What the Data for This Category Looks Like
Daily energy storage yield data draws from two primary sources: charge-discharge operational data collected by the local SCADA system of energy storage power stations, and public power trading market data from regional power grids. Data updates occur once daily, with full compilation of the previous day’s data completed in the early morning of the next day. Each record corresponds to daily revenue statistics for a single power station. Most documents use structured JSON or CSV formats, and include fields such as 电站ID, 统计日期, 充放电总电量, 上网电价, 运维成本, 总收益, and 等效利用小时数, with clear physical units attached to each field.
What Constraints These Characteristics Impose on Database and Operations
The daily full update requirement means the database must support high-throughput batch write operations, while avoiding timeouts caused by excessively large single write data volumes. The need to integrate multiple data sources requires that data association relies on the combined unique identifier of 电站ID and 统计日期 to prevent duplicate data writes. The requirement for clear units on all fields means the database layer must configure field validation rules to filter records with abnormal values, avoiding impacts on subsequent yield calculations. The sensitivity of financial revenue fields means the operations link must configure strict data backup and access permission policies to comply with industry data compliance requirements. The high-frequency query demand by date dimension also requires the database storage engine to support fast retrieval across time ranges.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
batch_write_size | 500-1000 records/batch | Adapts to the single-batch data volume for daily full imports, balancing write efficiency and gateway timeout risks |
data_update_cron | 0 2 * | Triggers data synchronization at 2 AM daily, avoiding peak call periods on power grid trading platforms during daytime |
field_validation_enabled | Enabled | Validates the numerical legitimacy of fields such as 充放电总电量 and 总收益, filtering abnormally entered data |
db_storage_engine | Columnar storage engine | Adapts to high-frequency query scenarios by date and power station ID, improving bulk read performance |
query_timeout | 30 seconds | Meets response time requirements for cross-data-source associated queries, avoiding frontend request timeouts |
backup_policy | Full daily backup + incremental backup every 6 hours | Ensures recoverability of financial data, complying with industry data security and compliance requirements |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by data format, volume, and business rules. Each scenario requires individual analysis, and testing against your own samples is recommended before finalizing settings.
Three Common Mistakes
- Symptom:
429 Too Many Requestserror when calling multi-power-station data interfaces. Cause: No reasonable concurrent request threshold is set, and simultaneous API calls exceed the rate limiting rules of the data source interface. - Symptom: Unable to connect to the SQL Server database. Cause: Correct database driver dependencies and port mappings are not configured, or cross-network segment access permissions are not open.
- Symptom: Duplicate records appear in daily data. Cause: The combined unique constraint of
电站ID+统计日期is not set, and multiple identical records are written when synchronization tasks are triggered repeatedly.
How to Verify Proper Configuration
- Run a manual synchronization task, and check that the number of new records in the database matches the number of records in the data source export file.
- Initiate a query with
电站IDand统计日期, and confirm that the units of the returned fields match the preset configuration. - Simulate concurrent requests, observe the interface return status codes and error information in logs, and confirm that the rate limiting configuration takes effect.
- Trigger a backup task, verify that the backup file is generated successfully and can be decompressed normally to restore data.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.