Distributed Generation In Smart Grid Research Articles

Vulnerability assessment and defence strategy to site distributed generation in smart grid

False data injection (FDI) attacks tamper with the state estimation data and can pose significant threats to the smart grid. The vulnerability analysis and defence strategies may help to mitigate the impact of these attacks. However, existing research efforts have not addressed the computational power and accuracy issues in the vulnerability analysis and defence mechanisms using realistic test environments. In this work, the authors present a novel low-complexity FDI attacks model to perform the vulnerability analysis. The authors develop a reduced-row-echelon-form-based greedy algorithm using the non-linear power flow system to generate FDI attacks more accurately. Later, the authors propose a novel optimal defence strategy by developing a greedy algorithm. The authors' algorithm finds the optimal power assets' locations and defends against hidden FDI attacks with low computation cost. Finally, the authors utilize the proposed AC-based attack and defence models to identify secure sites for distributed generation (DG) in the smart grid. The authors' experimental results for various IEEE standard test systems show enhanced accuracy of the attack and defence algorithms. The authors also validate the effectiveness of the proposed approaches in finding secure sites for DG units in the smart grid.

Optimal utilization of automated distributed generation in smart grid using genetic algorithm

<span>Distributed generation (DG) is an essential attributor in smart grid to fulfill the uncontrollable increase in the demand for energy. Artificial intelligent optimization techniques are widely used within automation systems for guarantee the optimal operation and utilization of DG allocation on the day-ahead power scheduling. In this paper, the genetic algorithm technique used for obtaining the optimal utilization of the automated operation of distributed generation for power losses and total cost minimization as well as user comfort maximization considering all operating constraints technique. Distributed generation represented by fuel cells to supply part of the daily demand in the power system. The target is to apply decision-making strategy of smart operation for economical and reliable operation of power system. Concentrated fuel cell units considered representing the available DG at the load centers. The methodology applied to the 11-bus test system. The simulation results have demonstrated that the GA capability for full automation of DGs in a smart manner within the power system for economic and safe operation</span>