Design, Construction and Testing of a Voltage-based Maximum Power Point Tracker (VMPPT) for Small Satellite Power Supply

It is shown that at maximum power, the Photovoltaic (PV) voltage varies nonlinearily with temperature and isolation level, but is directly proportional to the PV cell open circuit voltage. The proportionality voltage-factor is fixed for a given PV generator regardless of temperature, isolation and panel configuration, but depends on cell material and manufacturing. This remarkable property is used to achieve temperature and insolation independent maximum power point tracking of satellite\u27s solar cells with a simple and reliable technique. The open circuit voltage is continuously measured by a microcontroller and is used to estimate the maximum power operating point of the system. The Voltage-based Maximum Power Point Tracker (VMPPT) is demonstrated by construction and testing of a solar battery charger (using silicon solar cells, Ni-Cd batteries and a buck mode VMPPT), a solar water pump (using silicon solar cells, a PM DC Motor and a boost mode VMPPT) and resistive loads supplied by solar cells. Measured results are satisfactory and confirm the proposed technique. The advantage of this method as compared to the current-based MPPT are simplicity and higher efficiency

A multi-agent intelligent decision making support system for home energy management in smart grid: A fuzzy TOPSIS approach

In the context of intelligent home energy management in smart grid, the occupants' consumption behavior has a direct effect on the demand and supply of the electrical energy market. Correspondingly, the policies of the utility providers affect consumption behavior so techniques and tools are required to analyse the occupants' preferences, habits and lifestyles in order to support and facilitate their decision-making regarding the curtailing of their energy consumption and costs. The uncertainty about householders' preferences increases the uncertainty of appliance prioritization and makes it difficult to determine the consistency of preferences in terms of energy consumption. In this complex system, the preferences and judgments of householders are represented by linguistic and vague patterns. This paper proposes a much better representation of this linguistics that can be developed and refined by using the evaluation methods of fuzzy set theory. The proposed approach will apply the fuzzy Technique for Order Preference by Similarity to Ideal Solution (fuzzy TOPSIS) for achieving preferences. Based on our detailed literature review of the multi-agent system approach in this field, it is expected that the proposal model will offer a robust tool for communication and decision-making between occupant agents and dynamic environmental variables. It is shown that the proposed fuzzy TOPSIS approach will enable and assist householders to maximize their participation in demand response programs

A heuristic approach for coordination of plug-in electric vehicles charging in smart grid

In this paper, a heuristic load management algorithm (H-LMA) is proposed for Plug-in Electric Vehicles (PEVs) charging coordination. The proposed approach is aimed to minimize system losses over a period T (e.g., 24 hours) through re-optimizing the system at time intervals (e.g., 15 minutes) while regulating bus voltages through future smart grid communication system by exchanging signals with individual PEV chargers. Scheduling is performed based on the allowable substation transformer loading level and taking into account PEV owner preference/priority within three designated charging time zones. Starting with the highest priority consumers, H-LMA will distribute charging of PEVs within the selected priority time zones to minimize total system losses over a period T while maintaining network operation criteria such as power generation and bus voltages within their permissible limits. Simulation results are presented for different charging scenarios and are compared to demonstrate the performance of H-LMA for the modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs. The main contribution of this paper lies in the detailed simulations / analyses of the smart grid under study and highlighting the impacts of and T values on the performance of the proposed coordination approach in terms of accuracy and coordination execution time

Performance of heuristic optimization in coordination of plug-in electric vehicles charging

A heuristic load management (H-LMA) algorithm is presented for coordination of Plug-in Electric Vehicles (PEVs) in distribution networks to minimize system losses and regulate bus voltages. The impacts of optimization period T (varied from 15 minutes to 24 hours) and optimization time interval (varied 15 minutes to one hour) on the performance, accuracy and speed of the H-LMA is investigated through detailed simulations considering enormous scenarios. PEV coordination is performed by considering substation transformer loading while taking PEV owner priorities into consideration. Starting with the highest priority consumers, HLMA will use time intervals to distribute PEV charging within three designated high, medium and low priority time zones to minimize total system losses over period T while maintaining network operation criteria such as power generation and bus voltages within their permissible limits. Simulation results generated in MATLAB are presented for a 449 node distribution network populated with PEVs in residential feeders

Fuzzy Approach for Online Coordination of Plug-In Electric Vehicle Charging in Smart Grid

This paper proposes an online fuzzy coordination algorithm (OL-FCA) for charging plug-in electric vehicles (PEVs) in smart grid networks that will reduce the total cost of energy generation and the associated grid losses while maintaining network operation criteria such as maximum demand and node voltage profiles within their permissible limits. A recently implemented PEV coordination algorithm based on maximum sensitivity selection (MSS) optimization is improved using fuzzy reasoning. The proposed OL-FCA considers random plug-in of vehicles, time-varying market energy prices, and PEV owner preferred charging time zones based on priority selection. Impacts of uncoordinated, MSS, and fuzzy coordinated charging on total cost, gird losses, and voltage profiles are investigated by simulating different PEV penetration levels on a 449-node network with three wind distributed generation (WDG) systems. The main advantage of OL-FCA compared with the MSS PEV coordination is the reduction in the total cost it introduces within the 24h

Study on Adaptive Harmonic Extraction Approaches in Active Power Filter Applications

Active power filter (APF) has now become a mature technology for harmonic and reactive power compensations in two-wire (single phase), three-wire (three phase without neutral), and four-wire (three phase with neutral) ac power networks with nonlinear loads. This paper presents a study on three different adaptive algorithms for active power filtering applications. These algorithms are adaptive linear combiner (ADALINE), least mean square adaptive notch filter (ANF-LMS), and recursive least square adaptive notch filter (ANF-RLS). In this paper, these approaches are employed for extracting load harmonic currents. The important issues associated with adaptive methods are accuracy and prediction speed. These issues will be addressed in the paper. Simulations using MATLAB/Simulink are presented to clarify the algorithms