Towards a Sustainable Microgrid on Alderney Island Using a Python-based Energy Planning Tool

TL;DR

This paper presents a Python-based energy planning tool for designing sustainable microgrids on Alderney Island, incorporating stochastic modeling, linearized power flow, and scenario analysis to optimize investments in renewable energy and storage.

Contribution

It introduces a novel open-source Python tool, PyEPLAN, that integrates stochastic optimization, linearized power flow, and scenario generation for sustainable microgrid design in remote areas.

Findings

Optimized investment in batteries, solar, and wind units.

Effective scenario generation capturing load and renewable variability.

Demonstrated applicability on Alderney Island with promising results.

Abstract

In remote or islanded communities, the use of microgrids (MGs) is necessary to ensure electrification and resilience of supply. However, even in small-scale systems, it is computationally and mathematically challenging to design low-cost, optimal, sustainable solutions taking into consideration all the uncertainties of load demands and power generations from renewable energy sources (RESs). This paper uses the open-source Python-based Energy Planning (PyEPLAN) tool, developed for the design of sustainable MGs in remote areas, on the Alderney island, the 3$^{rd}$ largest of the Channel Islands with a population of about 2000 people. A two-stage stochastic model is used to optimally invest in battery storage, solar power, and wind power units. Moreover, the AC power flow equations are modelled by a linearised version of the DistFlow model in PyEPLAN, where the investment variables are here-and-now decisions and not a function of uncertain parameters while the operation variables are wait-and-see decisions and a function of uncertain parameters. The $k$-means clustering technique is used to generate a set of best (risk-seeker), nominal (risk-neutral), and worst (risk-averse) scenarios capturing the uncertainty spectrum using the yearly historical patterns of load demands and solar/wind power generations. The proposed investment planning tool is a mixed-integer linear programming (MILP) model and is coded with Pyomo in PyEPLAN.