by combiner boxes and then fed into the inverters, which convert the DC power into alternating current (AC) power suitable for the grid 106.

[0045] In an embodiment, the point of interconnection (POI) 104 is an interface where the solar power plant 102 connects to the grid 106. The point of interconnection 104 is a location where the generated power is delivered to the grid 106. The point of interconnection 104 is critical for measuring amount of power being injected into the grid 106. The point of interconnection (POI) 104 is operatively connected to the solar power plant 102 and the grid 106.

[0046] The point of interconnection 104 determines how and where the generated power is delivered, either to the grid 106 or for on-site use.

[0047] The POI 104’s location can vary depending on scale of the solar power plant installation. For large utility-scale projects, the point of interconnection 104 may be made at high-voltage transmission lines or substations, while smaller residential or commercial systems may connect at low-voltage distribution level.

[0048] Compliance with the grid regulations, comprising voltage, frequency, and safety standards, is mandatory, with specific equipment such as transformers and metering devices often required at the POI 104.

[0049] An interconnection agreement between solar project developer and grid operator outlines technical specifications, responsibilities, and costs associated with the POI 104. This agreement ensures both parties are aware of how the solar power plant 102 connects to the grid 106 and its impact on the grid stability.

[0050] Power flow at the POI 104 is closely monitored and metered to track electricity exported to or imported from the grid 106, and protection systems at the POI 104 prevent disturbances from affecting either the solar power plant installation or the grid 106.

[0051] The POI 104 is vital for integrating solar energy into the grid 106, ensuring safety, compliance, and efficiency, while also influencing the economic and operational aspects of solar projects.

[0052] In an embodiment, a power quality meter (PQM) is located at the point of interconnection 104. The power quality meter is a critical device in electrical power systems, such as the solar power plant 102, to monitor, analyze, and report quality of the electrical power. The power quality meter measures key parameters such as voltage, current, frequency, and power factor, ensuring the power being generated, transmitted, and distributed meets established standards.

[0053] The power quality meter provides real-time monitoring and data logging, allowing for remote or local access to key electrical parameters. They can trigger alarms or alerts when thresholds are exceeded and log events like power outages or frequency deviations, aiding in troubleshooting and ensuring system reliability.

[0054] In the solar power plant 102, the power quality meter is vital for detecting issues like harmonic distortion and voltage fluctuations, helping maintain stable, high-quality power.

[0055] In an embodiment, the grid 106 may be an electrical grid 106, which is a network of interconnected transmission lines, substations, and distribution lines that facilitate transport of electricity from the solar power plant 102 to consumers.

[0056] The grid is operatively connected to the point of interconnection (POI) 104 to receive the generated power from the solar power plant 102 via the POI 104 and facilitate transport of the generated power from the solar power plant 102 to consumers.

[0057] In an embodiment, the weather monitoring unit 108 collects real-time data from the solar power plant 102. The weather monitoring unit 108 is communicatively coupled with the solar power plant 102.

[0058] The weather monitoring unit 108 is configured to collect at least one real- time data from the solar power plant 102.

[0059] The real-time data comprises environmental data and operations data. In an embodiment, the environmental data comprises irradiation, temperature, wind speed and the like. The operations data comprise voltage, current and power output. The real-time data is essential for monitoring the solar power plant’s operation.

[0060] In an embodiment, the weather monitoring unit 108 is attached to or placed in proximity to the solar power plant 102.

[0061] The weather monitoring unit 108 continuously logs and monitors the real- time data. The real-time data helps optimize operations by adjusting panel angles, scheduling maintenance, and predicting power output based on current weather conditions. Additionally, the real time data is used in predictive maintenance and is integrated into forecasting models to enhance power generation predictions, supporting better grid management.

[0062] The weather monitoring unit 108 comprises at least one of irradiation sensor, ambient temperature sensor, wind speed sensor, module temperature sensor, voltage sensor and current sensor, weather sensor and the like.

[0063] In an embodiment, the irradiation sensor may be used to measure amount of solar radiation hitting the at least one solar panel. The irradiation sensor data provides prediction of how much electricity the solar panels will generate.

[0064] In an embodiment, the ambient temperature sensor may be used to measure temperature of air around the solar power plant 102.

[0065] In an embodiment, the wind speed sensor may be used to measure speed of wind at the solar power plant 102 and weather data such as atmospheric pressure, rainfall, and cloud cover.

[0066] In an embodiment, the module temperature sensor may be used to measure the temperature of the solar power plant 102.

[0067] In an embodiment, the voltage and current sensor may be used to measure electrical output from the solar panels, strings, and inverters of the solar power plant 102.

[0068] In an embodiment, the weather sensor may be used to measure weather surrounding the solar power plant 102 like atmospheric pressure, rainfall, and cloud cover.

[0069] In an embodiment, the energy management unit 110 is responsible for controlling and optimizing the power generation of the solar power plant 102. The energy management unit 110 uses real-time data from the weather monitoring unit 108 to adjust the operation of the solar power plant 102 in real-time, ensuring that the power output matches the scheduled forecast.

[0070] In an embodiment, the energy management unit 110 comprises at least one algorithm for load balancing, frequency regulation, and voltage control.

[0071] The at least one algorithms comprises at least one of load balancing algorithm, frequency regulation algorithm, and voltage control algorithm.

[0072] The load balancing algorithm comprises proportional load sharing control, model predictive control (MPC), and linear or quadratic programming.

[0073] The proportional load sharing control distributes power output proportionally among the inverter or the battery storage unit based on capacity.

[0074] The model predictive control (MPC) forecasts short-term demand and adjusts dispatch to minimize imbalance between scheduled and actual generation.

[0075] The linear or quadratic programming optimizes allocation of active power across available resources subject to DSM constraints.

[0076] The frequency regulation algorithm comprises droop control, automatic generation control (AGC), and adaptive gain scheduling.

[0077] The droop control adjusts active power output in proportion to frequency deviation, supporting primary frequency response.

[0078] The automatic generation control (AGC) maintains grid frequency within limits by fine adjustment of the inverter or the battery storage unit output.

[0079] The adaptive gain scheduling dynamically tunes frequency response parameters based on real-time system conditions.

[0080] The voltage control algorithms comprise volt-var control, volt-watt control and decoupled d-q vector control.

[0081] The volt-var control regulates reactive power injection / absorption to maintain voltage at the Point of Interconnection (POI).

[0082] The volt-watt control reduces active power injection during over-voltage conditions to stabilize the grid.

[0083] The decoupled d-q vector control manages active (P) and reactive (Q) flows independently for precise voltage and power factor regulation.

[0084] The energy management unit 110 may be an energy management system (EMS) located at the solar power plant 102.

[0085] The forecasted power is communicated to the energy management unit 110, equipped with functionalities such as active power, reactive power, power factor, frequency, and voltage controls.

[0086] In an embodiment, the energy management unit 110 also manages ramp up and ramp down, power curve smoothening, voltage clamping, artificial inertia, power oscillation damper, and spinning reserve features, all mandated may be by the grid code.

[0087] The solar inverter 112 converts the DC electricity generated by the solar power plant 102 into alternating current (AC) electricity, which is compatible with the grid 106. The solar inverter 112 also optimizes the power output by adjusting parameters such as voltage and current.

[0088] In an embodiment, the solar inverter 112 is attached to the solar power plant 102. The solar inverter 112 and the power conversion unit 114 are communicatively coupled with the energy management unit 110.

[0089] In an embodiment, the solar inverter 112 is configured to convert the direct current (DC) electricity generated by the solar power plant 102 into alternating current (AC), which is suitable for feeding into the electrical grid 106. Additionally, the solar inverter 112 is configured to optimize power output through maximum power point tracking (MPPT) and to manage the reactive power to support grid voltage stability.

[0090] In an embodiment, the power conversion unit 114 in the solar power plant 102 is responsible for converting the direct current (DC) generated by solar panels into alternating current (AC). This conversion is critical for integrating solar energyinto the electrical grid 106 and ensuring efficient on-site consumption. The power conversion unit 114 ensures that solar power can be safely and efficiently utilized.

[0091] The power conversion unit 114 regulates the flow of electricity from the solar power plant 102 to the grid 106. The power conversion unit 114 ensures that the power being exported meets the grid requirements, such as maintaining proper voltage and frequency levels. The power conversion unit 114 can also adjust the power output to prevent overloading the grid 106.

[0092] In an embodiment, the SCADA (Supervisory Control and Data Acquisition) unit is used to monitor, control, and analyze operational performance of the solar power plant 102. The SCADA unit 116 is communicatively coupled with the energy management unit 110, the solar inverter 112, and the at least one power conversion unit 114. The SCADA unit 116 provides a user interface for operators to oversee the solar power plant’s performance, view real-time data, and make necessary adjustments.

[0093] The SCADA unit 116 provides real-time monitoring, control, and data analysis, helping operators optimize the performance and reliability of the solar power plant installations. The SCADA unit 116 monitors the real time data and power output across the solar power plant 102 and allows remote control of the solar inverter 112.

[0094] The SCADA unit 116 also supports predictive maintenance using at least one prediction algorithm by analyzing real time data trends, triggering alarms for abnormal conditions, and generating reports for regulatory compliance and operational efficiency.

[0095] The at least one prediction algorithm comprises time-series forecasting, statistical process control (SPC), anomaly detection, deep learning, transformer- based forecaster, remaining useful life (RUL), ensemble regression, change point detection, and feature extraction and health indices.

[0096] The time-series forecasting comprises ARIMA (AutoRegressive Integrated Moving Average) and SARIMAX (Seasonal ARIMA). The time-series forecasting comprises Kalman or Extended or Unscented Kalman filters for drift and bias tracking.

[0097] The SPC comprises Shewart, chart, CUSUM (Cumulative Sum) Chart, and Exponentially Weighted Moving Average (EWMA). The SPC detects emerging faults and trigger alarms.

[0098] The anomaly detection comprises Isolation Forest, One-Class SVM, Local Outlier Factor (LOF), robust z-scores (MAD / IQR), and PCA-based residual, wherein the deep learning comprises LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), autoencoder and Temporal Convolutional Networks (TCN). The anomaly detection monitors sensor and equipment anomalies.

[0099] The transformer-based forecaster comprises Temporal Fusion Transformers (TFT). The transformer-based forecaster is used for joint forecasting of loads, temperatures, and vibration / EMI signatures where available.

[0100] The RUL comprises Weibull / cox models, random survival forests, regression on cumulative stressors. The RUL on the cumulative stressors estimates time-to-maintenance.

[0101] The ensemble regression comprises gradient boosting (XGBoost / LightGBM / CatBoost) and random forests. The ensemble regression predicts fault probability, failure severity, or maintenance priority codes from engineered features. The Bayesian online change-point detection is used for early regime shifts in inverter efficiency.

[0102] The change point detection comprises Page–Hinkley, Bayesian online change-point detection for early regime shifts in inverter efficiency, fan speed profiles, or PQM indices.

[0103] The feature extraction and health indices are used for forecast and monitor. The comprise spectral features (FFT), THD / THC, crest factors, kurtosis.

[0104] In an embodiment, the SCADA unit 116 receives the real-time data from the energy management unit 110, the weather monitoring unit 108, and the solar inverter 112 to maintain the optimal solar power plant operation.

[0105] In an embodiment, the communication network (not shown) may be configured to enable the at least one real-time data exchange between the solar power plant 102, the weather monitoring unit 108, the at least one deviationsettlement server 118, the at least energy storage unit 120, and other external entities like the grid operators.

[0106] In an embodiment, the communication network 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.

[0107] In an embodiment, the communication unit 204 comprises high- speed network interface and secure communication protocols.

[0108] In an embodiment, the communication unit 204 facilitates seamless and secure communication between the at least one deviation settlement server 118 and its subcomponents.

[0109] In an embodiment, the communication unit 204 is configured to facilitate real-time data transfer and ensure accurate and timely communication between system components.

[0110] In an embodiment, the deviation settlement processing unit 206 comprises one or more microprocessors, circuits, and other hardware configured for processing.

[0111] In an embodiment, the deviation settlement processing unit 206 is configured to provide deviation settlement management by aligning the real-time power output of the solar power plant 102 with the scheduled power.

[0112] In an embodiment, the deviation settlement processing unit 206 predicts future power outputs for upcoming time periods such as next day or week or month or year, generating the forecasted power that specifies grid export amounts at various intervals.

[0113] The deviation settlement processing unit 206 is configured to execute instructions stored in the memory unit 202 as well as communicate via the communication unit 204.

[0114] In an embodiment, the deviation settlement processing unit 206 comprises an intelligent bidding unit 208, a digital twin module 210, the deviation detection module 212, and the deviation correction module 214.

[0115] In an embodiment, the intelligent bidding unit 208 may be used to optimize energy bids for the solar power plant 102. The intelligent bidding unit 208 processes historical data on energy prices, demand, and weather conditions to forecast future trends. Further, the intelligent bidding unit 208 tracks real-time market prices and competitor bids to ensure timely and competitive offers.

[0116] In an embodiment, the intelligent bidding unit 208 utilizes machine learning and advanced algorithms to calculate optimal ban embodiment, the at least one deviation settlement server 118 can be a physical server located at the solar power plant 102 or can be a cloud server.

[0117] In an embodiment, the at least one deviation settlement server 118 is configured to process the at least one real-time data received from the weather monitoring unit 108 via the communication network (not shown).

[0118] In an embodiment, the deviation settlement server 118 manages and optimizes performance of the solar power plant 102. The deviation settlement server 118 ensures accurate deviation settlement management, which involves aligning a real-time power output of the solar power plant 102 with a scheduled power.

[0119] In an embodiment, the real-time power may be referred to as real- time electrical power output generated by the solar power plant 102 at a specific time period. Further, it represents the amount of electricity that is actively being produced and injected into the grid 106 or used on-site. The real-time power can fluctuate based on various factors such as weather conditions affecting solar irradiance, equipment performance, grid demand, and operational adjustments made by the solar power plant 102.

[0120] In an embodiment, the specific time period may be a day, a week, a month or a year.

[0121] In an embodiment, the scheduled power may be referred to as power according to agreements with energy management organizations or operators suchas government or private electricity boards and energy purchasers. The scheduled power levels are important for grid 106 stability and operational planning, as they help the grid operators to manage electricity supply and demand effectively.

[0122] In an embodiment, the forecasted power may be referred as planned or forecasted amount of electrical power that the solar power plant 102 at the specific time period or generating facility is expected to produce and supply to the grid 106 over a given period. The forecasted power may be determined based on operational schedules and forecasts.

[0123] In an embodiment, the energy storage unit 120 is present in proximity to the solar power plant 102.

[0124] In an embodiment, the energy storage unit 120 comprises at least one of battery packs, a battery management system (BMS), inverters / chargers, and, cooling and safety systems.

[0125] The energy storage unit 120 is communicatively coupled with the energy management unit 110 and the deviation settlement server 118.

[0126] In an embodiment, the energy storage unit 120 is configured to store excess electrical power generated by the solar power plant 102 upon receiving an excess energy instruction from the deviation settlement server 118. The excess energy instruction may comprise the amount of additional excess power to be stored by the energy storage unit 120, which is determined by comparing the scheduled power to the generated power.

[0127] Further, the energy storage unit 120 is configured to supply stored power to the grid 106 during cloud cover times or during frequency excursion events caused to maintain grid code requirements, upon receiving an energy shortage instruction from the deviation settlement server 118. The energy shortage instruction may comprise the amount of additional power to be provided by the energy storage unit 120, which is determined by comparing the scheduled power to the generated power.

[0128] The energy storage unit 120 by storing excess electrical power and releasing it when needed, helps in smoothing out fluctuations in the solar power output, improving the grid stability, and reducing need for conventional backuppower sources. This capability is crucial in modern grid management strategies, as it supports renewable energy integration by mitigating intermittency and enhancing overall reliability of solar power generation systems.

[0129] The energy storage unit 120 plays a crucial role in storing excess power generated beyond predetermined commitments in the battery energy storage system (BESS) of the energy storage unit 120. This stored excess power can be discharged as needed, such as during cloud cover or equipment breakdowns affecting solar power generation.

[0130] In an exemplary embodiment, for the solar power plant 102 with a capacity of approximately 320 MW, the BESS may ideally be sized to store energy equivalent to only about 3 to 7% of the total rated capacity of the solar power plant 102. This proportion ensures that the BESS can effectively manage fluctuations in energy production without overburdening the solar power plant 102 or the grid 106. By limiting the BESS capacity to this range, producers can optimize their operational costs and maintain grid stability. Additionally, this approach benefits both energy generators and consumers by enhancing reliability in energy supply, minimizing potential disruptions, and supporting a sustainable and efficient energy distribution system. Thus, correctly sizing the BESS container contributes to a balanced and beneficial energy management strategy for the solar power plants.

[0131] The energy storage unit 120 stores excess power generated by the solar power plant 102. The energy storage unit 120 can release stored energy back into the grid 106 during periods of low generation or high demand, helping to maintain a stable power supply and reduce penalties due to deviations from scheduled power output.

[0132] In an embodiment, the load dispatch center (LDC) 122 manages the grids real-time power demand and generation, ensuring that electricity supply matches demand and that the grid 106 remains stable.

[0133] In an embodiment, the load dispatch center 122 works closely with the energy management unit 110, receiving power from the solar inverter 112 via the power conversion unit 114 and supplying stored energy back to the grid 106 when needed.

[0134] In an embodiment, the load dispatch center 112 plays a crucial role in managing the power grid 106 at national, regional, and state levels.

[0135] The load dispatch center 122 is communicatively coupled with the grid 106 and the energy management unit 110. The load dispatch center 122 to manage the grids real-time power demand and generation, and to balance real-time supply and demand of power and coordinate scheduling requirements.

[0136] In an embodiment, the load dispatch center 122 is responsible for real-time monitoring of electricity supply, ensuring demand is balanced across the solar power plant 102 and transmission lines, and managing load shedding when necessary. The load dispatch center 122 coordinate grid operations, control frequency and stability, and handles energy scheduling and dispatch, ensuring optimal use of available power resources.

[0137] In an embodiment, the load dispatch center 122 also manages emergency situations, such as grid disturbances, to maintain stability and prevent outages. Their work is vital for integrating renewable energy into the grid 106 and ensuring the efficient delivery of electricity to consumers.

[0138] In an embodiment, the load dispatch center 122 is responsible for managing the distribution of electricity on the grid 106. The load dispatch center 122 coordinates with various power producers, comprising the solar power plant 102, to balance supply and demand, ensuring efficient grid 106 operation.

[0139] In an embodiment, the independent power producer 124 is communicatively coupled with the load dispatch center 122. The independent power producer 124 (IPP) is an entity that owns and operates the solar power plant 102. They are responsible for generating electricity and selling it to the grid 106 or other consumers. The independent power producer 124 coordinates with the load dispatch center 122 and other entities to ensure compliance with the grid requirements and optimize revenue.

[0140] In an embodiment, the independent power producer 124 in the solar sector are private entities that generate electricity from solar power plant 102 and sell it to utilities or consumers, contributing significantly to the growth of renewable energy. The independent power producer 124 manages entire lifecycle of the solarpower plant 102, from development to operation, and typically secure long-term Power Purchase Agreements (PPAs) to ensure stable revenue streams.

[0141] In an embodiment, the independent power producer 124 oversees the solar power plant 102 and coordinates with the load dispatch center 122 to ensure energy sales align with market conditions and regulatory frameworks.

[0142] Figure 2 illustrates sub-components of the deviation settlement server 118.

[0143] In an embodiment, the deviation settlement server 118 manages and optimizes performance of the solar power plant 102. The deviation settlement server 118 aligns the real-time power output of the solar power plant 102 with the scheduled power, thus ensuring accurate deviation settlement management.

[0144] Additionally, the deviation settlement server 118 provides compliance and penalty avoidance, thereby helping the solar power plant 102 adhere to regulatory requirements and contractual obligations regarding power delivery schedules. By minimizing deviations from these schedules, the deviation settlement server 118 reduces risk of penalties imposed by the grid operators for non-compliance.

[0145] In an embodiment, the at least one deviation settlement server 118 comprises a memory unit 202, a communication unit 204 and a deviation settlement processing unit 206.

[0146] In an embodiment, the memory unit 202 comprises one or more volatile and non-volatile memory components. The memory unit 202 may be solid- state drives (SSDs) or cloud-based storage which are capable of storing the at least one real-time data and instructions to be executed.

[0147] In an embodiment, the memory unit 202 is configured to store real- time and historical data.

[0148] In an embodiment, bidding strategies, adjusting prices and quantities dynamically. The machine learning and the advanced algorithms comprises time- series forecasting, ensemble and boosting, neural network architecture, regression, optimization framework, reinforcement learning (RL), and adaptive and online learning.

[0149] The time-series forecasting comprises ARIMA (AutoRegressive Integrated Moving Average), SARIMAX (Seasonal ARIMA with eXogenous variables), State-Space Models and Kalman Filtering and Vector Autoregression (VAR).

[0150] The ensemble regression for feature-driven prediction of market prices and plant output. The ensemble and boosting comprise Random Forest, XGBoost, LightGBM, and CatBoost.

[0151] The neural network architecture for multi-horizon forecasting of demand, price, and weather-linked generation. The neural network architecture comprises Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer-based architecture comprising Temporal Fusion Transformers (TFT).

[0152] The regression comprises linear regression, non-linear regression, and quantile regression. The regression is used for uncertainty-aware bid formulation.

[0153] The optimization frameworks comprise linear programming (LP), quadratic programming (QP), mixed-integer programming (MILP), and stochastic optimization. The optimization frameworks for scenario-based bid curve generation.

[0154] The reinforcement learning comprises Deep Q-Networks (DQN), Policy Gradient, Proximal Policy Optimization (PPO), and Actor–Critic variants. The reinforcement learning provides reward balances revenue, DSM penalties, and the battery storage unit degradation.

[0155] The adaptive and online learning comprises Bayesian updating and contextual bandits. The adaptive and online learning continuously adapts bid prices and quantities based on market response.

[0156] The intelligent bidding unit 208 may be communicatively coupled with the SCADA unit 116, receiving real-time operational data from the solar power plant 102 to ensure bidding reflects actual production capabilities. The real-time operational data comprises operational data of the solar power plant 102 in real- time. Additionally, the intelligent bidding unit 208 interfaces with the energymanagement unit 110 to align with energy storage and dispatch strategies, ensuring efficient energy utilization and maximizing revenue potential.

[0157] In an embodiment, the intelligent bidding unit 208 automates the submission of bids to ensure the solar power plant 102 maximizes revenue while maintaining the grid 106 stability.

[0158] In an embodiment, the digital twin module 210 comprises a virtual representation of the solar power plant’s physical layout, comprising solar PV modules, inverters, transformers, and associated electrical components. The digital twin module 210 integrates the at least one operational data and historical data to simulate power generation scenarios accurately.

[0159] In an embodiment, the digital twin module 210 is configured to predict real-time power output based on the historical data and the real-time operational data. The digital twin module 210 creates a digital twin of the solar power plant 102 and uses at least one algorithm to forecast energy production over different time horizons considering losses from cable resistance, transformer inefficiencies, and module degradation over time, aiding in proactive decision- making and optimization of plant operations.

[0160] In an embodiment, at least one algorithm may be at least one of artificial intelligence or machine learning algorithms. In an embodiment, the at least one of artificial intelligence or machine learning algorithms may be at least one of grid search, random forest, linear regression and non-linear based regression analysis.

[0161] In an embodiment, the deviation detection module 212 comprises grid settlement rules to compare real-time power output from the solar power plant 102 with the scheduled power output levels. The deviation detection module 212 analyzes the at least one real-time data to detect the deviations in real-time.

[0162] The deviation detection module 212 operates at the Point of Interconnection (POI) to compare real-time active power (MW) of the solar power plant 102 with the scheduled power (MW) for the corresponding dispatch block.

[0163] The deviation detection module 212 operates at the Point of Interconnection (POI) and aligns real-time active power measurements (MW) withthe grid-defined dispatch block Δt (e.g., 15-minute or 5-minute interval), the schedule for block k being S^ [MW], and the time-averaged POI active power for the same block being A^, computed after applying meter sanity checks, clock-skew tolerance, and debounce windows.

[0164] The deviation detection module 212 calculates one or more jurisdiction-specific deviation measures conformant with the prevailing Deviation Settlement Mechanism (DSM) or imbalance settlement framework, comprising: Power deviation D^ = A^ − S^ (sign as per grid convention: positive for over- injection, negative for under-injection) Energy deviation E^ = D^ × Δt (used where settlement is energy-based per block) Absolute percentage error ε^ = |A^ − S^| / den^ × 100, wherein den^ is selected based on the applicable grid code (for example, max(S^, ε_min) or other near-zero schedule denominators) Permissible band check, wherein D^ or ε^ is compared against an allowable deviation band β (static or dynamic) prescribed by the jurisdiction. The deviation detection module 212 raises a deviation event only upon fulfillment of a code-specific condition. such as: D^ exceeding MW_band or ε^ exceeding β for the relevant block outside any grace or gate-closure window or an estimated charge or penalty, computed using the jurisdiction’s charge function, surpassing a configured threshold, thereby ensuring that deviation detection is aligned with the settlement computation principles rather than raw numerical differences.

[0165] The deviation detection module 212 enhances robustness without altering DSM equations through schedule-aligned filtering of instantaneous error e(t) = A(t) − S^ via EWMA or low-pass filtering to suppress meter noise, deterministic dead-bands and ramp sanity checks against S^ to prevent false deviation flags near block boundaries, application of statistical process control (SPC) techniques such as Shewhart, EWMA, or CUSUM on D^ across consecutive blocks to detect persistent drift and an optional forecast interval guard to prevent transient spikes within predicted confidence intervals from triggering deviation events unless DSM limits are breached.

[0166] The deviation detection module 212 module operates in a parameterized mode to adapt to regional DSM or imbalance settlement frameworks, comprising at least one of computing D^, E^, and ε^ as per DSM regulations for renewable generators with configurable parameters such as block resolution, sign convention, permissible error band β, treatment of near-zero schedules, and frequency-linked or slabbed charge curves or adopting applicable imbalance definitions (e.g., Metered vs. Final Position), sign conventions, imbalance price mappings (single price, dual price, or capped CVaR), and renewable-specific tolerances, with deviation detection raised only under chargeable deviation conditions defined by the respective grid authority.

[0167] The algorithmic configuration is non-limiting and adaptable to future DSM or imbalance mechanisms comprising revised block durations (e.g., 5- minute intervals), modified permissible bands, or updated settlement formulas, provided the module continues to compare POI active power against the scheduled active power and determine deviations in accordance with the relevant jurisdiction’s official computation methods.

[0168] In an embodiment, the deviation detection module 212 is configured to monitor the real-time power injected into an electrical grid 106 during a predetermined time period and detect deviations between the real-time power injected and the scheduled power.

[0169] In an embodiment, the deviation correction module 214 comprises the at least one algorithm to adjust the power output in response to the detected deviations.

[0170] The at least one algorithm comprises proportional-integral- derivative (PID) control algorithms, droop control algorithms, model predictive control (MPC) algorithms, linear and quadratic programming (LP / QP) algorithms, stochastic optimization algorithms, rule-based correction algorithms, reinforcement learning algorithms and adaptive gain scheduling algorithms. The reinforcement learning algorithms comprises Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), or Actor–Critic methods.

[0171] The proportional-integral-derivative (PID) control algorithms continuously minimizes the error between scheduled and actual power output by adjusting the inverter and the battery storage unit set-points in real time.

[0172] The droop control algorithms proportionally change active power output based on frequency or schedule deviations, supporting grid stability and primary control.

[0173] The model predictive control (MPC) algorithms forecast deviation trends over the block horizon and generate optimal corrective actions for the inverter and the battery storage unit under DSM band and SOC (State of Charge) constraints.

[0174] The linear and quadratic programming (LP / QP) algorithms optimize active power dispatch between the solar power plant and the battery storage unit with respect to DSM penalty cost functions and operational limits.

[0175] The stochastic optimization algorithms calculate corrections under uncertainty of irradiance, demand, or DSM penalty curves, ensuring compliance across possible scenarios.

[0176] The rule-based correction algorithms apply deterministic actions such as curtailing active power during over-injection or discharging the battery storage unit during under-injection, directly aligned to DSM formulas.

[0177] The reinforcement learning algorithms to learn optimal correction policies that minimize penalties and degradation costs over repeated DSM settlement cycles.

[0178] The adaptive gain scheduling algorithms automatically tune correction parameters based on variability, SOC headroom, and prevailing grid conditions.

[0179] The reinforcement learning algorithms comprises Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), or Actor–Critic methods.

[0180] The deviation correction module 214 coordinates with the energy management unit 110 to optimize power generation and ensure compliance with operational commitments.

[0181] In an embodiment, the deviation correction module 214 is configured to manage the detected deviations.

[0182] In an embodiment, the deviation correction module 214 manages the detected deviations by limiting the real-time power injected into the grid 106 when excess power is generated compared to the scheduled power.

[0183] Further, the deviation correction module 214 communicates an excess energy instruction to the energy storage unit 120 to store additional power, based on comparison of the scheduled power and real-time power generated during time periods of the excess power generation.

[0184] Additionally, the deviation correction module 214 provides additional power from the energy storage unit 120 to the electric grid 106 when lesser power is generated compared to the scheduled power, by communicating an energy shortage instruction to the energy storage unit 120. Thereby the deviation correction module 214 maintains the electrical grid stability during frequency excursion event, in compliance with grid code requirements.

[0185] Figure 3 illustrates a method 300 for deviation settlement management for at least one solar power plant 102.

[0186] The method 300 begins with transmitting power generated by the solar power plant 102 to the grid 106 by a point of interconnection POI 104 operatively connected to at least one solar power plant 102, as depicted at step 302. Subsequently, the method 300 discloses receiving the generated power by the grid 106 operatively connected to the point of interconnection POI 104 from the at least one solar power plant 102 via the POI 104 and facilitating transport of the generated power from the at least one solar power plant 102 to consumers, as depicted at step 304. Additionally, the method 300 discloses collecting real-time data by at least one weather monitoring unit 108 communicatively coupled with the solar power plant 102 from the solar power plant 102, as depicted at step 306. Subsequently, the method 300 discloses optimizing power generation of the solar power plant 102 by at least one energy management unit 110 communicatively coupled with the weather monitoring unit 108 based on the real-time data, as depicted at step 308. Additionally, the method 300 discloses managing and optimizing performance ofthe solar power plant 102 by at least one deviation settlement server 118 communicatively coupled with the weather monitoring unit 108 and the energy management unit 110, and ensuring accurate deviation settlement management, as depicted at step 310. Thereafter, the method 300 discloses processing the real-time data by a deviation settlement processing unit 206, aligning a real-time power output of the solar power plant 102 with a scheduled power and predict forecasted power outputs for upcoming time periods and aligning them with market commitments, as depicted at step 312.

[0187] The advantages of the current invention include its ability to significantly enhance grid stability through precise forecasting of power generation from renewable energy sources.

[0188] An additional advantage is that the claimed method reduces penalty payments to regulatory bodies by minimizing deviations in power output compared to forecasted schedules.

[0189] An additional advantage is that the digital twin module accurately replicates the performance of individual solar power plants, ensuring reliable predictions of power generation.

[0190] An additional advantage is that the system optimizes power flow calculations from solar PV modules to the point of interconnection, considering all relevant factors such as cable losses and transformer efficiencies.

[0191] An additional advantage is that it improves the efficiency of power trading activities by providing regulatory agencies with precise forecasts of solar power plant outputs.

[0192] An additional advantage is that it enhances the overall reliability and predictability of renewable energy integration into the grid.

[0193] An additional advantage is that the system supports sustainable energy practices by minimizing wastage and maximizing the utilization of renewable energy resources.

[0194] An additional advantage is that it facilitates better resource planning and allocation for renewable energy projects based on accurate forecasting.

[0195] Applications of the current invention include power plant sites such as solar power plant, wind farms, hydroelectric facilities, thermal power stations, biomass power plants, nuclear power plants, geothermal power plants for renewable energy generation, electricity production, energy independence, industrial power supply, remote area electrification, grid stability support, sustainable energy development, peak load management, microgrid integration.

[0196] Additional applications include grids like electricity grid or power grid for transmission lines, substations, distribution networks, smart grid technologies, grid control centers, energy storage facilities, interconnectors, demand response systems for electricity transmission, distribution of power, grid interconnection, load balancing, voltage regulation, frequency control, black start capability, energy market integration, resilience against power outages, integration of renewable energy sources.

[0197] 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

CLAIMS We claim:

1. A system (100) for deviation settlement management for at least one solar power plant (102), the system comprising: a point of interconnection (POI) (104) operatively connected to at least one solar power plant (102), to transmit power generated by the solar power plant (102) to a grid (106); the grid (106) operatively connected to the point of interconnection (POI) (104) to receive the generated power from the at least one solar power plant (102) via the POI (104) and facilitate transport of the generated power from the at least one solar power plant (102) to consumers; at least one weather monitoring unit (108) communicatively coupled with the solar power plant (102), to collect real-time data from the solar power plant (102); at least one energy management unit (110) communicatively coupled with the weather monitoring unit (108), to optimize power generation of the solar power plant (102) based on the real-time data; and at least one deviation settlement server (118) communicatively coupled with the weather monitoring unit (108) and the energy management unit (110), to manage and optimize performance of the solar power plant (102) and ensure accurate deviation settlement management, wherein the deviation settlement server (118) comprises: a deviation settlement processing unit (206) to process the real-time data, align a real-time power output of the solar power plant (102) with a scheduled power and predict forecasted power outputs for upcoming time periods and align them with market commitments.

2. The system (100) as claimed in claim 1, wherein the real-time data comprises real-time environmental and operational data, wherein the environmentaldata comprises irradiation, temperature, and wind speed, and wherein the operational data comprises voltage, current and power output; wherein the at least one weather monitoring unit (108) comprises at least one of an irradiation sensor, an ambient temperature sensor, a wind speed sensor, a module temperature sensor, a voltage sensor, a current sensor, or a weather sensor; and wherein the energy management unit (110) comprises at least one algorithm for the power generation optimization, wherein the power optimization comprises load balancing, frequency regulation, and voltage control, wherein the at least one algorithms comprises at least one of load balancing algorithm, frequency regulation algorithm, and voltage control algorithm, wherein the load balancing algorithm comprises proportional load sharing control, model predictive control (MPC), and linear or quadratic programming, wherein the frequency regulation algorithm comprises droop control, automatic generation control (AGC), and adaptive gain scheduling, and wherein the voltage control algorithms comprise volt-var control, volt-watt control and decoupled d-q vector control.

3. The system (100) as claimed in claim 1, wherein the system comprises: at least one solar inverter (112) and at least one power conversion unit (114) communicatively coupled with the at least one energy management unit (110), to convert direct current (DC) generated by the at least one solar power plant (102) into alternating current (AC) for supply to the grid (106); at least one SCADA unit (116) communicatively coupled with the energy management unit (110), the at least one solar inverter (112), and the at least one power conversion unit (114), to monitor, control, and analyze operational performance of the solar power plant (102); at least one energy storage unit (120) communicatively coupled with the energy management unit (110) and the at least one deviation settlement server (118), to store excess electrical power generated by the solar power plant (102) and release stored power to the grid (106) during power shortage conditions;at least one load dispatch center (122) communicatively coupled with the grid (106) and the at least one energy management unit (110), to manage the grids real-time power demand and generation, and to balance real-time supply and demand of power and coordinate scheduling requirements; and at least one independent power producer (124) communicatively coupled with the load dispatch center (122) to oversee the solar power plant (102) and coordinate with the load dispatch center (122) to ensure energy sales align with market conditions and regulatory frameworks.

4. The system (100) as claimed in claim 1, wherein the deviation settlement processing unit (206) predicts future power outputs for upcoming time periods comprising next day, week, month, and year, generating the forecasted power that specifies grid export amounts at various intervals, the deviation settlement processing unit (206) comprising an intelligent bidding unit (208) to process historical data and real-time operational data received from the solar power plant (102) using machine learning and advanced algorithms to calculate optimal bidding strategies, adjusting prices and quantities dynamically, wherein the historical data comprises energy prices, demand, and weather conditions to forecast future trends and wherein the real-time operational data comprises operational data of the solar power plant (102) in real- time, wherein the machine learning and the advanced algorithms comprises time- series forecasting, ensemble and boosting, neural network architecture, regression, optimization framework, reinforcement learning (RL), and an adaptive and online learning, wherein the time-series forecasting comprises ARIMA (AutoRegressive Integrated Moving Average), SARIMAX (Seasonal ARIMA with eXogenous variables), State-Space Models and Kalman Filtering and Vector Autoregression (VAR), wherein the ensemble and boosting comprises Random Forest, XGBoost, LightGBM, and CatBoost, wherein the neural network architecture comprises Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer- based architectures comprising Temporal Fusion Transformers (TFT), the regression comprises linear regression, non-linear regression, and quantileregression, the optimization frameworks comprise linear programming (LP), quadratic programming (QP), mixed-integer programming (MILP), and stochastic optimization, the reinforcement learning comprises Deep Q-Networks (DQN), Policy Gradient, Proximal Policy Optimization (PPO), and Actor–Critic variants, and wherein the adaptive and online learning comprises Bayesian updating and contextual bandits; a digital twin module (210) to receive the real-time operational data and historical data, generate a virtual representation of the solar power plant (102) for predicting real-time power output and use at least one algorithm to forecast energy production over different time horizons, wherein the at least one algorithm comprises grid search, random forest, linear regression and non-linear based regression analysis; a deviation detection module (212) to compare the real-time power output from the solar power plant (102) using the grid settlement rules with the scheduled power and detect deviations in real-time; and a deviation correction module (214) to adjust the real-time power output in response to the detected deviations using at least one algorithm, coordinate with the energy management unit (110) to optimize power generation, and by instructing the energy storage unit (120) to store additional power, based on comparison of the scheduled power and the real-time power output generated during time periods of the excess power generation, wherein the at least one algorithm comprises proportional- integral-derivative (PID) control algorithms, droop control algorithms, model predictive control (MPC) algorithms, linear and quadratic programming (LP / QP) algorithms, stochastic optimization algorithms, rule-based correction algorithms, reinforcement learning algorithms and adaptive gain scheduling algorithms, and wherein the reinforcement learning algorithms comprises Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), or Actor–Critic methods.

5. The system (100) as claimed in claim 3, wherein the at least one energy storage unit (120) comprises a battery energy storage system (BESS) having a capacity sized between 3% and 7% of a total rated capacity of the solar power plant (102),wherein the SCADA unit (116) performs predictive maintenance using at least one prediction algorithm by analyzing real-time data trends, triggering alarms, and generating reports for regulatory compliance, wherein the at least one prediction algorithm comprises time-series forecasting, statistical process control (SPC), anomaly detection, deep learning, transformer-based forecaster, remaining useful life (RUL), ensemble regression, change point detection, and feature extraction and health indices, wherein the time-series forecasting comprises ARIMA (AutoRegressive Integrated Moving Average) and SARIMAX (Seasonal ARIMA), wherein the SPC comprises Shewart, chart, CUSUM (Cumulative Sum) Chart, and Exponentially Weighted Moving Average (EWMA), wherein the anomaly detection comprises Isolation Forest, One-Class SVM, Local Outlier Factor (LOF), robust z-scores (MAD / IQR), and PCA-based residual, wherein the deep learning comprises LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), autoencoder and Temporal Convolutional Networks (TCN), wherein the transformer-based forecaster comprises Temporal Fusion Transformers (TFT), wherein the RUL comprises Weibull / cox models, random survival forests, regression on cumulative stressors, wherein the ensemble regression comprises gradient boosting (XGBoost / LightGBM / CatBoost) and random forests, wherein the change point detection comprises Page–Hinkley, Bayesian online change-point detection for early regime shifts in inverter efficiency, fan speed profiles, or PQM indices, and wherein the feature extraction and health indices comprises spectral features (FFT), THD / THC, crest factors, kurtosis, and wherein the independent power producer (124) manages operation of the solar power plant (102) and coordinates with the load dispatch center (122) for power scheduling and compliance.

6. The system (100) as claimed in claim 4, comprising a power quality meter (PQM) located at the point of interconnection (104) to monitor, analyze, and report quality of the generated power from the at least one solar power plant (102),wherein the deviation correction module (214) manages the detected deviations by limiting the real-time power injected into the grid (106) when excess power is generated compared to the scheduled power, and wherein the deviation correction module (214) communicates an excess energy instruction to the energy storage unit (120) to store additional power, based on comparison of the scheduled power and real-time power generated during time periods of the excess power generation.

7. A method (300) for deviation settlement management for at least one solar power plant (102), the method comprising: transmitting, by a point of interconnection (POI) (104) operatively connected to at least one solar power plant (102), power generated by the solar power plant (102) to the grid (106); receiving, by the grid (106) operatively connected to the point of interconnection (POI) (104), the generated power from the at least one solar power plant (102) via the POI (104) and facilitating transport of the generated power from the at least one solar power plant (102) to consumers; collecting, by at least one weather monitoring unit (108) communicatively coupled with the solar power plant (102), real-time data from the solar power plant (102); optimizing, by at least one energy management unit (110) communicatively coupled with the weather monitoring unit (108), power generation of the solar power plant (102) based on the real-time data; managing and optimizing, by at least one deviation settlement server (118) communicatively coupled with the weather monitoring unit (108) and the energy management unit (110), performance of the solar power plant (102), and ensuring accurate deviation settlement management; and processing, by a deviation settlement processing unit (206), the real-time data, aligning a real-time power output of the solar power plant (102) with a scheduled power and predict forecasted power outputs for upcoming time periods and aligning them with market commitments.

8. The method (300) as claimed in claim 7, comprising providing the real-time data comprises real-time environmental and operational data, wherein the environmental data comprises irradiation, temperature, and wind speed, and wherein the operational data comprises voltage, current and power output; wherein the at least one weather monitoring unit (108) comprises at least one of an irradiation sensor, an ambient temperature sensor, a wind speed sensor, a module temperature sensor, a voltage sensor, a current sensor, or a weather sensor; and wherein the energy management unit (110) comprises at least one algorithm for the power generation optimization, wherein the power optimization comprises load balancing, frequency regulation, and voltage control, and wherein the at least one algorithms comprises at least one of load balancing algorithm, frequency regulation algorithm, and voltage control algorithm, wherein the load balancing algorithm comprises proportional load sharing control, model predictive control (MPC), and linear or quadratic programming, wherein the frequency regulation algorithm comprises droop control, automatic generation control (AGC), and adaptive gain scheduling, and wherein the voltage control algorithms comprises volt-var control, volt-watt control and decoupled d-q vector control.

9. The method (300) as claimed in claim 7, comprising: converting, by at least one solar inverter (112) and at least one power conversion unit (114) communicatively coupled with the at least one energy management unit (110), direct current (DC) generated by the at least one solar power plant (102) into alternating current (AC) for supply to the grid (106); monitoring, controlling, and analyzing, by at least one SCADA unit (116) communicatively coupled with the energy management unit (110), the at least one solar inverter (112), and the at least one power conversion unit (114), operational performance of the solar power plant (102); storing, by at least one energy storage unit (120) communicatively coupled with the energy management unit (110) and the at least one deviation settlement server (118), excess electrical power generated by the solar power plant (102) and releasing stored power to the grid (106) during power shortage conditions;managing, by at least one load dispatch center (122) communicatively coupled with the grid (106) and the at least one energy management unit (110), the grids real- time power demand and generation, and balancing real-time supply and demand of power and coordinating scheduling requirements; and overseeing, by at least one independent power producer (124) communicatively coupled with the load dispatch center (122), the solar power plant (102) and coordinating with the load dispatch center (122), ensuring energy sales align with market conditions and regulatory frameworks.

10. The method (300) as claimed in claim 7, comprising: predicting, by the deviation settlement processing unit (206), future power outputs for upcoming time periods comprising next day, week, month, and year, generating the forecasted power that specifies grid export amounts at various intervals, the deviation settlement processing unit (206) comprising: processing, by an intelligent bidding unit (208), historical data and real-time operational data received from the solar power plant (102) using machine learning and advanced algorithms for calculating optimal bidding strategies, adjusting prices and quantities dynamically, wherein the historical data comprises energy prices, demand, and weather conditions for forecasting future trends and wherein the real-time operational data comprises operational data of the solar power plant (102) in real-time, wherein the machine learning and the advanced algorithms comprises time-series forecasting, ensemble and boosting, neural network architecture, regression, optimization framework, reinforcement learning (RL), and an adaptive and online learning, wherein the time-series forecasting comprises ARIMA (AutoRegressive Integrated Moving Average), SARIMAX (Seasonal ARIMA with eXogenous variables), State-Space Models and Kalman Filtering and Vector Autoregression (VAR), wherein the ensemble and boosting comprises Random Forest, XGBoost, LightGBM, and CatBoost, wherein the neural network architecture comprises Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer-based architectures comprising Temporal Fusion Transformers (TFT), the regression comprises linear regression, non-linearregression, and quantile regression, the optimization frameworks comprise linear programming (LP), quadratic programming (QP), mixed-integer programming (MILP), and stochastic optimization, the reinforcement learning comprises Deep Q-Networks (DQN), Policy Gradient, Proximal Policy Optimization (PPO), and Actor–Critic variants, and wherein the adaptive and online learning comprises Bayesian updating and contextual bandits; receiving, by a digital twin module (210), the real-time operational data and historical data, generating a virtual representation of the solar power plant (102) for predicting real-time power output and using at least one algorithm for forecasting energy production over different time horizons, wherein the at least one algorithm comprises grid search, random forest, linear regression and non-linear based regression analysis; comparing, by a deviation detection module (212), the real-time power output from the solar power plant (102) using grid settlement rules with the scheduled power and detect deviations in real-time; and adjusting, by a deviation correction module (214), the real-time power output in response to the detected deviations using at least one algorithm, coordinating with the energy management unit (110) for optimizing power generation, and by instructing the energy storage unit (120) for storing additional power, based on comparison of the scheduled power and the real-time power output generated during time periods of the excess power generation, wherein the at least one algorithm comprises proportional-integral-derivative (PID) control algorithms, droop control algorithms, model predictive control (MPC) algorithms, linear and quadratic programming (LP / QP) algorithms, stochastic optimization algorithms, rule-based correction algorithms, reinforcement learning algorithms and adaptive gain scheduling algorithms, and wherein the reinforcement learning algorithms comprises Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), or Actor–Critic methods.

11. The method (300) as claimed in claim 9, comprising:providing the at least one energy storage unit (120) comprising a battery energy storage system (BESS) having a capacity sized between 3% and 7% of a total rated capacity of the solar power plant (102); performing, by the SCADA unit (116), predictive maintenance using at least one prediction algorithm by analyzing real-time data trends, triggering alarms, and generating reports for regulatory compliance, wherein the at least one prediction algorithm comprises time-series forecasting, statistical process control (SPC), anomaly detection, deep learning, transformer-based forecaster, remaining useful life (RUL), ensemble regression, change point detection, and feature extraction and health indices, wherein the time-series forecasting comprises ARIMA (AutoRegressive Integrated Moving Average) and SARIMAX (Seasonal ARIMA), wherein the SPC comprises Shewart, chart, CUSUM (Cumulative Sum) Chart, and Exponentially Weighted Moving Average (EWMA), wherein the anomaly detection comprises Isolation Forest, One-Class SVM, Local Outlier Factor (LOF), robust z-scores (MAD / IQR), and PCA-based residual, wherein the deep learning comprises LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), autoencoder and Temporal Convolutional Networks (TCN), wherein the transformer-based forecaster comprises Temporal Fusion Transformers (TFT), wherein the RUL comprises Weibull / cox models, random survival forests, regression on cumulative stressors, wherein the ensemble regression comprises gradient boosting (XGBoost / LightGBM / CatBoost) and random forests, wherein the change point detection comprises Page–Hinkley, Bayesian online change-point detection for early regime shifts in inverter efficiency, fan speed profiles, or PQM indices, and wherein the feature extraction and health indices comprises spectral features (FFT), THD / THC, crest factors, kurtosis; and managing, by the independent power producer (124), operation of the solar power plant (102) and coordinating with the load dispatch center (122) for power scheduling and compliance.

12. The method (300) as claimed in claim 10, comprising:locating a power quality meter (PQM) at the point of interconnection (104) for monitoring, analyzing, and reporting quality of the generated power from the solar power plant (102); managing, by the deviation correction module (214), the detected deviations by limiting the real-time power injected into the grid (106) when excess power is generated compared to the scheduled power; and communicating an excess energy instruction to the energy storage unit (120) to store additional power, based on comparison of the scheduled power and real-time power generated during time periods of the excess power generation. Date:16thOctober, 2024 Digitally Signed Muhammad Muzzammil (Agent for the Applicant) IN / PA: 5814

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