Novel Predictive Fuzzy Logic-Based Energy Management System for Grid-connected and Off-grid Operation of Residential Smart Micro-grids

IEEE In this paper, a novel energy management system with two operating horizons is proposed for a residential micro-grid application. The micro-grid utilises the energies of a photovoltaic (PV), a fuel cell and a battery bank to supply the local loads through a combination of electric and magnetic buses. The proposed micro-grid operates in a large number of grid-connected and off-grid operation modes. The energy management system includes a long-term data prediction unit based on a 2D dynamic programming and a short-term fuzzy controller. The long-term prediction unit is designed to determine the appropriate variation range of the battery state of charge and fuel cell state of hydrogen. The efficiency performance of the micro-grid components, predicted energy generation and demand, energy cost and the system constraints are taken into account. The resultant data then is sent to the short-term fuzzy controller which determines the operation mode of the micro-grid based on the real-time condition of the micro-grid elements. A prototype of the proposed micro-grid including the energy management system is developed, and experimental tests are conducted for three different energy management scenarios. The proposed management technique is validated through energy distribution and cost analysis

Robust frequency regulation in mobile microgrids: HIL implementation

© 2007-2012 IEEE. It is undeniable that marine vessel systems play an important role to transfer huge loads and weapons with low cost. However, ship power systems produce a lot of greenhouse gases, which in turn lead to serious environmental pollution. Hence, the utilizing of wind turbines (WTs), solar generation, sea wave energy (SWE), and energy storage systems (ESSs) in marine vessel power systems have been attracting a lot of attention in recent years. In this paper, it is assumed that a marine vessel power system with photovoltaic (PV), WT, SWE, and ESS can be regarded as a mobile-islanded MG. Then, a novel topology for hybrid shipboard microgrids (MGs) is presented. Next, in order to make a balance between consumption and power generation in shipboard MGs, an optimal modified model-free nonlinear sliding mode controller is introduced for the secondary load frequency control. Since the quality of the control actions of the proposed model-free approach depends on its parameters, a hybrid version of the sine-cosine algorithm (SCA) and wavelet-mutation (WM), called SCAWM, is employed to find the best value of these coefficients. Comparisons are conducted with other existing methodologies, such as model predictive control, interval type-2 fuzzy logic controller, and conventional PI (PI) to establish the supremacy of the newly suggested control strategy. Finally, a real-time hardware-in-the-loop (HIL) simulation based on OPAL-RT is accomplished to affirm the applicability of the suggested controller, from a systemic perspective, for the load frequency control problem in the shipboard MG

An optimal approach for load-frequency control of islanded microgrids based on nonlinear model

Due to the increased environmental and economic challenges, in recent years, renewable based distribution generation has been developed. More penetrations from the side of consumers caused a new concept called microgrids which are able to stand with or without connection to the bulk power system. Control of microgrids in islanded mode is very crucial for decreasing the amplitude of frequency deviations as well as damping speed. This chapter aims to propose an optimal combination of FOPD and fuzzy pre-compensated FOPI approach for load-frequency control of microgrids in islanded mode. The optimization parameter of the control scheme is designed by the differential evolution (DE) algorithm which has been improved by a fuzzy approach. In the optimization, control effort is considered as a constraint. Due to the robustness and flexibility of the proposed method, the simulation results have been improved substantially. Robust performance of the proposed control method is examined through sensitivity analysis.fi=vertaisarvioitu|en=peerReviewed