Grid Intelligence
for Clean Energy
Clean, Reliable, and Cost-Optimized Energy
The energy transition is becoming more distributed, more variable, and more real-time—with solar, wind, batteries, EVs, and flexible loads transforming grid operations. Akshaya.io helps utilities, energy companies, campuses, and charging network operators deploy production-grade AI that improves reliability, reduces cost, and accelerates decarbonization through smarter forecasting, optimization, and autonomous control.
The Energy Transition Needs Intelligent Software
The transition to renewable energy isn't just a hardware challenge—it's a data challenge. Akshaya.io applies industrial-grade AI to the energy sector's most complex problems. From optimizing battery storage arbitrage to orchestrating microgrids and managing EV charging networks, we engineer the digital nervous systems that make sustainable infrastructure profitable, resilient, and scalable.
Profitable
Maximize asset value and ROI through intelligent optimization
Resilient
Maintain operations through disturbances and grid events
Scalable
Deploy solutions that grow with your portfolio
Sustainable
Accelerate decarbonization with measurable impact
What We Deliver
1) AI for Energy Storage (BESS + Battery Fleets)
Turn storage into a controllable, revenue-generating grid asset. We build AI that improves battery performance, safety, and economics using telemetry, degradation modeling, and dispatch optimization.
Core Capabilities
- State of Charge / State of Health / Remaining Useful Life estimation using advanced time-series ML
- Predictive maintenance & anomaly detection for thermal issues, cell imbalance, inverter faults, and abnormal patterns
- Optimal dispatch & bidding intelligence (where applicable): price-aware, constraint-aware charge-discharge strategies
- Safety analytics: risk scoring, fault signatures, and automated incident triage
Business Outcomes
- Longer asset life and improved availability
- Lower O&M costs and fewer unplanned outages
- Better utilization and improved ROI
2) AI for Microgrids & Autonomous Energy Systems
Operate microgrids with intelligence, resilience, and real-time optimization. As systems get more heterogeneous (PV, storage, EVs, building controls), coordination becomes the challenge.
Core Capabilities
- Forecasting + optimization loops for load, renewables, and storage to reduce cost and improve stability
- Real-time control architectures supporting centralized/decentralized/distributed coordination
- Islanding logic (grid-connected ↔ island mode) to maintain continuity during disturbances (site-dependent)
- Controller validation support and performance testing patterns (including hardware-in-the-loop where relevant)
Business Outcomes
- Higher uptime and faster recovery from outages
- Reduced fuel/energy cost and improved power quality
3) AI for DERM / DERMS (Distributed Energy Resource Management)
Orchestrate DERs at scale—forecast, coordinate, and control. We help integrate and operationalize DERMS intelligence across diverse assets.
Core Capabilities
- Targeted forecasting (AMI + weather + device telemetry) for flexibility planning
- DER coordination & constraint management under feeder limits, voltage constraints, and program rules
- Flexibility enablement: automated demand response orchestration and program performance monitoring
- Grid visibility intelligence: anomaly detection, event correlation, and operational decision support
Business Outcomes
- Reduced peak demand and improved reliability
- Smoother DER interconnection operations and higher program performance
4) AI for EV Charging Solutions (Smart Charging + V2G Readiness)
Optimize charging operations—without stressing the grid. We help CPOs and fleets improve utilization, reliability, and site economics.
Core Capabilities
- Dynamic load management for sites/fleets: stay within constraints, reduce demand charges, improve throughput
- Charging demand prediction to plan capacity, reduce congestion, and improve customer experience
- Carbon- and price-aware charging where data signals exist
- V2G strategy enablement: optimize when EVs charge/discharge based on operational signals and constraints (where supported)
Business Outcomes
- Lower OPEX and improved charger utilization
- Reduced grid stress and improved uptime
5) Grid Planning & Scenario Modeling (AI-Accelerated)
Use AI to improve planning speed and scenario fidelity for DER growth, EV adoption, and extreme-event resilience.
Core Capabilities
- AI-based scenario modeling for growth, EV adoption, DER penetration, and extreme events
- Faster planning workflows using surrogate models and stochastic optimization patterns
- Risk and resilience analytics for operational and investment decisions
Top AI Capabilities
Grid & Load Forecasting (Multi-Horizon)
Short- to long-horizon forecasting for load, renewables, and charging demand using time-series ML plus weather and telemetry signals.
Real-Time Optimization & Control
Constraint-aware optimization coordinating storage, PV, and controllable loads across centralized and distributed control patterns.
Energy Storage Health Intelligence (SOH/RUL)
Battery health estimation and remaining-life prediction using telemetry-driven ML to improve maintenance planning and lifecycle economics.
Anomaly Detection & Predictive Maintenance
Early detection of failure patterns across batteries, inverters, microgrid controllers, and charging hardware to reduce downtime and safety risk.
DERMS Forecasting & Coordination
Forecasting and orchestration to support grid reliability, flexibility programs, and aggregated dispatch under operational constraints.
Smart Charging & Fleet Optimization
Optimize charging schedules with dynamic load management and behavior prediction to reduce costs and improve utilization.
V2G Strategy & Optimization Enablement
Model and optimize bidirectional charging strategies (where supported) to coordinate EV batteries as flexible resources.
AI-Accelerated Grid Planning & Scenario Modeling
Generate high-fidelity scenarios and speed up planning/optimization to support reliability, affordability, and decarbonization.
Typical Deliverables
- Sustainability AI roadmap (use cases, data sources, architecture, ROI)
- Forecasting + optimization pipelines (time-series ML + operational constraints)
- DERMS/EMS integration design (telemetry ingestion, control signals, observability)
- Production deployment patterns (monitoring, drift detection, alerting, incident runbooks)
- Performance measurement: reliability, cost, CO₂ impact, utilization
Engagement Options
AI for Sustainability Assessment
Use-case prioritization + data readiness + ROI
Microgrid / DERMS Optimization Sprint
Forecasting + control logic + dashboards
Energy Storage Intelligence Build
SOH/RUL + anomaly + dispatch optimization
EV Charging Optimization Launchpad
Smart charging + site/fleet orchestration
Frequently Asked Questions
What does AI for sustainability mean in the energy sector?
AI for sustainability in energy refers to using machine learning and optimization algorithms to improve efficiency, reduce waste, and accelerate decarbonization. This includes forecasting renewable generation, optimizing battery dispatch, coordinating distributed energy resources (DERs), managing EV charging loads, and enabling autonomous microgrid operations—all with the goal of reducing carbon emissions while maintaining reliability and cost-effectiveness.
How does AI improve battery storage ROI and safety?
AI improves battery storage economics through better State of Health (SOH) and Remaining Useful Life (RUL) estimation, enabling optimized maintenance scheduling and asset lifecycle planning. Predictive maintenance detects anomalies like thermal issues or cell imbalance before they cause failures. Dispatch optimization uses price signals and constraints to maximize revenue while preserving battery health. Together, these capabilities reduce O&M costs, extend asset life, and improve utilization.
What is DERMS and how does AI help orchestrate DERs?
DERMS (Distributed Energy Resource Management System) is software that coordinates distributed assets like solar, storage, EVs, and flexible loads. AI enhances DERMS through improved forecasting (predicting generation and load), constraint-aware optimization (managing voltage and thermal limits), and automated demand response orchestration. This enables utilities and aggregators to reliably dispatch DERs at scale while respecting grid constraints.
How does smart charging reduce demand charges and grid congestion?
Smart charging uses AI to dynamically manage when and how fast vehicles charge based on electricity prices, grid constraints, and user needs. By shifting charging to off-peak hours and staying within site power limits, organizations can significantly reduce demand charges. At scale, coordinated charging reduces grid congestion and can defer expensive infrastructure upgrades.
What is V2G and when does it make business sense?
V2G (Vehicle-to-Grid) allows EVs to discharge power back to the grid or building during peak demand periods. It makes business sense when: (1) there are sufficient price spreads between charging and discharging times, (2) vehicles have predictable idle periods, (3) grid services markets offer attractive compensation, and (4) battery degradation from additional cycling is factored into economics. Fleet operations with predictable schedules often see the best V2G business cases.
How do you measure carbon impact from AI-driven energy optimization?
Carbon impact measurement requires tracking emissions across operations—direct emissions from on-site generation, indirect emissions from grid electricity (using locational marginal emissions data), and avoided emissions from optimization. We help organizations establish baselines, implement real-time carbon accounting linked to operational data, and report verified impact against targets. This enables transparent Scope 2 and Scope 3 reporting for sustainability goals.
Akshaya.io delivers AI for net zero initiatives with smart grid optimization, DERMS consulting, and advanced AI in energy storage. Our work spans microgrid control systems, EV charging infrastructure optimization, virtual power plants (VPP), V2G (vehicle-to-grid) strategy enablement, and predictive maintenance for renewables—helping organizations modernize operations while improving carbon transparency and resilience.