Model predictive control (MPC) facilitates online optimal resource scheduling in electrical networks, thermal systems, water networks, process industry to name a few. In electrical systems, the capability of MPC can be used not only to minimise operating costs but also to improve renewable energy utilisation and energy storage system degradation. This work assesses the application of MPC for energy management in an islanded microgrid with PV generation and hybrid storage system composed of battery, supercapacitor and regenerative fuel cell. The objective is to improve the utilisation of renewable generation, the operational efficiency of the microgrid and the reduction in rate of degradation of storage systems. The improvements in energy scheduling, achieved with MPC, are highlighted through comparison with a heuristic based method, like Fuzzy inference. Simulated behaviour of an islanded microgrid with the MPC and fuzzy based energy management schemes will be studied for the same. Apart from this, the study also carries out an analysis of the computational demand resulting from the use of MPC in the energy management stage. It is concluded that, compared to heuristic methods, MPC ensures improved performance in an islanded microgrid. INDEX TERMS Energy management, model predictive control, fuzzy systems, energy storage systems, degradation reduction, islanded microgrid.
Energy storage systems (ESS) are being considered to overcome issues in modern grids, caused by increasing penetration of renewable generation. Nevertheless, integration of ESS should also be supplemented with an optimal energy management framework to ensure maximum benefits from ESS. Conventional energy management of battery, used with PV system, maximises self-consumption but does not mitigate grid congestion or address battery degradation. Model predictive control (MPC) can alleviate congestion and degradation while ensuring maximum self-consumption. Studies will be carried out to highlight the improvement with MPC based energy management over conventional method using simulations of oneyear system behaviour. As MPC uses forecast information in decision making, the impact of forecast uncertainties will be assessed and a method to address them through a constraint tightening will be presented.
Increasing integration of photovoltaic (PV) system in electric grids cause congestion during peak power feed-in. Battery storage in PV systems increases self-consumption, for consumer's benefit. However with conventional maximising self consumption (MSC) control for battery scheduling, the issue of grid congestion is not addressed. The batteries tend to be fully charged early in the day and peak power is still fed-in to grid. This also increases battery degradation due to increased dwell time at high state of charge (SOC) levels. To address this issue, this work uses a model predictive control (MPC) for scheduling in PV system with battery storage to achieve multiple objectives of minimising battery degradation, grid congestion, while maximising self consumption. In order to demonstrate the improvement, this work compares the performances of MPC and MSC schemes when used in battery scheduling. The improvement is quantified through performance indices like self consumption ratio, peak power reduction and battery capacity fade for one-year operation. An analysis on computation burden and maximum deterioration in MPC performance under prediction error is also carried out. It is concluded that, compared to MSC, MPC achieves similar self consumption in PV systems while also reducing grid congestion and battery degradation.
Wide bandgap (WBG) power electronic devices realized using silicon carbide (SiC) and gallium nitride (GaN) are increasingly replacing their silicon (Si) counterparts in power electronics applications. The obvious advantages of these devices with their higher switching speeds, lower on state resistance and high temperature operation over Si devices have aided in the paradigm shift towards wide bandgap devices. The low gate charge requirements of SiC MOSFETs enables use of these devices in radio frequency (RF) converters using resonant topologies operating at MHz frequency range. The RF converters employed in various industrial applications are currently realized with vacuum tubes. Replacing vacuum tubes with solid state devices provides greater reliability. This requires power switches transferring high power at high switching speeds. Wide bandgap devices operating at these specifications are not commercially available and power modules have to be custom designed for these applications. This work demonstrates performance of various commercial MOSFET packages at frequency of 2.56 MHz. Commercial SiC MOSFETs in TO-247 and D2Pak packs are tested in Class E resonant converter operating at 2.56 MHz and compared with DE-275 Radio Frequency (RF) package performance under same operating conditions. Design considerations deduced from results can then be used in design of custom low voltage SiC RF modules and eventually can be used in the design of high voltage modules.
Non-deterministic generation from renewable sources have resulted in the incorporation energy storage systems in modern grids. Management of energy between different storage elements need to done optimally to ensure efficient operation of the grid. The intraday energy management problem is addressed in this work through an online model predictive control using multi objective optimisation. This work analyses the energy interaction among different storages when penalty weights in a multi objective optimisation problem is varied, in order to find an optimal scenario in terms of weight distribution. Different scenarios are identified and performance indices are proposed to achieve the same. The work also addresses implicitly the objective of minimising rate of degradation batteries. Simulation results are presented to aid in the analysis.
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