Model Predictive Control Strategies for Low-Voltage Microgrids
Modern concepts for grids are being developed owing to the increasing stress put on the electrical producers and the burgeoning need for thermal demands due to varying climate conditions. The increasing awareness of the depletion of natural resources has also been a driver in the direction of modern grids which are more efficient and less susceptible to failures. One solution which is being considered as an alternative for the existing centralised electrical grid and thermal needs is the concept of microgrid. Microgrids are decentralised subunits which consist of different cogeneration units and combined heat and power units along with renewable sources and heat and electricity consumers. Multiple microgrids combine together to aid and complement the existing producers thus, reducing the stress on these producers while increasing the efficiency of the entire electricity grid. The presence of different storage devices and electricity production units mean that the microgrids can also maintain very high quality of electricity, thus reducing losses during transmission and distribution. Another advantage of a microgrid is it's ability of being self-sufficient for a short period time which means that microgrids have the ability to island themselves voluntarily or accidentally from the rest of the electricity grid which could be used to prevent entire grid failures during meteorological problems or when the economic factors are not conducive. The presence of decentralised sources, storage devices and consumers mean that there is a strong need for control of all the different components that constitute a microgrid. There are many different approaches to control a microgrid. This paper attempts to demonstrate a model predictive control (MPC) strategy developed to provide strategies for a low-voltage microgrid with both thermal and electrical devices and requirements. The aim of the strategy is to satisfy the demands and comforts of the consumers while minimising the costs associated by performing a multi-layered mixed-integer optimisation which provides strategy that needs to be employed by each unit of the system. The optimisation consists of two layers, the first of which provides the setpoints for both the thermal and electrical components and the second layer solves a problem which is only takes the electrical units into account and is performed over a shorter timestep. This is owing to the different dynamics of the thermal and electrical units. The fast dynamics of the electrical systems requires us to perform the second layer to maintain the efficiency of the microgrid. The paper then shows the importance of optimal sizing for the model predictive control strategy employed for varying periods of the year and the advantages of this strategy for microgrids over the existing strategies. The paper also attempts to show the impact of certain variables employed in the MPC strategy in particular, for LV (Low-Voltage) microgrid.