Search Results (2)

Automation and modernization of the grid with the availability of micro-grids including non-conventional sources of energy are the main constituent of smart grid technology. Most energy demand is fulfilled by fossil fuel-based power plants. Inadequacy of fuel resources, higher operating costs, and ever-increasing carbon emissions are the primary constraints of fossil fuels-operated power plants. Sustainable energy resource utilization in meeting energy demand is thought to be a preferred solution for reducing carbon emissions and is also a sustainable economic solution. This research effort discusses an accurate mathematical modeling and simulation implementation of a sustainable energy resource model powered by solar, grid, and proton exchange membrane fuel cell (PEMFC) stack and focuses on the energy management of the model. In the proposed model, despite energy resources being sustainable, consumer side sustainability is achieved by using electrical charging vehicles (ECVs) to be integrated with sustainable resources. The proposed energy resource management (ERM) strategy is evaluated by simulating different operating conditions with and without distributed energy resources exhibiting the effectiveness of the proposed model. PEMFC is incorporated in the model to control fluctuations that have been synchronized with other energy resources for the distribution feeder line. In this proposed model, PEMFC is synchronized with grid and solar energy sources for both DC and AC load with ERM of all sources, making the system effective and reliable for consumer-based load and ECVs utilization. Full article

In this paper three metaheuristics are used to solve a smart grid multi-objective energy management problem with conflictive design: how to maximize profits and minimize carbon dioxide (CO₂) emissions, and the results compared. The metaheuristics implemented are: weighted particle swarm optimization (W-PSO), multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II). The performance of these methods with the use of multi-dimensional signaling is also compared with this technique, which has previously been shown to boost metaheuristics performance for single-objective problems. Hence, multi-dimensional signaling is adapted and implemented here for the proposed multi-objective problem. In addition, parallel computing is used to mitigate the methods’ computational execution time. To validate the proposed techniques, a realistic case study for a chosen area of the northern region of Portugal is considered, namely part of Vila Real distribution grid (233-bus). It is assumed that this grid is managed by an energy aggregator entity, with reasonable amount of electric vehicles (EVs), several distributed generation (DG), customers with demand response (DR) contracts and energy storage systems (ESS). The considered case study characteristics took into account several reported research works with projections for 2020 and 2050. The findings strongly suggest that the signaling method clearly improves the results and the Pareto front region quality. Full article