GreenGrid Hub is an enterprise-grade renewable energy management platform that unifies real-time grid data, weather intelligence, and IoT sensor networks into a comprehensive operational dashboard for distributed energy asset monitoring. The system demonstrates full-stack integration capabilities by connecting live external APIs with simulated asset telemetry to create a production-ready interface for renewable energy portfolio management, grid optimization, and carbon impact tracking.

The project addresses a fundamental challenge in the renewable energy sector: the fragmentation of data sources across grid operators, weather services, and distributed asset networks. Energy managers typically juggle multiple disconnected systems to monitor carbon intensity, weather conditions affecting generation, asset performance, and financial returns. I designed GreenGrid Hub to consolidate these disparate data streams into a unified command center, enabling real-time decision-making for energy trading, maintenance scheduling, and grid balancing operations.

The platform’s architecture integrates the UK National Grid Carbon Intensity API to provide live carbon emissions data measured in grams of CO₂ per kilowatt-hour (g/kWh), allowing operators to optimize generation timing for maximum environmental impact. This API connection delivers regional and national grid intensity forecasts, enabling predictive dispatch strategies that align renewable energy injection with periods of highest fossil fuel displacement. The system processes carbon intensity data at 30-minute intervals, matching the UK electricity market settlement periods, and calculates cumulative CO₂ savings in real-time based on actual generation volumes.

Weather intelligence is powered by the OpenWeatherMap API, which provides hyperlocal meteorological data essential for renewable energy forecasting. The integration retrieves wind speed, solar irradiance, temperature, cloud cover, and precipitation data for each asset location, correlating weather patterns with generation performance to detect underperforming assets and validate production forecasts. The weather module implements fallback logic and error handling to maintain system reliability when external API connections experience latency or downtime.

The IoT data layer simulates telemetry from 523 distributed renewable energy assets, generating realistic power output readings, operational status updates, and performance metrics that mirror actual smart inverter and SCADA system data structures. This simulated dataset enables demonstration of time-series data processing, anomaly detection algorithms, and real-time aggregation techniques without requiring physical hardware infrastructure. The simulation engine produces statistically accurate generation profiles based on time-of-day patterns, seasonal variations, and weather correlations.

The front-end dashboard implements a card-based KPI visualization system displaying current power generation (MW), active asset count, system-wide efficiency percentages, and daily revenue projections. Each metric includes trend indicators showing percentage changes versus previous periods, enabling rapid identification of performance shifts. The interface uses color-coded status indicators for instant visual assessment, with green confirmation for connected APIs and red alerts for service disruptions. The design follows modern energy sector UI conventions with a professional color scheme optimized for operations center display environments.

The backend architecture employs RESTful API integration patterns with asynchronous request handling to prevent UI blocking during external API calls. The system implements intelligent caching strategies to minimize API request volumes while maintaining data freshness requirements for operational decision-making. Error handling includes graceful degradation logic that maintains partial functionality when individual data sources become unavailable, with clear status communication through the API Connections monitoring panel.

Revenue calculation logic combines real-time generation data with UK wholesale electricity prices, factoring in Renewable Obligation Certificates (ROCs), Feed-in Tariffs (FiTs), and Contracts for Difference (CfD) mechanisms that govern renewable energy compensation in the British market. The financial module projects end-of-day revenue based on current generation trajectories and forward price curves, providing operators with actionable financial intelligence for intraday trading decisions.

Project Outcome

GreenGrid Hub delivers a functional prototype that validates technical capabilities essential for energy sector software development, including multi-API orchestration, time-series data visualization, operational dashboard design, and domain-specific calculation logic. The platform demonstrates understanding of renewable energy market structures, grid operations terminology, and the data integration challenges faced by distributed energy resource management systems. Beyond its immediate demonstration value, the architecture establishes patterns for building scalable energy management platforms capable of handling thousands of assets across diverse generation technologies, with extension points for predictive maintenance algorithms, automated trading systems, and regulatory compliance reporting. The system showcases how modern web technologies can be applied to critical infrastructure monitoring, delivering the real-time performance and reliability standards expected in energy operations environments.