Behind-the-Meter (BTM) Optimization

The primary economic driver for BTM storage. Algorithms forecast site load and dispatch batteries to cap the maximum power draw from the grid during a billing interval.

• Peak Shaving: Discharging stored energy precisely when facility load spikes to shave the top off the demand curve.
• Predictive Windows: Using rolling 15-minute interval forecasts to avoid setting a new peak later in the billing cycle.
• Tariff Awareness: The logic ingests complex utility rate structures, including non-coincident demand charges and time-of-use energy rates.

The algorithmic strategy to minimize export to the grid and maximize on-site usage of locally generated solar energy.

• Dynamic Feed-In Management: Throttling inverter output or diverting excess solar to battery storage when export prices are low or grid constraints exist.
• Load Following: Scheduling deferrable loads (e.g., EV charging, HVAC pre-cooling) to align with the solar generation profile.
• Zero Export Profiles: Enforcing strict limits at the point of common coupling to comply with utility interconnection agreements.

The automated cycling of battery energy storage to exploit differential energy pricing across the day.

• Charge Scheduling: Importing and storing energy during low-cost off-peak periods (typically overnight).
• Discharge Dispatch: Injecting stored energy to serve site load during high-cost on-peak windows.
• Degradation-Aware Cycling: The optimization engine factors in the marginal cost of battery degradation to ensure the arbitrage spread remains profitable.

Enabling BTM assets to bid into wholesale markets or utility programs for revenue stacking.

• Frequency Regulation: Autonomous response to grid frequency deviations using fast-ramping battery inverters.
• Demand Response (OpenADR 2.0b): Receiving automated dispatch signals to curtail load or export power during grid stress events.
• Spinning Reserve: Maintaining a reserved capacity in the battery that can be discharged instantly to support the grid.

The real-time constraint management ensuring BTM operations do not violate the utility's dynamic connection limits.

• Import/Export Limits: The controller respects time-varying kW limits broadcast by the distribution utility to prevent voltage violations.
• Volt-Watt and Volt-VAR Curves: Smart inverters autonomously adjust active and reactive power based on local terminal voltage measurements.
• Anti-Islanding Compliance: Continuous monitoring to ensure the system ceases to energize within 2 seconds of a grid outage per IEEE 1547.

The advanced mathematical core that optimizes BTM dispatch over a receding time horizon.

• State Forecasting: Predicts future solar generation, load, and energy prices using machine learning models.
• Constraint Solving: Uses Mixed-Integer Linear Programming (MILP) to solve the unit commitment problem while respecting battery state-of-charge limits.
• Real-Time Feedback: Re-optimizes the dispatch plan every few minutes to correct for forecast errors and unexpected load changes.

A control logic that dispatches battery energy storage to discharge during periods of highest site load, thereby reducing the maximum demand charge levied by the utility. The algorithm analyzes historical load profiles and real-time meter data to predict the optimal discharge window. Key characteristics include:

• Target: Minimize the highest 15-minute interval kW reading in a billing cycle
• Method: Forecasts load spikes using regression models and pre-charges the battery from solar or off-peak grid power
• Result: Can reduce demand charges by 30-70% for commercial and industrial customers

The strategy of charging a battery energy storage system during low-price off-peak periods and discharging it during high-price on-peak periods to capture the energy cost differential. This exploits the temporal spread in volumetric energy rates. The optimization engine must account for:

• Round-trip efficiency losses (typically 85-95%)
• Battery degradation costs per cycle
• Solar generation forecasts to avoid charging when PV is abundant
• Dynamic tariff structures with seasonal and hourly variations

The autonomous adjustment of a distributed energy resource's real and reactive power output based on local voltage and frequency measurements. Modern smart inverters certified under UL 1741 SB execute mandatory grid-support functions including:

• Volt-VAR mode: Absorbs reactive power when voltage is high, injects when low
• Frequency-Watt mode: Reduces active power output as grid frequency rises above nominal
• Volt-Watt mode: Curbs active power export when local voltage exceeds a threshold
• Ramp rate controls: Limits the rate of power change to prevent voltage flicker

A statistical methodology that estimates what a customer's energy consumption would have been without a demand response event, used to calculate incentive payments. Common methods include:

• 10-of-10 Baseline: Averages the 10 highest usage days from the prior 10 non-event days
• Matching Day Baseline: Uses consumption from the most recent comparable day with similar weather and occupancy
• Regression-Based Baseline: Fits a model using temperature, time-of-day, and day-type variables Accurate CBLs are critical for performance-based compensation in virtual power plant programs.

A time-varying import and export capacity limit calculated by the distribution utility for a specific grid connection point. Unlike static connection agreements, DOEs enable higher DER penetration by dynamically managing constraints:

• Calculation: Based on real-time state estimation of the low-voltage network
• Communication: Sent to the DER management system via IEEE 2030.5 or OpenADR
• Benefit: Allows customers to export more energy during low-congestion periods while enforcing strict limits only when the network is constrained
• Outcome: Maximizes BTM asset utilization without triggering voltage or thermal violations

An advanced optimization strategy that uses a dynamic model of the microgrid to forecast future states and determine the optimal dispatch schedule over a receding time horizon. Unlike simple rule-based controllers, MPC:

• Solves a constrained optimization problem at each time step
• Incorporates forecasts of load, solar generation, and energy prices
• Accounts for battery state-of-charge dynamics and degradation
• Handles multi-objective trade-offs between cost, comfort, and resilience This is the gold standard for commercial BTM optimization when paired with Mixed-Integer Linear Programming (MILP) solvers.