A preview of the PDF is not available
An optimal home energy management scheme considering grid connected microgrids with day-ahead weather forecasting using artificial neural network
Abstract and Figures
Demand response (DR) strategy provides an opportunity to electricity consumers to participate in making power system reliable by managing their electricity consumption. Due to increasing population, a lot of energy is consumed in the residential sector. Therefore, in this thesis, we propose an optimal scheme to systematically manage the energy consumption in residential area. Electricity cost and peak to average ratio reduction are the main goals of this study. Furthermore, reduction in imported electricity from the external grid is also the objective of this study. The proposed scheme schedules smart appliances and electrical vehicle (EV) charging/discharging optimally according to the consumers’ preferences. Each consumer has its own grid-connected microgrid for electricity generation; which consists of wind turbine, solar panel, micro gas turbine and energy storage system (ESS). On the other hand, a real time forecasting of wind speed and temperature has been performed using an artificial neural network for efficient energy management. Furthermore, the scheduling problem is mathematically formulated and solved by mixed integer linear programming (MILP). We also provide the comparison of the optimal solutions, while considering EV with and without discharging capabilities. Findings from simulations affirm our proposed scheme in terms of above-mentioned objectives. After solving home energy management and EV charging/discharging problem using MILP, power trading problem is considered in this thesis to earn maximum profit and proper utilization of renewable energy sources (RES). This problem is solved using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. In this case, the microgrid also consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, ESS is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization. Furthermore, a comparative analysis is also performed to show the ixlegitimacy and productiveness of CSA and SA
Figures - uploaded by Sheraz Aslam
All figure content in this area was uploaded by Sheraz Aslam
Content may be subject to copyright.