Planning of Multi-Microgrids Considering Uncertainties and Spatial Characteristics

Abstract

Global warming is a serious issue that is being tackled from various fronts, one of them is the decarbonization of electrical energy systems, which may be addressed by introducing clean Distributed Energy Sources (DERs) such as Renewable Energy Resources (RESs) and Energy Storage Systems (ESSs). These types of technologies can be clustered to form Microgrids (MGs), which have proven to be technically and financially feasible solutions to supply electricity demand while reducing emissions and increasing resiliency. MGs can operate isolated or connected to the grid, both in rural and urban settings, which allows them to interact with the existing electricity grid to enhance its capabilities and functionalities, while improving power quality, reducing network congestion, increasing efficiency, reliability, and flexibility, and delaying investments in transmission and distribution systems. Hence, this thesis focuses on various relevant and timely aspects of MG planning, in particular for isolated Remote Communities (RCs) and for the interconnection of MGs and their integration with Active Distribution Networks (ADNs) to form Multi-Microgrid (MMG) systems.

The deployment of clean MGs to satisfy RC electricity needs, considering their inherent geographic characteristics, imposes a series of challenges that must be taken into account when planning them. Thus, delivering electricity to RCs is economically and environmentally expensive, as the main source of electricity is diesel generators, which present significant Greenhouse Gas (GHG) emissions, and Operations and Maintenance (O\&M), transportation, and fuel costs. Therefore, an optimization model for the long-term planning of RC MGs to introduce RESs and ESSs is proposed in this thesis, with the objective of reducing costs and emissions. The presented model considers lithium-ion batteries and hydrogen systems as part of ESSs technologies. The model is used to investigate the feasibility of integrating these DERs in an MG in Sanikiluaq, an RC in the Nunavut territory in Northern Canada, where several planning scenarios with various combinations of resources are considered in order to assess the impact of different technologies. The results show that wind resources along with solar and storage technologies can play a key role in satisfying RC electricity demand, while significantly reducing costs and GHG emissions.

Independent MGs can be interconnected to form MMG systems in the context of ADNs, bringing valuable benefits such as energy use, power quality and stability improvements, as well as flexibility and thus economic enhancements for both costumers and utilities. Therefore, a Two Stage Stochastic Programming (TSSP) model is proposed for the planning of MMGs within ADNs at Medium Voltage (MV) levels to minimize the total costs, while benefiting from interconnections of MGs and considering uncertainties associated with electricity demand and RESs. Furthermore, the model includes long-term purchase decisions and short-term operational constraints, using Geographical Information Systems (GIS) to realistically estimate rooftop solar limits. The planning model is used to study the feasibility of implementing an MMG system consisting of 4 individual MGs at an ADN in a municipality in the state of São Paulo, Brazil. The results show that the TSSP model tends to be less conservative than the deterministic model, which is based on simple and pessimistic reserve constraints, while being computationally more efficient than the usual, Stochastic Linear Programming (SLP) and Monte Carlo Simulations (MCS) approaches, with adequate accuracy.

Finally, the MMG planning model at MV is further extended to include the Low Voltage (LV) grid. Thus, a model is proposed for the realistic planning of MMGs in the context of ADNs, with the assistance of GIS. The model considers the distribution system grid with an adequate level of detail for multi-year planning as well as the geographic features of the studied region. Similar to the MV model, it also includes long-term purchase decisions and short-term operational constraints, and considers uncertainties associated with electricity demand and RESs using a TSSP approach. GIS along with Deep Learning (DL) are used to more accurately estimate the rooftop areas within the studied region for solar PV deployment, as well as for modelling the LV grid. The planning model is then used to study in more detail the feasibility of implementing the MMG system previously considered in São Paulo, Brazil. The results of the extended TSSP LV grid model are compared with the results obtained using MCS and the less detailed TSSP MV grid model, demonstrating that both TSSP solutions are close to those obtained with MCS at a lower computational cost, while providing accurate and practical planning results.