Saad Ullah Khan

Coordinated Control Algorithm for Distributed Battery Energy Storage Systems for Mitigating Voltage and Frequency Deviations

Energy storage systems (ESSs) are essential in future power systems because they can improve power usage efficiency. In this paper, a novel coordinated control algorithm is proposed for distributed battery ESSs (BESSs). The neighboring BESSs of a simulation system are grouped and controlled by a main control center. The main control center sends charging or discharging operation signals to each BESS. The primary objective of the proposed coordinated control scheme is to mitigate voltage and frequency deviations. In order to verify the proposed algorithm, the BESSs are connected to a distribution system of the Korea Electric Power Corporation. The results are compared with those obtained using uncoordinated control scheme with on-load tap changer considering aspects of power quality (voltage and frequency variation). The simulation results show that the voltage and frequency deviations are reduced with the proposed coordinated control algorithm.

Direct Lightning Overvoltage Analysis of a Distribution Feeder Considering Integrated Electric Vehicles

Secure and continuous electric vehicle (EV) charging operation is a key concern for the safety of EVs and their reliable integration with the power grid. During a lightning incident on the distribution network, the security of integrated EVs is threatened. Lightning overvoltages can be damaging for the EVs, resulting in huge repair costs. Thus, it is necessary that the feeder is provided with lightning protection for the safe integration of EVs. This paper has analyzed direct lightning overvoltages on IEEE 13-node distribution feeder with and without surge arrester protection. The system overvoltages are evaluated on primary distribution nodes, which are the prospective locations for EV connection. Results show the importance of lightning protection of the feeder for EV integration.

Decentralized Demand Response Architecture for Energy Management of Residential Consumers

Due to the current alarming conditions regarding environmental pollution and growing energy crisis, electric vehicles (EVs) are gaining popularity across the world. According to one estimation [1], there will be 20 million EV fleets on the road by 2020 and that the aggregated sale of EVs will rise to 5.9 million. Integration of large number of EVs to the distribution network will pose serious threats in terms of the reliability and stability of the power system. In order to mitigate the adverse effects on the power system, this paper proposes an approach for the management of EV charging along with smart home load management (SHLM) systems by using a water-filling (WF) algorithm. The proposed framework ensures that the instantaneous load at a distribution transformer is restrained below its rated capacity while consumer preferences are taken into account. In the case that the instantaneous demand increases beyond the transformer capacity, the proposed framework allocates a demand restraining limit (DRL) to each home associated with the transformer. The allocation of the DRL is dynamic and based on the consideration of the convenience factor, demand profile and characteristics of the flexible appliances of all the consumers. The performance of the proposed plan is verified by considering a network consisting of a distribution transformer and five homes.

An Adaptive Control of Smart Appliances with Peak Shaving Considering EV Penetration

Electric utilities may face new threats with increase in electric vehicles (EVs) in the personal automobile market. The peak demand will increase which may stress the distribution network equipment. The focus of this paper is on an adaptive control of smart household appliances by using an intelligent load management system (ILMS). The main objectives are to accomplish consumer needs and prevent overloading of power grid. The stress from the network is released by limiting the peak demand of a house when it exceeds a certain point. In the proposed strategy, for each smart appliance, the customers will set its order/rank according to their own preferences and then system will control the household loads intelligently for consumer reliability. The load order can be changed at any time by the customer. The difference between the set and actual value for each load’s specific parameter will help the utility to estimate the acceptance of this intelligent load management system by the customers.