Solution Summary
AI-Powered Fuel Cost Reduction
Minimize fuel costs by implementing coordinated control with diesel generators. AI-based optimization allows generators to operate over 20% longer compared to traditional diesel-only operations. (it varies by installation capacity and a demand pattern)
AI-Driven 100% Clean Energy Offgrid Operation Support
Utilize optimal operation techniques that account for demand characteristics, enabling independent operation solely with ESS and PV without relying on diesel generators, achieving 100% clean energy-based operation.
Features
Generation/Demand Forecasting and Optimization for Local Conditions
Encored's global weather forecasting provides detailed high-resolution weather forecast for any region worldwide, independent of local meteorological agencies, enhancing the accuracy of generation and demand forecasts. The generation and demand forecasts are utilized for optimizing ESS performance.
Coordinated Control with a Diesel Generator
Model the fuel efficiency of a diesel generator and the minimum output constraint, combining these with demand and generation forecasts to calculate the optimal ESS schedule. Design PV and ESS capacities that enable stable diesel generator operation by modeling dynamics to mitigate sudden demand changes and generation fluctuations, and validate these designs through simulations.
Independent Operation with ESS and PV
When sufficient generation is provided by PV, support diesel-free operation using the independent operation capabilities of ESS. This allows for carbon-free power supply, with AI-based optimization executing ESS charge and discharge strategies to maximize operational duration. Validate the stability of independent operation in advance by simulating dynamics to mitigate sudden demand changes and generation fluctuations.
On-Grid Cost Minimization
Analyze local TOU and demand pricing, and contract terms to make ESS operation schedules that minimize costs when connected to the grid. This is based on PV generation and demand forecasts, and controls the ESS charge/discharge schedule considering utility changes. Implement reinforcement learning-based AI to continuously adapt to changing plan and constraints, allowing the system to learn from data without needing additional optimization modeling for future changes.
Case Study
Project
- NELHA 55’’ Pump Station
Objectives
- Increase the use of renewables and enable independent operation during power outages
Installation Capacity:
- Solar: 500 kW
- ESS: 250 kW / 750 kWh
- Diesel: 1 MW (using existing generator)
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