Dimensioning of a PV-battery system

Relevance

The global energy transition is driving rapid adoption of renewable energy technologies, with solar photovoltaics (PV) playing a central role. However, deploying PV in residential and commercial contexts is not simply a matter of maximizing installed capacity. System design requires careful consideration of site-specific constraints, such as roof geometry, shading, inverter sizing, and regulatory requirements. At the same time, economic viability must be ensured through cost modeling, investment analysis, and performance forecasting. Batteries add another dimension to system design, offering opportunities for load shifting and grid independence, but also introducing new costs and constraints.

This course addresses these challenges by combining technical modeling of PV and battery systems with economic evaluation tools. Students will learn to work with irradiance and load data, simulate solar production, model power flows with and without storage, and assess energy bills under different tariff structures. The course also introduces investment analysis techniques such as Net Present Value (NPV), Internal Rate of Return (IRR), and payback time. By integrating these aspects into a single computational framework, participants gain the skills to optimize PV–battery systems for both technical feasibility and economic performance.

Abstract

This course provides a comprehensive introduction to the modeling, simulation, and economic evaluation of solar photovoltaic (PV) and battery systems. Participants will develop a Python-based framework that integrates multiple components of system design: irradiance modeling, solar energy production, load interaction, power flow simulation, and cost analysis. The technical modules cover the calculation of tilted irradiance, solar production per panel, constraints on installation (roof geometry, inverter sizing, grid export limits), and battery charge–discharge dynamics. On the economic side, students will analyze energy bills under static and dynamic tariff structures and assess investment feasibility using discounted cash flow methods.

A key element of the course is the exploration of constraints that ensure realistic and feasible designs, including geometric, electrical, economic, and regulatory limitations. Optimization methods are then applied to identify configurations that maximize economic performance (e.g., IRR or NPV) while remaining technically valid. The course culminates in an integrated framework that allows participants to simulate multiple scenarios, evaluate trade-offs, and recommend optimal system designs for given contexts.

This hands-on approach equips participants with both the theoretical background and practical coding skills to evaluate PV–battery investments, bridging the gap between engineering, economics, and policy.