Solar Power Plant Battery Storage Simulator
Our client is an industrial engineering and construction company based in Europe that delivers complex infrastructure projects for the oil & gas, petrochemical, and energy industries. In addition to large-scale industrial and infrastructure projects, the company participates in the construction and operation of solar and other alternative energy facilities.
Having committed to fixed energy delivery contracts with the national grid, they faced a recurring financial dilemma: should they invest in battery storage systems to buffer production variability - and if so, at what capacity?
To answer this question with data rather than guesswork, they engaged Four Ages to build a purpose-built economic simulator.
Client location
Europe
Industry
Energy
Duration
Ongoing
Team
3 members
Challenge
Solar power plant operators in the client’s energy market must commit in advance to an hourly generation plan for the following day. If actual output falls short of the committed volume, the operator is obligated to purchase the shortfall on the open market — typically at a significant premium. If output exceeds the plan, the surplus energy cannot be delivered and is simply lost, since there is no mechanism to store it without dedicated hardware.
Battery storage systems (in this case, Potis Edge units) offer a solution to both problems: charge during overproduction, discharge during underproduction. However, each battery unit carries a capital cost comparable to an individual solar panel, and every panel requires its own dedicated unit. With capacity options varying widely, the investment decision is non-trivial and highly sensitive to production patterns, market prices, and contract parameters.
To model these tradeoffs, the decision was made to invest in a simulator that could run scenarios, compare outcomes, and give decision-makers a defensible answer.
Solution
Four Ages built a web-based simulation platform that models the full economic lifecycle of a battery investment at a solar plant, taking into account production variability, market pricing, contract obligations, and battery characteristics.
Production vs. Commitment Modelling
At the heart of the simulator is a time-series model that ingests historical and forecast generation data and compares it against the contracted delivery schedule on an hourly basis.
For each hour, the model calculates whether the plant is in surplus or deficit, and what the financial consequence would be under each scenario — with or without battery storage.
Battery Economics Engine
The simulator models battery behaviour across a configurable set of parameters: unit cost, capacity (kWh), charge/discharge efficiency, and degradation over time.
It calculates State of Charge (SoC) hour by hour, determines how much surplus can be stored and how much deficit can be covered, and translates those outcomes into avoided purchase costs and recovered generation revenue.
Extended Platform Functionality
While the Potis Edge hardware includes base-level monitoring and management software, the Four Ages team built custom functionality on top of it — adding simulation logic, financial modelling layers, and scenario tooling that go beyond what the manufacturer provides out of the box.
This gives the client capabilities specifically designed around their contractual and market context.
Impact
The simulator gives the client's decision-makers a structured, data-driven framework for evaluating battery investments that previously did not exist.
Rather than relying on intuition or one-off spreadsheet analysis, operators can now run parameterised scenarios, test assumptions, and produce outputs that directly support capital allocation decisions.
The platform is under active development, with further simulation capabilities and deeper integration with live generation data planned as the next phase.
Responsibilities
Business requirements analysis and financial modelling design
Backend architecture and simulation engine development
Battery economics modelling (SoC, efficiency, degradation, cost)
Scenario configuration and comparison interface
Integration with Potis Edge hardware platform
Custom feature development beyond manufacturer baseline
Ongoing iteration and feature expansion
Technologies
Grafana
PostgreSQL
Potis Edge Integration
REST APIs
Time-Series Analysis
Author:
Maria Roy
