Energy Systems 

About: Energy Systems is an academic journal. The journal publishes majorly in the area(s): Electric power system & Wind power. It has an ISSN identifier of 1868-3967. Over the lifetime, 477 publications have been published receiving 5737 citations.

Optimal power flow: a bibliographic survey I

Abstract: Over the past half-century, Optimal Power Flow (OPF) has become one of the most important and widely studied nonlinear optimization problems. In general, OPF seeks to optimize the operation of electric power generation, transmission, and distribution networks subject to system constraints and control limits. Within this framework, however, there is an extremely wide variety of OPF formulations and solution methods. Moreover, the nature of OPF continues to evolve due to modern electricity markets and renewable resource integration. In this two-part survey, we survey both the classical and recent OPF literature in order to provide a sound context for the state of the art in OPF formulation and solution methods. The survey contributes a comprehensive discussion of specific optimization techniques that have been applied to OPF, with an emphasis on the advantages, disadvantages, and computational characteristics of each. Part I of the survey (this article) provides an introduction and surveys the deterministic optimization methods that have been applied to OPF. Part II of the survey examines the recent trend towards stochastic, or non-deterministic, search techniques and hybrid methods for OPF.

Optimal power flow: A bibliographic survey II non-deterministic and hybrid methods

Abstract: Over the past half-century, Optimal Power Flow (OPF) has become one of the most important and widely studied nonlinear optimization problems. In general, OPF seeks to optimize the operation of electric power generation, transmission, and distribution networks subject to system constraints and control limits. Within this framework, however, there is an extremely wide variety of OPF formulations and solution methods. Moreover, the nature of OPF continues to evolve due to modern electricity markets and renewable resource integration. In this two-part survey, we survey both the classical and recent OPF literature in order to provide a sound context for the state of the art in OPF formulation and solution methods. The survey contributes a comprehensive discussion of specific optimization techniques that have been applied to OPF, with an emphasis on the advantages, disadvantages, and computational characteristics of each. Part I of the survey provides an introduction and surveys the deterministic optimization methods that have been applied to OPF. Part II of the survey (this article) examines the recent trend towards stochastic, or non-deterministic, search techniques and hybrid methods for OPF.

A stochastic dynamic programming model for co-optimization of distributed energy storage

Abstract: We develop a stochastic dynamic programming model that co-optimizes the use of energy storage for multiple applications, such as energy, capacity, and backup services, while accounting for market and system uncertainty. Using the example of a battery that has been installed in a home as a distributed storage device, we demonstrate the ability of the model to co-optimize services that ‘compete’ for the capacity of the battery. We also show that these multiple uses of a battery can provide substantive value.

An overview of energy demand forecasting methods published in 2005–2015

Abstract: The importance of energy demand management has been more vital in recent decades as the resources are getting less, emission is getting more and developments in applying renewable and clean energies has not been globally applied. Demand forecasting plays a vital role in energy supply-demand management for both governments and private companies. Therefore, using models to accurately forecast the future energy consumption trends—specifically with nonlinear data—is an important issue for the power production and distribution systems. Several techniques have been developed over the last few decades to accurately predict the future in energy consumption. This paper reviews various energy demand-forecasting methods that have been published as research articles between 2005 and 2015. The scope of forecasting applications and techniques is quite large, and this article focuses on the methods which are used to predict energy consumption. The applications of traditional techniques such as econometric and time series models along with soft computing methods such as neural networks, fuzzy logic and other models are reviewed in the current work. The most cited studies applied neural networks to forecast the energy consumption and approved the notable performance of the models, but computation time is much more than many other methods based on its sophisticated structure. Another field of future research includes the development of hybrid methods. The literature shows that the classical methods cannot result in dominant outputs anymore.

A comprehensive review on power distribution network reconfiguration

Abstract: Reconfiguration of radial distribution networks is becoming a viable solution for improving the performance of distribution networks. Configurations may be varied with manual or automatic switching operations so that all of the loads are supplied and reduce power loss, increase system security, and enhance power quality. Reconfiguration also relieves the overloading of the network components. The change in the network configuration is performed by opening sectionalizing (normally closed) and closing tie (normally open) switches of the network. These switchings are performed in such a way that the radiality of the network is maintained and all of the loads are energized. Several researchers have attempted to solve the power distribution network reconfiguration problem using various techniques. This paper presents a comprehensive survey on network reconfiguration to bring out a clear idea for future research.