This item is available under a Creative Commons License for non-commercial use only
Electrical and electronic engineering
To achieve the goal of decarbonising the electric grid by 2050 and empowering energy citizen, this research focuses on the development of Microgrid (μGrid) systems in Irish environment. As part of the research work, an energy efficient and cost effective solution for μGrid, termed Community-μGrid (C-μGrid) is proposed. Here the users can modify their micro-Generation (μGen) converters to facilitate a single inverter in a C-μGrid structure. The new system could allow: (i) technological advantage of improved Power Quality (PQ); (ii) economic advantage of reduced cost of energy (COE) to achieve sustainability. Analysis of scenarios of C-μGrid (AC) systems is performed for a virtual community in Dublin, Ireland. It consists of (10 to 50) similar type of residential houses and assumes that each house has a wind-based μGen system. It is found that, compared to individual off-grid μGen systems, an off-grid C-μGrid can reduce upto 35% of energy storage capacity. Thus it helps to reduce the COE from €0.22/kWh to 0.16/kWh. In grid connected mode, it can sell excess energy to the grid and thus COE further decreases to €0.11/kWh. Thus a cost-effective C-μGrid is achieved.
The proposed system can advance its energy management efficiency through implementation of Demand Side Management (DSM) technique. For the test case, 50% of energy storage capacity could be avoided through DSM technique. It also helps to further decrease the COE by 25%. The C-μGrid system with storage is optimised by implementing the Economic Model Predictive Control (EMPC) approach operating at the pricing level. Emphasis is given to the operational constraints related to the battery lifetime, so that the maintenance and replacement cost would be reduced. This technique could help to improve the battery performance with optimised storage and also reduces the COE of the system by 25%.
Mariam, L. (2018) Modelling of an Intelligent Microgrid System in a Smart Grid Network. Doctoral thesis, DIT, 2018.