Real-time predictive voltage control of a direct-current generating system, using a hybrid computer

Real-time predictive voltage control of a direct-current (d-c) generating system is achieved with the aid of a hybrid computer. Some assumptions were required to adequately simplify the simulation of the d-c generating system. A predictive controller is used to maintain the load voltage of the d-c generating system (the controlled system) at the desired level for any change in the load. This controller consists of a fast-time scale linearized analog model of the controlled system and a control logic, which is programmed on the digital part of the hybrid computer. In showing the applicability of the controller, the predictive control technique was first applied to the linearized analog simulation of the controlled system and the desired control was successfully achieved. Control of the real system was then attempted and satisfactory results were obtained

Assessing the Potential of Plug-in Electric Vehicles in Active Distribution Networks

A multi-objective optimization algorithm is proposed in this paper to increase the penetration level of renewable energy sources (RESs) in distribution networks by intelligent management of plug-in electric vehicle (PEV) storage. The proposed algorithm is defined to manage the reverse power flow (PF) from the distribution network to the upstream electrical system. Furthermore, a charging algorithm is proposed within the proposed optimization in order to assure PEV owner's quality of service (QoS). The method uses genetic algorithm (GA) to increase photovoltaic (PV) penetration without jeopardizing PEV owners' (QoS) and grid operating limits, such as voltage level of the grid buses. The method is applied to a part of the Danish low voltage (LV) grid to evaluate its effectiveness and capabilities. Different scenarios have been defined and tested using the proposed method. Simulation results demonstrate the capability of the algorithm in increasing solar power penetration in the grid up to 50%, depending on the PEV penetration level and the freedom of the system operator in managing the available PEV storage

Real-time optimal demand response for frequenc regulation in smart μgrid environment

Real-time demand response (DR) in smart μgrid has been shown to be an effective tool for frequency regulation with increased penetration of renewable energy resources into the grid. Since DR is recognized as an incentive or direct payment to the participants, it is consequently desired to minimize the cost of DR for the utility. This paper presents an optimal DR strategy for minimizing the cost of DR for the utility in smart grid era. The economic model developed by Pennsylvania/New Jersey/Maryland (PJM) utility in the USA is used on an IEEE 13-bus standard system. Simulation results verify the effectiveness of the proposed approach to minimize the cost of DR for the utility. It is also shown that the DR, with or without optimization, decreases the overall cost of frequency regulation for the utility compared to the conventional spinning reserve, without sacrificing system stability