Optimal control of a DC microgrid with renewable energy sources : doctoral dissertation - Gulin, Marko (M. Gulin; Fakultet elektrotehnike i računarstva, 2019)
A microgrid is a cluster of distributed generation sources, storage devices and loads that operate together. Ideally, they improve reliability and quality of the power supply and the power system. Microgrids are expected to significantly reduce power transmission losses and enable integration of a large share of renewable energy sources. This doctoral dissertation primarily develops control algorithms for residential grid-connected DC microgrids with renewable energy sources to improve stability and optimize techno-economic microgrid operation. To that aim, the dissertation addresses several important aspects: (i) modeling of all systems included in the microgrid; (ii) prediction of day-ahead production and consumption in a microgrid by means of machine learning and artificial intelligence, which is especially challenging due to strong correlation of predicted variables with atmospheric conditions; (iii) power flow management based on convex optimization and the model predictive control scheme with receding horizon principle, which is developed in a stochastic framework to account for production and consumption prediction uncertainty; and (iv) DC link (bus) voltage control with power flow reference tracking, whereas optimal power references are generated by the power flow management control loop. Control (decision) variables in the system under consideration are charge and discharge profiles for local energy storage devices, that serve as an energy buffer which improves system stability and enables joining a decentralized electricity market. In the simplest scenario, a microgrid would buy electricity during low electricity price intervals and sell during high price intervals, which reduces microgrid operating costs. However, the decision of when to buy and sell energy to the utility grid and in which amount, i.e., when to charge and discharge storage devices, is a complex function of the predicted microgrid load, power production, current storage device state of charge, and of the predicted electricity price provided by the electricity market. Together, this is the central subject of the conducted research. Note that all concepts developed in this dissertation can also be applied to AC microgrids. The residential DC microgrid under consideration was constructed at the Laboratory for Renewable Energy Systems (LARES) at the University of Zagreb Faculty of Electrical Engineering and Computing (UNIZG-FER), Croatia. The developed concepts are verified by experiments in LARES based on real meteorological and electricity price data, i.e., they are tested in a real operating environment. Predictions of relevant meteorological variables, which are used as inputs to predict day-ahead production and consumption in the microgrid, are provided by the Croatian Meteorological and Hydrological Service (DHMZ). Keywords: dc microgrid, smart grid, renewable energy systems, distributed storage, photovoltaic system, machine learning, artificial intelligence, load prediction, battery state of charge estimation, Kalman filter, ultracapacitor, fuel cell, power flowmanagement, convex optimization, linear program, model predictive control, stochastic control framework, voltage stability