Generation loss in renewable energy

 

Thanks to Shri B. Vali and Shri S. Simha chalam Sir focused on topic Generation loss in renewable energy 

To analyze generation loss in renewable energy due to equipment failure, external grid issues, internal grid limitations, and Load Dispatch Center (LDC) curtailment, a multi-faceted approach is required. This involves assessing equipment health, monitoring grid performance, analyzing curtailment events, and evaluating the impact on overall energy production. 

1. Equipment Failure:

Monitoring and Diagnostics:

Regularly monitor the performance of renewable energy equipment (solar panels, wind turbines, etc.) using SCADA (Supervisory Control and Data Acquisition) systems. Implement predictive maintenance strategies based on data analysis to identify potential failures before they occur.

Root Cause Analysis:

When equipment fails, conduct a thorough root cause analysis to determine the underlying reasons (e.g., component degradation, manufacturing defects, environmental factors). This helps in preventing similar failures in the future.

Performance Benchmarking:

Compare the actual energy output of equipment with its expected output based on design specifications and historical data. Deviations can indicate potential issues with the equipment's performance.

Availability and Reliability Analysis:

Calculate metrics like Mean Time Between Failures (MTBF) and Mean Time To Repair (MTTR) to assess the availability and reliability of equipment. This helps in optimizing maintenance schedules and spare parts inventory. 

2. External Grid Issues:

Grid Stability Analysis:

Use simulation tools and phasor measurement units (PMUs) to assess the impact of external grid disturbances (e.g., faults, voltage fluctuations) on renewable energy generation. 

Contingency Analysis:

Analyze the system's response to various grid contingencies (e.g., loss of a major transmission line) to identify potential vulnerabilities and develop mitigation strategies, according to the report on transmission system faults. 

Transmission Congestion Management:

Analyze transmission line congestion patterns and implement strategies like dynamic line rating, congestion management techniques, and grid expansion to improve transmission capacity. 

Forecasting and Scheduling:

Use advanced forecasting techniques to predict renewable energy generation and grid demand accurately. This helps in scheduling generation and optimizing grid operations to minimize curtailment. 

3. Internal Grid Issues:

Grid Stability Analysis:

Analyze the stability of the internal grid (e.g., voltage stability, frequency stability) using simulation tools. Identify potential issues like voltage sags, voltage dips, or frequency deviations that can impact renewable energy generation. 

Reactive Power Management:

Implement strategies for managing reactive power (e.g., using capacitor banks, synchronous condensers) to maintain voltage stability within the internal grid. 

Fault Ride-Through:

Ensure that renewable energy plants can "ride through" grid faults (e.g., temporary voltage dips) without tripping off the grid. This requires proper design of inverters and control systems. 

Coordination with LDC:

Maintain close coordination with the LDC to ensure that the internal grid is operated within acceptable limits and that renewable energy generation is dispatched efficiently. 

4. LDC Curtailment:

Curtailment Analysis:

Analyze the reasons for curtailment events, including grid congestion, low demand, or operational constraints. Identify the specific generators that are being curtailed and the duration of curtailment. 

Must-Run Status:

Ensure that renewable energy generators are given "must-run" status as per grid codes, subject to grid security and safety considerations. 

Demand Response:

Implement demand response programs to shift load from periods of low renewable energy generation to periods of high generation, reducing the need for curtailment, according to a study on renewable energy curtailment.