T. Q. Duong

Y. Shi, H. D. Tuan, A. A. Nasir et al.

In this paper, phasor measurement unit (PMU) placement for power grid state estimation under different degrees of observability is studied. Observability degree is the depth of the buses' reachability by the placed PMUs and thus constitutes an important characteristic for PMU placement. However, the sole observability as addressed in many works still does not guarantee a good estimate for the grid state. Some existing works also considered the PMU placement for minimizing the mean squared error or maximizing the mutual information between the measurement output and grid state. However, they ignore the observability requirements for computational tractability and thus potentially lead to artificial results such as acceptance of the estimate for an unobserved state component as its unconditional mean. In this work, the PMU placement optimization problem is considered by minimizing the mean squared error or maximizing the mutual information between the measurement output and grid state, under grid observability constraints. The provided solution is free from the mentioned fundamental drawbacks in the existing PMU placement designs. The problems are posed as binary nonlinear optimization problems, for which this paper develops efficient algorithms for computational solutions. The performance of the proposed algorithms is analyzed in detail through numerical examples on large-scale IEEE power networks.

Y. Shi, H. D. Tuan, T. Q. Duong et al.

The success of the transportation electricification in this century particularly requires the penentration of the internet of plug-in electric vehicles (PEVs) into the smart power grid. Beside the function of serving the traditional residential power demand, next-generation power grids also aim to support the internet of PEVs at the same time. The distinct difference between the traditional power demand and PEVs' power demand is that while the statistics of the former is rich enough for treating it as inelastic/known before hand, the latter is unknown until random PEVs' arrivals. Massive penentration of PEVs certainly causes the grid unpredictable fluctuation. The present paper considers the joint PEVs charging coordination and grid power generation to minimizing both of the negative impact of PEVs' integration and the cost of power generation while meeting the grid operating constraints and all parties' demand. The bang-bang PEVs charging strategy is adopted to exploit its simple implementation. By using a recently developed model predictive control (MPC) model for this problem, the online compuation is based on a predictive mixed integer nonlinear programming (MINP). A new solution computation for this optimization problem is developed. Its capacity of achieving the globally optimal solution is shown by numerical comparison between its performance and that by an off-line optimal solution.

Y. Shi, H. D. Tuan, A. V. Savkin et al.

Next-generation power grids will likely enable concurrent service for residences and plug-in electric vehicles (PEVs). While the residence power demand profile is known and thus can be considered inelastic, the PEVs' power demand is only known after random PEVs' arrivals. PEV charging scheduling aims at minimizing the potential impact of the massive integration of PEVs into power grids to save service costs to customers while power control aims at minimizing the cost of power generation subject to operating constraints and meeting demand. The present paper develops a model predictive control (MPC)- based approach to address the joint PEV charging scheduling and power control to minimize both PEV charging cost and energy generation cost in meeting both residence and PEV power demands. Unlike in related works, no assumptions are made about the probability distribution of PEVs' arrivals, the known PEVs' future demand, or the unlimited charging capacity of PEVs. The proposed approach is shown to achieve a globally optimal solution. Numerical results for IEEE benchmark power grids serving Tesla Model S PEVs show the merit of this approach.