2020/07/29 by Shahab Dehghan, Dehghan, Shahab, Agnes M. Nakiganda +5
Energy · Engineering · #Hybrid Renewable Energy Systems #Integrated Energy Systems Optimization #Smart Grid Energy Management
paper · pdf · doi:10.48550/arxiv.2007.15165
In remote or islanded communities, the use of microgrids (MGs) is necessary\nto ensure electrification and resilience of supply. However, even in\nsmall-scale systems, it is computationally and mathematically challenging to\ndesign low-cost, optimal, sustainable solutions taking into consideration all\nthe uncertainties of load demands and power generations from renewable energy\nsources (RESs). This paper uses the open-source Python-based Energy Planning\n(PyEPLAN) tool, developed for the design of sustainable MGs in remote areas, on\nthe Alderney island, the 3rd largest of the Channel Islands with a\npopulation of about 2000 people. A two-stage stochastic model is used to\noptimally invest in battery storage, solar power, and wind power units.\nMoreover, the AC power flow equations are modelled by a linearised version of\nthe DistFlow model in PyEPLAN, where the investment variables are here-and-now\ndecisions and not a function of uncertain parameters while the operation\nvariables are wait-and-see decisions and a function of uncertain parameters.\nThe k-means clustering technique is used to generate a set of best\n(risk-seeker), nominal (risk-neutral), and worst (risk-averse) scenarios\ncapturing the uncertainty spectrum using the yearly historical patterns of load\ndemands and solar/wind power generations. The proposed investment planning tool\nis a mixed-integer linear programming (MILP) model and is coded with Pyomo in\nPyEPLAN.\n