Sivasubramani Shanmugavelu

The growing adoption of plug-in electric vehicles (PEVs) has significantly increased electricity demand, necessitating upgrades to both the power grid and charging infrastructure. Uncoordinated charging of PEVs can result in an increased power losses and the overloading of distribution lines, leading to higher operational costs. This work proposes a coordinated charging methodology that incorporates both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations to facilitate the smooth incorporation of PEVs in distribution system. To assess the technical and economic implications of PEV adoption, a comprehensive charging strategy is developed to minimize the operational costs of charging stations (CSs). The problem is modeled as a single-objective optimization task and solved using the exponential distribution-based particle swarm optimization (EDPSO), effectively reducing energy costs, reducing system power losses, and preventing overloading. For comparison, the proposed model is also solved using the advanced PSO (APSO), and the basic PSO. The methodology is tested on a 33-bus radial distribution network, integrating CSs located at industrial nodes with solar and wind energy. Feasibility is analyzed under scenarios including and excluding solar and wind energy to ensure that distribution connections remain within safe limits despite the addition of PEVs.

In this study, a novel application of multi-objective (MO) optimization methodology is presented for distributed generation (DG) planning incorporating energy management (EM). The MO problem considers the DG cost reduction, system reliability enhancement, and power loss minimization objectives to determine the optimal capacity and position of renewable and dispatchable DG. The initial capital, maintenance and operation, emission, and fuel costs make up the proposed long-term DG cost. Expected outage cost (ECOST) due to interrupted power supply is evaluated to estimate the reliability of the distribution network. The proposed MO optimization problem is optimized using a cooperative game theory technique. The technique is based on the super-criterion and bargaining model concept. In this game process, each objective acts as a participant, and ultimately, all stakeholders are able to achieve win-win outcomes through collective negotiations. Various scenarios and case studies are performed on the modified IEEE 33-bus system with an hourly profile of solar photovoltaic arrays, wind turbines, and load demand. Simulation results illustrate the efficacy of the proposed approach. Moreover the real-time applicability of the proposed model is validated through an experimental setup on the OPAL-RT platform.

Electric vehicle (EV) charging stations (CSs) are the coupling point between the power grid and the transport network. Operational behaviour of EVs will affect both the networks simultaneously. Thus, the optimal placement of CSs in a distribution network plays a vital role. This paper proposes a novel method for optimal placement of EV charging stations considering loss minimization of the electrical grid, as well as EVs’ power loss during travel towards charging station. CS’s utilization factor helps to determine the number of CSs required in a network by ensure the effective usage of the investment in the CS infrastructure. Instead of a constant load/static load demand, the proposed work uses a queueing model for the dynamic behaviour of a CS serviceability. To capture the uncertainty in electrical demand and EV behaviour, a probabilistic load modelling (PLM) method is employed. With the recognized modelling, this work can optimally allocate CSs in a distribution network. The problem is structured as a multi-objective optimization problem. Simulation experiments are conducted on a test system to illustrate the proposed method. The influence on voltage profile, CS utilization, and load uncertainty are analyzed and presented to demonstrate the effectiveness of the proposed method.

This paper proposes a joint optimization model of grid-tied residential microgrid (MG) planning and operation with the support of demand side management (DSM). In this model, the residential MG is included with various distributed energy resources, such as photovoltaic units, wind turbines, microturbines, diesel engine generators (DEs), and battery energy storage systems (BESSs). The proposed model is formulated as a bi-objective optimization problem in view of economic and environmental contemplation. Therefore, the minimization of MG total annual cost and total annual emission are considered as the primary objectives. This paper mainly analyzes the benefits of DSM assistance in the process of residential MG joint planning and operation optimization. The proposed bi-objective optimization problem is converted into a single-objective optimization problem and the single-objective problem is solved using a mixed integer linear programming method. To investigate the impacts of DSM on the MG planning and operation problem, the proposed model is applied to a residential MG consisting of 1000 smart homes with different DSM participation levels. The simulation results show that the proposed model is an efficient tool for residential MG planning and operation optimization from the perspective of economic and environmental considerations.

In modern power systems, efficient methods like demand side management (DSM) are needed for handling the peak load management problem. An intelligent DSM approach not only gives advantages to utilities but also provides indirect benefits to generating companies. This article introduces a combined model of multi-objective dynamic optimal power flow (MODOPF) and a game theory-based DSM technique. In this article, a single utility company and multiple residential energy consumers are considered for the game theory-based DSM technique. Here, the day ahead load shifting DSM technique is implemented with the help of a day-ahead pricing strategy and an energy consumption game. The total generation cost and transmission losses are considered as the main objectives in the proposed MODOPF problem. These two objectives were optimized individually and simultaneously using harmony search (HS) algorithm with different DSM participation levels for the investigation of generation side benefits. The proposed combined model was tested on two different test systems such as IEEE 30 bus and 118 bus. From the simulation results, it is clear that the combined model consisting of MODOPF and DSM is able to achieve a better economic and secure operation when compared to MODOPF alone.

The future transportation network is expected to be dominated by electric vehicles (EVs). With an increase in grid connected vehicles, the power grid needs to increase its generation capacity to meet their charging demand. Thus, from a grid perspective, it is essential to find the maximum number of EVs that the existing infrastructure can handle. This research proposes a new framework to identify the fleet size capability of a power network in both uncontrolled and vehicle to grid (V2G) modes of operation. The proposed model is modelled as an optimization problem and tested on a system which is formed by combining IEEE 33-bus radial distribution system and a 10 unit generation system. Simulation results show that the current system can handle a fixed number of EVs when they operate independently. Participation of EVs in a V2G market, significantly enhances the fleet size handling capability of the existing infrastructure which is approximately four times the present capacity. Further, a relation between the fleet size of EVs and operating points of plants is extracted which is very beneficial for system planners. The result reveals that the time span requirements for power infrastructure up-gradation can be increased by twofold if the V2G market is applied in the network.