Energy Analysis / Sub-Area
EMS & AI-Assisted Optimization
An EMS decision does not start with a software choice: first the objective and the control logic for managing consumption, generation and storage are defined. We run that logic on models that learn energy price, load and solar generation forecasts: flexible loads are shifted to cheap and suitable hours, and the storage charge–discharge schedule is planned against price and peak targets. We have no EMS software product of our own: we run the optimization on our partner's EMS platform and put the output into service either as a recommended schedule or, on request, as automatic commands to the control system.
Which facilities this is for
- Facilities with solar and/or storage and several flexible loads
- Companies managing multiple sites from a single center
- Facilities planning an EMS investment that want an independent requirements definition
- Operations not getting the expected benefit from their existing automation
How We Build the Strategy
It starts from the objective, not from product selection: first the control objective, then the control logic, then verification.
Data Inputs
- 15-minute consumption, generation and, if any, storage data
- List of shiftable / curtailable loads and their flexibility limits
- Tariff structure and peak / price components
- Existing control rules and setpoints (if any)
- Grid connection capacity plus process and comfort constraints
- Hourly or 15-minute energy price history (day-ahead / tariff) and the source of price forecasts
Data is validated before analysis; flexibility assumptions on paper are confirmed with on-site measurement.
Model and Scenarios
- Control objectives are defined: self-consumption, peak limiting, price-based operation, backup
- Rule-based and optimization-based control logic are modeled separately
- Consumption, generation and storage are simulated together in a single dispatch model
- Portfolio-level coordination is evaluated for multiple facilities
- Price, load and solar generation forecasts are produced; the load-shifting schedule is optimized for flexible loads and storage
- Sensitivity to forecast error is tested; the schedule is bounded so it runs within process, comfort and connection-power constraints
Deliverables
- Control strategy and logic definition
- EMS functional requirements list — for tender evaluation
- Strategy comparison and expected impact ranges
- Multi-facility optimization plan
- AI-assisted price and load forecasting model (calibrated on facility data)
- Load-shifting schedule and EMS/SCADA integration: as a recommended schedule or, on request, automatic commands
Assumptions and Limits
EMS benefit depends on measured flexibility; flexibility on paper looks larger than the actual benefit until verified on site.
The strategy and model are built on the facility's data; actual performance depends on operations and how the assets respond, so no savings are guaranteed.
Optimization is bounded by physical and contractual constraints: connection capacity, process and comfort limits cannot be exceeded.
More automation is not always better; some strategies deliver benefit at a disproportionate increase in complexity.
Load-shifting gains depend on the variability of the price profile and on real flexibility; forecasting models carry uncertainty and returns are not guaranteed.
Automatic commands run only on request; authority, constraints and override rules are defined with the facility, and protection and safety functions are not handed to the EMS.
Common Mistakes
Buying an EMS product before defining the control objective.
Optimizing a single asset (only the battery, or only solar) instead of the system as a whole.
Ignoring process and comfort constraints, producing strategies operators override.
Assuming a single control strategy fits every tariff and price regime.
Reference Frameworks
- ISO 50001
- Energy management systems: a continuous improvement framework and energy performance indicators.
- IEC 61850
- Communication standard for power automation and distributed resource integration.
- IEEE 2030
- Smart grid interoperability guide: data and control interfaces between systems.
- IEC 62443
- Cybersecurity framework for industrial automation and control systems.
Methods
- Rule-based and optimization-based control logic modeling
- Multi-asset dispatch simulation
- Portfolio / multi-facility optimization
- Scenario and sensitivity analysis
- Machine-learning-based forecasting of price, load and solar generation
- Forecast-based optimization: load shifting and storage dispatch
Sample Analysis
Below, the qualitative effect of four control strategies is compared on the same facility profile. The values indicate direction; actual impact is modeled from the facility's data and flexibility profile.
EMS Control Strategy Comparison
Sample Scenario
| Strategy | Primary objective | Self-consumption effect | Peak effect |
|---|---|---|---|
| Self-consumption priority | Use solar on site | High | Medium |
| Peak limiting | Cap peak demand | Medium | High |
| Price-based | Charge when cheap, discharge when expensive | Variable | Low |
| Forecast-based (AI) | Shift load on price and load forecasts | High | High |
| Hybrid (rule + optimization) | Balance objectives together | High | High |
Strategy: Self-consumption priority
- Primary objective
- Use solar on site
- Self-consumption effect
- High
- Peak effect
- Medium
Strategy: Peak limiting
- Primary objective
- Cap peak demand
- Self-consumption effect
- Medium
- Peak effect
- High
Strategy: Price-based
- Primary objective
- Charge when cheap, discharge when expensive
- Self-consumption effect
- Variable
- Peak effect
- Low
Strategy: Forecast-based (AI)
- Primary objective
- Shift load on price and load forecasts
- Self-consumption effect
- High
- Peak effect
- High
Strategy: Hybrid (rule + optimization)
- Primary objective
- Balance objectives together
- Self-consumption effect
- High
- Peak effect
- High
This is a sample scenario; it indicates qualitative direction and cannot be used on its own as an investment or control decision.
Related questions
It depends on the work. In energy analysis, power systems and project management we don't sell equipment or take on turnkey contracting; we stay on the owner's side and build the technical and financial basis for a decision, and implementation is carried out by the supplier or contractor you choose. This separation keeps our analysis free of any obligation to defend a particular product or contractor. For the measurement, communication and monitoring infrastructure of the facility digital twin, we can deliver turnkey (EPC); that scope is defined separately from the analysis work.
Our energy analysis scope spans BESS investment feasibility, solar/load analytics, EMS optimization, and energy procurement advisory — we model consumption, generation, and price data together.
EMS Strategy Consultation
We review your consumption, generation and site asset data, set the control objective that fits the facility and measure the load-shifting potential with a model built on price and load forecasts. The output is a clear function list and an executable optimization plan.
Request an EMS Strategy Consultation