Optimization

The optimization module implements a mixed-integer linear programming (MILP) formulation for energy system optimization using Pyomo as the mathematical modeling framework. The energy management problem is expressed through linear objective functions and constraints representing system operation, energy balances, and logical decisions, with both continuous and integer decision variables. The resulting MILP model is solved using the HiGHS optimization solver via its Python interface Highspy, providing efficient and reliable solutions for large-scale and time-coupled optimization problems. This architecture enables flexible model extension, solver interchangeability, and repeated execution for real-time or near-real-time energy optimization applications. At regular intervals, the optimization module provides power setpoints for energy assets based on the measured data.

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Features

• 7 Built-in Optimization Objectives

  • Maximize Self-Consumption
  • Maximize EV Satisfaction
  • Minimize Fossil Emissions
  • Maximize Reliability
  • BESS Lifetime Extension
  • Peak Shaving / Grid Support
  • Maximize Weighted Power Flow

• Multi-Asset Support

  • Multiple devices of the same type
  • Automatic asset aggregation

• Comprehensive Validation

  • Automatic configuration validation
  • Asset requirement checking
  • Warning system for edge cases

Supported Assets

Supported Type	Description
Solar PV	Electrical plant with photovoltaic panels
Battery Storage	Battery Energy Storage System (BESS)
Load	Any load which can be curtailed/shedded during the optimization process. For example, lighting during the day
Critical Load	The load which must not be curtailed during the optimization process. For example, server's power supply.
Unidirectional EV charger	EV chargers which support only one way of energy flow (from grid to car)
Bidirectional EV charger	EV charger that support bidirectional power flow, i.e. Vehicle to Grid (V2G) chargers
Wind Generator	Wind generator
Active Front End	Indicates the asset which connects the site to the main electrical grid.

Supported Objectives

Objective	Code	Best For
Maximize Self-Consumption	maxSelfConsumption	Solar/wind sites wanting to minimize grid import
Maximize EV Satisfaction	maxEVSatisfaction	EV charging stations, fleet operations
Minimize Fossil Emissions	minFossilEmissions	Sustainability goals, carbon reduction
Maximize Reliability	maxReliability	Critical infrastructure, hospitals, data centers
BESS Lifetime Extension	lifeExtentBESS	Expensive batteries, long-term cost optimization
Peak Shaving	peakShaving	Demand charge reduction, grid services
Maximize Weighted Power Flow	maxWeightPowerFlow	Maximize weighted power flows, prioritizing: PV -> Loads -> EVs -> GridService -> BESS -> Grid Import -> V2G

Maximize Self-Consumption (maxSelfConsumption)

Goal: Maximize the use of locally generated renewable energy

Strategy:

• Prioritize PV and wind power usage
• Store excess renewable energy in BESS
• Minimize grid import
• Prefer local consumption over export

Required Assets:

• At least one AFE (Active Front End)
• At least one renewable source (PV or WIND)

Optimal For:

• Sites with significant renewable generation
• Reducing grid dependency
• Lowering energy costs

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Maximize EV Satisfaction (maxEVSatisfaction)

Goal: Maximize EV charging to meet departure requirements

Strategy:

• Prioritize EV charging above other loads
• Ensure vehicles reach target SoC
• Minimize V2G discharge
• Use all available energy sources

Required Assets:

• At least one AFE
• At least one EV charger (UNI_EV or BI_EV)

Optimal For:

• Fleet charging operations
• EV charging stations
• Sites where EV charging is critical

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Minimize Fossil Emissions (minFossilEmissions)

Goal: Minimize carbon footprint by reducing fossil fuel usage

Strategy:

• Maximize renewable energy usage
• Minimize grid import (assumed fossil-based)
• Store renewables in BESS
• Allow controlled load shedding if necessary

Required Assets:

• At least one AFE
• At least one renewable source (PV or WIND)

Optimal For:

• Sustainability-focused operations
• Carbon-neutral goals
• Green energy initiatives

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Maximize Reliability (maxReliability)

Goal: Ensure continuous power supply to critical loads

Strategy:

• Maintain high BESS charge for backup
• Prioritize critical loads
• Keep reserve capacity
• Minimize grid dependence

Required Assets:

• At least one AFE
• At least one BESS (for backup power)

Optimal For:

• Data centers
• Hospitals
• Critical infrastructure
• Sites requiring high uptime

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BESS Lifetime Extension (lifeExtentBESS)

Goal: Extend battery life by minimizing cycling and stress

Strategy:

• Minimize charge/discharge cycles
• Keep SoC in optimal range (40-70%)
• Avoid deep discharge
• Reduce power throughput
• Use grid and V2G instead of BESS when possible

Required Assets:

• At least one AFE
• At least one BESS

Optimal For:

• Expensive battery systems
• Long-term cost optimization
• Systems with limited battery replacement budget

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Peak Shaving / Grid Support (peakShaving)

Goal: Reduce peak demand and provide grid services

Strategy:

• Reduce peak grid import
• Provide grid services when requested
• Use BESS to flatten load profile
• Export excess renewable energy

Required Assets:

• At least one AFE
• At least one BESS

Optimal For:

• Demand charge reduction
• Grid service revenue
• Utility partnerships
• Load balancing applications

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Choosing the Right Objective

For renewable energy sites:

• Use maxSelfConsumption to maximize on-site renewable usage
• Use minFossilEmissions to minimize carbon footprint

For EV charging:

• Use maxEVSatisfaction to prioritize vehicle charging

For critical infrastructure:

• Use maxReliability to ensure backup power
• Use peakShaving to reduce demand charges

For battery health:

• Use lifeExtentBESS to minimize battery degradation
• Best for expensive or hard-to-replace batteries

Maximize Weighted Power Flow (maxWeightPowerFlow)

Maximize weighted power flows, prioritizing: PV -> Loads -> EVs -> GridService -> BESS -> Grid Import -> V2G