Power & Energy

Power Trading

Centralized Processing and Analytics for Market Operations

A unified data foundation and high-performance compute layer for multi-source data management, forecast validation, clearing and settlement, and risk assessment.

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Industry Background

From Fragmented Data to Unified Intelligence

Power market operations involve trading centers, dispatch centers, and a wide range of market participants, centered on the cycle of data → forecasting and clearing → settlement and assessment → review and optimization.

Operations depend heavily on time-series data — load, price, clearing, and metering curves — drawn from both market systems (bidding, transaction, settlement) and operational systems (SCADA, metering, weather, equipment, and consumption). This places stringent demands on data accuracy, timeliness, and system performance.

High-Frequency Spot Trading
As spot markets mature, trading frequency has shifted from monthly and annual cycles to day-ahead and real-time, compressing data processing requirements from hours to minutes.
Growing Market Complexity
Generators, consumers, retailers, and energy storage participants all interact across data exchange, contract performance, and deviation assessment — creating complex business rules and coordination demands.
High Renewable Penetration
High output volatility from renewables makes forecasting difficult, raising the bar for deviation management and precision calculation in ancillary service markets.
Challenges
Siloed Data
Trading, dispatch, metering, weather, generation, and consumption data are scattered across systems with inconsistent field definitions and time granularities — forcing manual reconciliation and repeated validation for clearing, settlement, and deviation analysis.
High-Volume Time-Series Data Computation
Load forecasting, generation forecasting, clearing, and metering curves all require continuous alignment, aggregation, windowing, interpolation, resampling, and version management — placing sustained pressure on storage and compute performance.
Weak Forecasting Engineering Support
Dispatch-side load forecasting spans bus, zone, and system levels, while participant-side output forecasting requires integrating weather, holidays, pricing, and events. Feature engineering and backtesting are complex, with no unified platform to support the full workflow.
Complex Settlement Calculation
Settlement involves multiple participants, rules, and fee types — deviation, assessment, compensation, and allocation — with frequently changing rule sets. Existing workflows rely on offline scripts and stitched-together systems, making iteration slow and computation hard to audit.

DolphinDB Solution

Multi-Model Data Storage

  • Trading, metering, clearing, and settlement data are partitioned by time and stored in distributed tables to maintain query performance as data volumes grow.
  • Time-series curves (forecasting, metering, pricing) and relational data (contracts, profiles, participants, parameters, and rule configurations) are managed within a single platform, eliminating data silos.

Distributed Multi-Table Join

  • Large trading and metering tables can be joined with contract and reference tables, with field alignment, computation, and result persistence completed in a single job.
  • Distributed parallel execution of joins, group-bys, and window aggregations reliably handles high-load scenarios such as batch clearing, settlement, and reconciliation.

Modular Rule Engineering

DolphinDB's scripting language and operator library turn complex market rules into modular, reusable components:

  • Custom functions and logic are published directly via script — no compilation or packaging required, significantly shortening rule deployment cycles.
  • Core computation logic is organized as reusable operators versioned by market rule edition.
  • New and existing rules can be run in parallel on the same data foundation, enabling fast diff analysis to support decision-making across trading centers, dispatch centers, and generation enterprises.

Model Forecasting and Inference

  • Built-in machine learning support enables feature engineering, model training, inference, and evaluation directly within the database, with results written back in place — eliminating data movement.
  • Integration with time-series models and external algorithm ecosystems shifts the workflow from "export data, then model" to "model directly on the data" — improving efficiency and consistency.

Key Benefits

Unified Architecture, Consistent Data

Trading, clearing, metering, settlement, contracts, rules, and weather data are all modeled within a single platform — eliminating cross-system fragmentation and inconsistent definitions.

High-Performance Storage and Query

Distributed architecture and partitioned storage maintain write, query, and aggregation performance under high-load scenarios such as settlement windows, batch clearing, and bulk reconciliation.

Faster Rule and Model Iteration

A versioned, scriptable operator library enables rapid rule deployment. Forecasting models train, infer, and write results directly on the data — significantly compressing the overall iteration cycle.

Ready to Streamline Your Power Trading Workflows?
Discover how DolphinDB can help your team:
pointUnify trading, clearing, metering, settlement, and forecasting data in one platform
pointAccelerate load forecasting, clearing validation, settlement, and deviation assessment
pointEngineer complex market rules for rapid iteration and auditable analysis
pointImprove coordination and decision-making across trading centers, dispatch centers, and market participants
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