Marko Gulin

Analysis of microgrid power flow optimization with consideration of residual storages state

Microgrid is a cluster of distributed generation sources, storages and loads that cooperate so as to improve the reliability and quality of the local power supply and of the power system. In this paper we present a power flow optimization of a DC microgrid that consists of photovoltaic array, batteries stack and fuel cells stack with electrolyser, and is connected to the grid via bidirectional power converter. The optimization problem aims to minimize microgrid operating costs and is formulated using a linear program that takes into account the storages charge and discharge efficiency, and considers the residual state of the energy storage systems in the criterion function. Performance of the proposed approach is verified through year-scale simulations based on the actual meteorological, electricity price and consumption data. The analysis performed points out that especially for short prediction horizons it is very important to ensure proper penalization of the residual storages state in the criterion function in order to yield optimum revenue from microgrid operation.

Stochastic model predictive control for optimal economic operation of a residential DC microgrid

In this paper we present power flow optimization of a residential DC microgrid that consists of photovoltaic array, batteries stack and fuel cells stack with electrolyser, and is connected to the grid via bidirectional power converter. The optimization problem aims to minimize microgrid operating costs and is formulated using a linear program that takes into account the storages charge and discharge efficiency. To account for power predictions uncertainty, optimization problem is defined in a stochastic framework by using chance constraints. Since we assume that the error in realization of power predictions will be compensated by utility grid, chance constraints are defined for power exchange between the microgrid and the utility grid. Finally, we investigate a stochastic model predictive control for the closed-loop power management in the microgrid. Performance verification of the proposed approach is performed on simulations for two-month period.

Load forecast of a university building for application in microgrid power flow optimization

Microgrid is defined as a cluster of distributed generation sources, storages and loads that cooperate together in order to improve power supply reliability and overall power system stability. Short-term power production and load profile prediction is very important for power flow optimization in a microgrid, thus enhancing the management of distributed generation sources and storages in order to improve the microgrid reliability, as well as the economics of energy trade with electricity markets. However, short-term load prediction is a complex procedure, mainly because of the highly nonsmooth and nonlinear behaviour of the load time series. In this paper we develop and verify a neural-network-based short-term load profile prediction model. Neural network inputs are lagged load data, as well as meteorological and time data, while neural network output is load at the particular moment. Neural network training and validation is performed on load data recorded at University of Zagreb Faculty of Electrical Engineering and Computing, and on meteorological data obtained from Meteorological and Hydrological Service of Croatia, in period 2011-2013.

Dynamical behaviour analysis of a DC microgrid in distributed and centralized voltage control configurations

In majority of cases residential microgrids are constituted of renewable energy sources, energy storage systems, and of power converters representing control points that by proper operation assure overall system stability and quality of power supply. In this paper we present simulation based analysis of dynamical behaviour of a residential DC microgrid laboratory setup in distributed and centralized voltage control configurations. It is shown that these control configurations have several inherent limitations, like overload of microgrid components during rapid load changes which can affect components lifetime. In order to overcome such limitations, the cause of such behaviour is assessed and control concepts to overcome that are proposed. Microgrid is simulated on electrical level using equivalent electrical models of all components involved: photovoltaic array, electrochemical batteries, fuel cells stack and power converters.

Field-oriented control of an induction machine with winding asymmetries

Modern electrical AC drives with best available performances are based on the so-called fundamental wave machine model approaches. This paper introduces an upgrade of fundamental wave model approach by respecting both inherent and fault-induced deviations of machine flux from its fundamental component. A field-oriented control scheme for an asymmetric induction machine is presented. The algorithm is based on observing newly introduced flux-angle-based variations in the transient leakage inductance due to the asymmetry. A simple extension of the conventional rotor field-oriented control structure is proposed that takes into account detected variations and improves machine performance in the asymmetry conditions. Detection and characterization of newly formed modulation in transient leakage inductance are performed by employing an unscented Kalman filter. Simulation results for the case of a 5.5 kW induction machine are presented.

Multi-level optimal control of a microgrid-supplied cooling system in a building

Recent studies highlighted the model predictive control as a promising platform for complex systems management and energy efficiency improvement in a large number of applications, particularly prominent in building climate and smart grid control. Involvement of microgrids offered great possibilities of power consumption peak shaving, improved grid stability and decentralisation, by introducing buildings as active market participants. To this aim, the paper is focused on the development of a microgrid optimal control that acts as an intermediary method for integration of a building to the smart grid with dynamic pricing of electricity. A multilevel optimal control is applied on a building cooling system and energy flow optimization of a DC microgrid that consists of a photovoltaic array, batteries stack and fuel cells stack with electrolyser, all connected to the utility grid via a bidirectional power converter. Performance of the proposed approach is verified through 4-month simulations of a microgrid integrated with a building cooling system, in actual meteorological and electricity price data scenario. Microgrid energy storages and the proposed control method fully exploit possibilities of dynamic pricing and greatly reduce cooling system operation costs while ensuring the high level of user comfort.

Predictor-corrector method for weather forecast improvement using local measurements

Weather forecast is a crucial input for prediction of local building consumption and power production profiles in the buildings microgrid. E.g., prediction of solar irradiance components and air temperature is used to predict photovoltaic array power production, while air temperature and humidity are often used to predict building consumption during the day. Due to the computation complexity of meteorological models, new prediction sequence becomes available every 6 h at best, and often comes with a nearly 4 h lag. In this paper we develop a linear and nonlinear corrector models to improve weather forecast by using local measurements only. The main motivation behind this approach is to correct prediction sequence by using local measurements as they become available, i.e. prediction sequence is refreshed every 1 h instead of every 6 h. The proposed approach is validated on historical air temperature prediction sequences and actual measurements during 6 months period.