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

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 it here-and-now decisions and not a function of uncertain parameters while the operation variables are it 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.

Publication
Proc. of the 2020 MEDPOWER
Shahab Dehghan
Shahab Dehghan
Postdoctoral Researcher (Imperial College London)
Agnes Marjorie Nakiganda
Agnes Marjorie Nakiganda
PhD Candidate (UoL)

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