About Storage Modelling#
Storage modeling is complex because storage systems behave differently than generation or demand. Storages move energy across time, not just produce or consume it. This means:
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🔁 1. Storage shifts energy in time
Unlike solar panels (generate) or homes (consume), storage stores energy when it’s abundant and releases it when needed. That means:
You can't look at one hour in isolation — what happened before matters.
The state of charge (SOC) today depends on all previous hours.
Analogy: It's like a savings account — what you can spend today depends on how much you saved earlier.
⏳ 2. Time resolution matters
Should you model every single hour? Every day? A few representative days? Each choice impacts:
Accuracy (high resolution = realistic, but slow to compute)
Computational effort (simplified models lose detail)
If you model storage with only a few sample days, you risk missing important patterns, like a long wind lull or weekend charging habits.
🔗 3. Storage needs continuity
Many simplification techniques (e.g. clustering days) break the continuity of storage:
The model might forget how full the battery was yesterday.
Artificial resets can distort when charging/discharging actually happens.
This makes it tricky to model seasonal storage or multi-day balancing.
⚡ 4. Storage interacts with everything Storage isn’t just an isolated battery:
It interacts with renewables (absorbing variability)
It supports grid reliability (fast response)
It responds to prices, constraints, and emissions goals
So modeling it well requires integrating with the rest of the energy system.
state of the art energy storage algorithms:#
Niet (Direct Link) — “Continuous Diary”#
Analogy: Imagine someone writes in their diary every hour without skipping, and each day picks up exactly where the last one left off. The emotional ups and downs (mood swings) are smooth and continuous over the week.
Key idea: Full hourly resolution; perfect continuity over time.
Use case: Most accurate but data-heavy.
Welsch (Fixed-Pattern) — “Photocopied Day”#
Analogy: This person writes down one typical day of feelings and then photocopies that same page for each day of the week. So Monday, Tuesday, etc., look exactly the same.
Key idea: Same daily pattern repeated; no inter-day variation.
Use case: Fast and simple but ignores variation between days.
Kotzur (Unverified Cluster) — “Alternating Diaries”#
Analogy: This person has two types of days — maybe “busy” and “lazy” days — and alternates between those two. They use pre-written diary templates for each. But they don’t bother connecting one day’s feelings to the next, so it can feel jumpy or inconsistent.
Key idea: Captures daily diversity using clusters, but no continuity.
Use case: Captures more variety than Welsch but lacks smooth transitions.
Novo (Hourly-Verified) — “Linked Journaling”#
Analogy: This person still uses simplified mood templates for different kinds of days, but they always make sure each day starts where the previous one left off. So it feels realistic and connected, even if it's not a full diary.
Key idea: Combines typical days with continuity between hours.
Use case: A practical middle ground between realism and simplicity.