J. Moyalan

Bio: J. Moyalan is an academic researcher from Veermata Jijabai Technological Institute. The author has contributed to research in topics: Computer science & Grid. The author has an hindex of 2, co-authored 10 publications receiving 10 citations. Previous affiliations of J. Moyalan include Clemson University.

Data-Driven Optimal Control of Nonlinear Dynamics Under Safety Constraints

Abstract: This letter considers the optimal control problem of nonlinear systems under safety constraints with unknown dynamics. Departing from the standard optimal control framework based on dynamic programming, we study its dual formulation over the space of occupancy measures. For control-affine dynamics, with proper reparametrization, the problem can be formulated as an infinite-dimensional convex optimization over occupancy measures. Moreover, the safety constraints can be naturally captured by linear constraints in this formulation. Furthermore, this dual formulation can still be approximately obtained by utilizing the Koopman theory when the underlying dynamics are unknown. Finally, to develop a practical method to solve the resulting convex optimization, we choose a polynomial basis and then relax the problem into a semi-definite program (SDP) using sum-of-square (SOS) techniques. Simulation results are presented to demonstrate the efficacy of the developed framework.

Sum of Squares based Convex Approach for Optimal Control Synthesis

Abstract: We consider an optimal control synthesis problem for a class of control-affine nonlinear systems. We propose Sum-of-Square based computational framework for optimal control synthesis. The proposed computation framework relies on the convex formulation of the optimal control problem in the dual space of densities. The convex formulation to the optimal control problem is based on the duality results in dynamical systems’ stability theory. We used the Sum-of-Square based computational framework for the finite-dimensional approximation of the convex optimization problem. The efficacy of the developed framework is demonstrated using simulation results.

Vehicle-to-Grid Integration for Enhancement of Grid: A Distributed Resource Allocation Approach

Abstract: In the future grids, to reduce greenhouse gas emissions Electric Vehicles (EVs) seems to be an important means of transportation. One of the major disadvantages of the future grid is the demand-supply mismatch which can be mitigated by incorporating the EVs into the grid. The paper introduces the concept of the Distributed Resource Allocation (DRA) approach for incorporating a large number of Plug-in EV (PEVs) with the power grid utilizing the concept of achieving output consensus. The charging/discharging time of all the participating PEVs are separated with respect to time slots and are considered as strategies. The major aim of the paper is to obtain a favorable charging strategy for each grid-connected PEVs in such a way that it satisfies both grid objectives in terms of load profile smoothening and minimizing of load shifting as well as economic and social interests of vehicle owners i.e. a fair share of the rate of charging for all connected PEVs. The three-fold contribution of the paper in smoothening of load profile, load shifting minimization, and fair charging rate is validated using a representative case study. The results confirm improvement in load profile and also highlight a fair deal in the charging rate for each PEV.

Power Grid Load Management using Electric Vehicles: A Distributed Resource Allocation Approach

Abstract: Technological advancements in modern age has introduced complex networks and multi-agent systems all around us. The main aim of multi-agent systems is to implement local control laws to achieve a global objective. The Distributed Resource Allocation (DRA) approach has been utilised in this paper for successful incorporation of a large number of Plug-in Electric Vehicles (PEVs) with the power grid. The DRA approach has been used to find an optimal charging strategy for all PEVs such that it satisfies the power grid objective in terms of the smoothening and flattening of grid load profile. The consensus protocol, an application of DRA approach has been implemented to provide a fair scheme of charging strategy to each individual PEV connected to the grid based on their commitment factor.

Packetized plug-in electric vehicle charge management

Abstract: Plug-in electric vehicle (PEV) charging could cause significant strain on residential distribution systems, unless technologies and incentives are created to mitigate charging during times of peak residential consumption. This paper describes and evaluates a decentralized and ‘packetized’ approach to PEV charge management, in which PEV charging is requested and approved for time-limited periods. This method, which is adapted from approaches for bandwidth sharing in communication networks, simultaneously ensures that constraints in the distribution network are satisfied, that communication bandwidth requirements are relatively small, and that each vehicle has fair access to the available power capacity. This paper compares the performance of the packetized approach to an optimization method and a first-come, first-served (FCFS) charging scheme in a test case with a constrained 500 kVA distribution feeder and time-of-use residential electricity pricing. The results show substantial advantages for the packetized approach. The algorithm provides all vehicles with equal access to constrained resources and attains near optimal travel cost performance, with low complexity and communication requirements. The proposed method does not require that vehicles report or record driving patterns, and thus provides benefits over optimization approaches by preserving privacy and reducing computation and bandwidth requirements.

Integration of Large-Scale Electric Vehicles into Utility Grid: An Efficient Approach for Impact Analysis and Power Quality Assessment

Abstract: Electric vehicles (EVs) have received massive consideration in the automotive industries due to their improved performance, efficiency and capability to minimize global warming and carbon emission impacts. The utilization of EVs has several potential benefits, such as increased use of renewable energy, less dependency on fossil-fuel-based power generations and energy-storage capability. Although EVs can significantly mitigate global carbon emissions, it is challenging to maintain power balance during charging on-peak hours. Thus, it mandates a comprehensive impact analysis of high-level electric vehicle penetration in utility grids. This paper investigates the impacts of large-scale EV penetration on low voltage distribution, considering the charging time, charging method and characteristics. Several charging scenarios are considered for EVs’ integration into the utility grid regarding power demand, voltage profile, power quality and system adequacy. A lookup-table-based charging approach for EVs is proposed for impact analysis, while considering a large-scale integration. It is observed that the bus voltage and line current are affected during high-level charging and discharging of the EVs. The residential grid voltage sag increases by about 1.96% to 1.77%, 2.21%, 1.96 to 1.521% and 1.93% in four EV-charging profiles, respectively. The finding of this work can be adopted in designing optimal charging/discharging of EVs to minimize the impacts on bus voltage and line current.

Data-Driven Optimal Control of Nonlinear Dynamics Under Safety Constraints

Abstract: This letter considers the optimal control problem of nonlinear systems under safety constraints with unknown dynamics. Departing from the standard optimal control framework based on dynamic programming, we study its dual formulation over the space of occupancy measures. For control-affine dynamics, with proper reparametrization, the problem can be formulated as an infinite-dimensional convex optimization over occupancy measures. Moreover, the safety constraints can be naturally captured by linear constraints in this formulation. Furthermore, this dual formulation can still be approximately obtained by utilizing the Koopman theory when the underlying dynamics are unknown. Finally, to develop a practical method to solve the resulting convex optimization, we choose a polynomial basis and then relax the problem into a semi-definite program (SDP) using sum-of-square (SOS) techniques. Simulation results are presented to demonstrate the efficacy of the developed framework.

A robust vehicle to grid aggregation framework for electric vehicles charging cost minimization and for smart grid regulation

Abstract: In this paper, we propose an optimal hierarchical bi-directional aggregation algorithm for the electric vehicles (EVs) integration in the smart grid (SG) using Vehicle to Grid (V2G) technology through a network of Charging Stations (CSs). The proposed model forecasts the power demand and performs Day-ahead (DA) load scheduling in the SG by optimizing EVs charging/discharging tasks. This method uses EVs and CSs as the voltage and frequency stabilizing tools in the SG. Before penetrating EVs in the V2G mode, this algorithm determines the on arrival EVs State of Charge (SOC) at CS, obtains projected park/departure time information from EV owners, evaluates their battery degradation cost prior to charging. After obtaining all necessary data, it either uses EV in the V2G mode to regulates the SG or charge it according to the owner request but, it ensure desired SOC on departure. The robustness of the proposed algorithm has been tested by using IEEE-32 Bus-Bars based power distribution in which EVs are integrated through five CSs. Two intense case studies have been carried out for the appropriate performance validation of the proposed algorithm. Simulations are performed using electricity pricing data from PJM and to test the EVs behaviour 3 types of EVs having different specifications are penetrated. Simulation results have proved that the proposed model is capable of integrating EVs in the voltage and frequency stabilization and it also simultaneously minimizes approximately $1500 in term of charging cost for EVs contributing in the V2G mode each day. Particularly, during peak hours this algorithm provides effective grid stabilization services.