Why most households are solving the wrong problem

For years, “home energy optimization” has meant one thing:

Shift your consumption to the cheapest hours.

Run the dishwasher at night.
Charge the EV when prices are low.
Heat water when electricity is cheap.

It sounds rational.
It is simple.
And in many cases — it is wrong.

The problem: optimization has been oversimplified

Electricity price is only one variable.

In reality, household energy systems operate in a multi-dimensional system, where outcomes depend on:

• Price (spot, hybrid, fixed)
• Power (kW peaks and emerging capacity tariffs)
• Heat pump efficiency (COP varies with temperature)
• Building thermal inertia
• Solar production and self-consumption
• Storage (thermal and electrical)
• EV charging flexibility
• Comfort constraints
• Grid and market signals

When you optimize only against price, you ignore most of the system.

And that leads to suboptimal — sometimes even worse — outcomes.

The real issue: optimization has become a daily task

In many households today:

• Prices are checked daily
• Devices are manually scheduled
• Charging is constantly adjusted
• Heating is tweaked based on forecasts

What started as optimization has become:

Continuous decision-making.

Even 5 minutes per day equals over 30 hours per year.

For many households, the value of that time exceeds the financial benefit.

A different perspective

What if the goal is not to optimize better…

…but to design systems that don’t require constant optimization?

This is the idea behind HEOMF.

Introducing HEOMF
(Home Energy Optimization Maturity Framework)

HEOMF is not a product.
It is not a specific technology.

It is a framework for understanding and designing household energy systems.

The core principle:

The higher the maturity, the less manual effort is required from the user.

The HEOMF Levels

Each level is described through:

• What it is
• What is required
• What it looks like in practice

Level 0 — No Optimization (Static Household)

What it is

The household uses energy without reacting to price signals or actively controlling loads.

Devices operate using their own internal control logic (e.g. heat pump control), but there is no system-level optimization.

What is required

Nothing beyond standard installation:

• Fixed or dynamic contract (not actively used)
• No monitoring or automation

What it looks like in practice

• No hourly or 15-minute consumption tracking
• No scheduling of loads
• No prioritization of devices

Risk

• Cost and power peaks occur unintentionally
• No control over when consumption happens
• System behavior depends entirely on individual devices and installation quality

Key insight

This is not a failure.

For many households, this is the optimal level.

If savings potential is low, optimization effort is not justified.

Level 1 — Scheduling and Manual Control

What it is

The household shifts 1–2 loads manually or via timers to cheaper time periods.

Typical loads:

• Domestic hot water
• Electric heating buffers
• EV charging

What is required

• Access to price signal (spot pricing or equivalent)
• Basic consumption awareness (bill, datahub, app)
• Simple timers or manual control

What it looks like in practice

• EV charging scheduled overnight
• Water heating timed to cheaper hours
• User checks prices daily

Benefit

• First step into demand response
• Immediate and visible savings

Typical challenge

• Optimization is schedule-based, not system-based
• Comfort may be affected (too cold / too hot)
• Loads may overlap → power peaks

Level 2 — Measured and Controllable System (Basic Automation)

What it is

The household has continuous measurement and automation controlling multiple loads.

Optimization is based on rules and constraints, not manual actions.

What is required (minimum)

• Consumption and production data (hourly or preferably 15-minute resolution)
• Controllable loads:
  • EV charger
  • Heat pump / hot water
  • Resistive loads
• Integration layer (APIs, smart home platform)
• Basic monitoring

What it looks like in practice

• EV charging limited by max power
• Heating shifted based on price thresholds
• Automations like:
  • “charge when price < X”
  • “limit total power to Y kW”

Goal

Make sensible shifts without making the system fragile.

Architecture

• Separation between:
  • device-level control
  • automation layer

Limitation

• Rules are static
• Optimization is still component-level
• Interactions between systems are limited

Level 3 — Orchestrated System Optimization

What it is

Optimization becomes a separate function, coordinating multiple resources simultaneously:

• Electricity
• Heat
• Storage
• EV

Decisions are based on data and forecasts.

What is required

• Time-series data (power, energy, temperature)
• Forecasts:
  • price
  • weather
  • usage patterns
• Defined constraints:
  • comfort limits
  • device protection
  • fallback modes
• Layered architecture
• Fault-tolerant design

What it looks like in practice

• EV charging scheduled within price windows but limited by household peak power
• Thermal storage charged during cheap periods while respecting COP and temperature limits
• Solar production used to maximize self-consumption without creating instability

Why 15-minute resolution matters

As markets and balancing increasingly operate at 15-minute intervals:

Cost and power effects are shaped by short-term peaks.

Challenge

• System complexity increases significantly
• Requires careful architecture design

Level 4 — Market- and Grid-Participating Household

What it is

The household can actively participate in energy markets:

• Providing flexibility
• Offering capacity or reserves
• Acting via an aggregator or directly

What is required

• Understanding of market processes:
  • reserve products
  • testing and qualification
  • data exchange
• Telemetry and controllability
• Defined responsibilities (e.g. balance responsibility, aggregation)
• Cybersecure and auditable implementation

What it looks like in practice

• Loads or storage respond to external control signals
• Participation in reserve or flexibility markets
• Aggregator coordinates multiple households

System requirements

• Reliable communication
• Verified performance
• Compliance with market rules

Future path (e.g. V2G)

• Vehicle-to-grid integration
• Interoperability standards (e.g. ISO 15118)
• Increasing role of standardization and regulation

Why this framework matters

Energy systems are evolving rapidly:

• Dynamic pricing is becoming standard
• Capacity-based tariffs are emerging
• Solar and batteries are increasing
• Flexibility markets are expanding

Households are no longer passive consumers.

They are becoming distributed energy systems.

The key question

Most discussions focus on:

• Devices
• Automations
• Algorithms

But the real question is:

What level of maturity does your household actually need?

Not everyone should optimize.

Not everyone should automate everything.

HEOMF helps make that decision.

Final thought

The goal is not to:

• chase every price signal
• build the most complex system
• optimize everything manually

The goal is:

To achieve the desired outcome with minimal effort and maximum robustness.

Or simply:

The best optimization is the one you don’t have to do.