Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107

A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

Recent technological advances have greatly improved the performance and features of embedded systems. With the number of just mobile devices now reaching nearly equal to the population of earth, embedded systems have truly become ubiquitous. These trends, however, have also made the task of managing their power consumption extremely challenging. In recent years, several techniques have been proposed to address this issue. In this paper, we survey the techniques for managing power consumption of embedded systems. We discuss the need of power management and provide a classification of the techniques on several important parameters to highlight their similarities and differences. This paper is intended to help the researchers and application-developers in gaining insights into the working of power management techniques and designing even more efficient high-performance embedded systems of tomorrow

Low Power Processor Architectures and Contemporary Techniques for Power Optimization – A Review

The technological evolution has increased the number of transistors for a given die area significantly and increased the switching speed from few MHz to GHz range. Such inversely proportional decline in size and boost in performance consequently demands shrinking of supply voltage and effective power dissipation in chips with millions of transistors. This has triggered substantial amount of research in power reduction techniques into almost every aspect of the chip and particularly the processor cores contained in the chip. This paper presents an overview of techniques for achieving the power efficiency mainly at the processor core level but also visits related domains such as buses and memories. There are various processor parameters and features such as supply voltage, clock frequency, cache and pipelining which can be optimized to reduce the power consumption of the processor. This paper discusses various ways in which these parameters can be optimized. Also, emerging power efficient processor architectures are overviewed and research activities are discussed which should help reader identify how these factors in a processor contribute to power consumption. Some of these concepts have been already established whereas others are still active research areas. © 2009 ACADEMY PUBLISHER

Market based intelligent charging system for electric vehicles

The existing electrical infrastructure is very unlikely to expand overnight. Therefore, a smart solution is certainly needed to integrate the additional load which electric vehicles (EV) bring to the network. The aim of the thesis is to study the electricity market, different intelligences related to electric vehicle charging and establish an algorithm that produces an optimized charging schedule for electric vehicles. The algorithm ensures a cost profit for user and takes part in demand response by shifting the timing of charging loads based on energy prices. The core intelligences integrated to the EV charging system in the thesis are cost optimization, peak shaving and load shifting. The algorithm follows the hourly unit cost related to the energy consumption and distribution fee in order to find the cheapest time slot for charging operation. It allocates as high charging power as possible to the cheapest time slots and then start selecting the expensive time slots until the battery is charged to desired state of charge. Along this process, the algorithm continuously calculates the maximum charging power available after other house-hold usage. The Elspot area price of Finland for 2018 added with 0.3 cents/kWh margin and 24% VAT are used as energy prices. Distribution unit prices include time-of-use pricing for day and nighttime energy use in addition to the fixed fuse-based fee. By following these unit prices, the algorithm shifts the load from high demand to low demand hours in order to minimize the total costs. The algorithm is implemented in MATLAB and tested through a case study on different type of Finnish detached houses. Detached houses with different load profile data are used as input for charging a 75 kWh EV with a 10 kW and 7.5 kW charger in different cases, where the other inputs remain same for all the test cases. The Elspot area price of Finland for 2018 added with 0.3 cents/kWh margin and 24% VAT are used as energy prices. Different day and night-time distribution prices are applied depending on the consumption. The simulation results are compared to regular EV charging, where the charging operation starts right after the EV is plugged in and finishes charging within shortest time. The results from the simulation are investigated from user’s and grid’s point of view. From user’s perspective, all the charging events with intelligent charging have costs savings over regular charging. The monetary profit is higher for higher charger rating (10 kW). In cases where the household usage is low, the proportional profit is high. From grid point of view, over 99% of the load gets shifted to night-time for 10 kW charger cases. For the 7.5kW charger, the amount of shifted load is over 97%, which is a little lower than 10 kW charger cases because of longer charging time. The findings of the case study validate the use of smart charging algorithm in order to ensure cost savings for the user

Demand and Storage Management in a Prosumer Nanogrid Based on Energy Forecasting

Energy efficiency and consumers' role in the energy system are among the strategic research topics in power systems these days. Smart grids (SG) and, specifically, microgrids, are key tools for these purposes. This paper presents a three-stage strategy for energy management in a prosumer nanogrid. Firstly, energy monitoring is performed and time-space compression is applied as a tool for forecasting energy resources and power quality (PQ) indices; secondly, demand is managed, taking advantage of smart appliances (SA) to reduce the electricity bill; finally, energy storage systems (ESS) are also managed to better match the forecasted generation of each prosumer. Results show how these strategies can be coordinated to contribute to energy management in the prosumer nanogrid. A simulation test is included, which proves how effectively the prosumers' power converters track the power setpoints obtained from the proposed strategy.Spanish Agencia Estatal de Investigacion ; Fondo Europeo de Desarrollo Regional