Model Predictive Control for Smart Grids With Multiple Electric-Vehicle Charging Stations

Shi, Ye; Tuan, Hoang Duong; Savkin, Andrey V.; Duong, Trung Q.; Poor, H. Vincent

doi:10.1109/tsg.2017.2789333

Cited by 126 publications

(58 citation statements)

References 29 publications

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“…Following the developments in [41]- [45], instead of handling the nonconvex constraints (29b) and g 2 (y y y) ≥ |Z| and g 3 (c c c) ≥ N −|Z| we incorporate the degree of their satisfaction into the objective in (22), leading to the following penalized optimization problem: where μ > 0 is a penalty parameter. This penalized optimization problem is exact with sufficiently large μ in the sense that its optimal solution is also optimal for (22).…”

Section: Scalable Penalty Algorithms For Optimal Pmu Placementmentioning

confidence: 99%

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PMU Placement Optimization for Efficient State Estimation in Smart Grid

Shi

Tuan

Poor

et al. 2020

IEEE J. Select. Areas Commun.

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This paper investigates phasor measurement unit (PMU) placement for informative state estimation in smart grid by incorporating various constraints for observability. Observability constitutes an important information-theoretic characteristic for PMU placement to reply the depth of the buses' reachability by the placed PMUs, but addresing it solely by binary linear programming in many works still does not guarantee a good estimate for the grid state. Some existing works considered optimization problems of some estimation indexes by ignoring the observability requirements for computational ease and thus potentially lead to trivial 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 subject to observability constraints, which incorporate operating conditions such as presence of zero injection buses, contigency of measurement loss, and limitation of communication channels per PMU. The proposed design is thus free from the fundamental shortcomings in the existing PMU placement designs. The problems are posed as large scale binary nonlinear optimization problems involving thousands binary variables, for which this paper develops efficient algorithms for computational solutions. Their performance is analyzed in detail through numerical examples on large scale IEEE power networks. The solution method is also shown to be extended to AC power flow models, which are formulated by nonlinear equations.

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Section: Scalable Penalty Algorithms For Optimal Pmu Placementmentioning

confidence: 99%

“…The computational complexity of Algorithm 1 is determined by (45), which is the computational complexity of the convex problem (41)/(42) solved at each iteration, and the number of iterations for its convergence. Fig.…”

Section: B Computation Experiencementioning

confidence: 99%

PMU Placement Optimization for Efficient State Estimation in Smart Grid

Shi

Tuan

Poor

et al. 2020

IEEE J. Select. Areas Commun.

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“…Like [13], we consider an electric power grid with a set of buses N := {1, 2, ..., N } connected through a set of flow lines L ⊆ N ×N , under which bus k is connected to bus m if and only if (k, m) ∈ L. Denote by N (k) the set of other buses connected to bus k. G ⊆ N is the set of those buses that are connected to distributed generators (DGs). Bus k ∈ N \ G is not connected to DGs and bus k ∈ G also has a function to serve PEVs and will be referred as charging station (CS) k. Thus, there are M = |G| CSs in the grid.…”

Section: Mpc For Joint Pev Bang-bang Charging Coordination and Grid Pmentioning

confidence: 99%

Mixed Integer Nonlinear Programming for Joint Coordination of Plug-in Electrical Vehicles Charging and Smart Grid Operations

Shi

Tuan

Savkin

2019

2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

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The problem of joint coordination of plug-in electric vehicles (PEVs) charging and grid power control is to minimize both PEVs charging cost and energy generation cost, while meeting both residential and PEVs' power demands and suppressing the potential impact of PEVs integration. A bang-bang PEV charging strategy is adopted to exploit its simple online implementation, which requires computation of a mixed integer nonlinear programming problem (MINP) in binary variables of the PEV charging strategy and continuous variables of the grid voltages. A new solver for this MINP is proposed. Its efficiency is shown by numerical simulations.

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“…The numerical solution of Eq. (26) shows that the variables converge to zero in the finite time, T r , and the upper bound of this time is as below [23]…”

Section: Two Novel Approaches Of Terminal Sliding Mode Controlmentioning

confidence: 99%

“…In [6], a Cyber-Physical Power System (CPPS) paradigm has been presented for smart transmission grids control, modeling, and monitoring. In [26], a model predictive control (MPC)-based approach has been proposed for smart grids with multiple electric-vehicle charging stations. In [34], a resonance attacks have been investigated on Load Frequency Control of Smart Grids.…”

Section: Introductionmentioning

confidence: 99%

Two Novel Approaches of NTSMC and ANTSMC Synchronization for Smart Grid Chaotic Systems

Abadi

Hosseinabadi

Mekhilef

2018

Technol Econ Smart Grids Sustain Energy

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The presence of uncertainties and external disturbances is one of the unavoidable problems with various practical systems which might be unavailable in real-time. Sliding Mode Control (SMC) is one of the effective robust control methods to deal with these uncertainties and external disturbances. In this paper, two novel controllers are designed by using Nonsingular Terminal SMC (NTSMC) and Adaptive Nonsingular Terminal SMC (ANTSMC) methods for synchronization of dual smart grid chaotic systems with various uncertainties and external disturbances. Indeed, both adaptive and non-adaptive controllers based on NTSMC are proposed to provide two alternatives which can adjust by changing operating conditions and dynamics. The concept of SMC method guarantees controller robustness against various uncertainties and external disturbances. Elimination of the undesirable chattering phenomenon is addressed in this study which is one of the common deficiencies with conventional SMC method. Additionally, finite time concept is used to speed up the convergence rate. Finite time stability proof is performed by using Lyapunov stability theory. The numerical simulation is carried out in Simulink/MATLAB to reveal the validity of the proposed controllers for the smart grid chaotic system. A comprehensive comparison is made by performing simulation for the Fractional Order Adaptive Sliding Mode Control (FOASMC) controller and defining three performance criteria, among the proposed controllers in this study and FOASMC controller.

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Model Predictive Control for Smart Grids With Multiple Electric-Vehicle Charging Stations

Cited by 126 publications

References 29 publications

PMU Placement Optimization for Efficient State Estimation in Smart Grid

PMU Placement Optimization for Efficient State Estimation in Smart Grid

Mixed Integer Nonlinear Programming for Joint Coordination of Plug-in Electrical Vehicles Charging and Smart Grid Operations

Two Novel Approaches of NTSMC and ANTSMC Synchronization for Smart Grid Chaotic Systems

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