Energy management

About: Energy management is a research topic. Over the lifetime, 24241 publications have been published within this topic receiving 410577 citations.

A review of energy sources and energy management system in electric vehicles

Abstract: The issues of global warming and depletion of fossil fuels have paved opportunities to electric vehicle (EV). Moreover, the rapid development of power electronics technologies has even realized high energy-efficient vehicles. EV could be the alternative to decrease the global green house gases emission as the energy consumption in the world transportation is high. However, EV faces huge challenges in battery cost since one-third of the EV cost lies on battery. This paper reviews state-of-the-art of the energy sources, storage devices, power converters, low-level control energy management strategies and high supervisor control algorithms used in EV. The comparison on advantages and disadvantages of vehicle technology is highlighted. In addition, the standards and patterns of drive cycles for EV are also outlined. The advancement of power electronics and power processors has enabled sophisticated controls (low-level and high supervisory algorithms) to be implemented in EV to achieve optimum performance as well as the realization of fast-charging stations. The rapid growth of EV has led to the integration of alternative resources to the utility grid and hence smart grid control plays an important role in managing the demand. The awareness of environmental issue and fuel crisis has brought up the sales of EV worldwide.

Demand Side Management in Smart Grid Using Heuristic Optimization

Abstract: Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.

Predictive energy management of a power-split hybrid electric vehicle

Abstract: In this paper, a Model Predictive Control (MPC) strategy is developed for the first time to solve the optimal energy management problem of power-split hybrid electric vehicles. A power-split hybrid combines the advantages of series and parallel hybrids by utilizing two electric machines and a combustion engine. Because of its many modes of operation, modeling a power-split configuration is complex and devising a near-optimal power management strategy is quite challenging. To systematically improve the fuel economy of a power-split hybrid, we formulate the power management problem as a nonlinear optimization problem. The nonlinear powertrain model and the constraints are linearized at each sample time and a receding horizon linear MPC strategy is employed to determine the power split ratio based on the updated model. Simulation results over multiple driving cycles indicate better fuel economy over conventional strategies can be achieved. In addition the proposed algorithm is causal and has the potential for real-time implementation.

AC-microgrids versus DC-microgrids with distributed energy resources: A review

Abstract: This paper presents the latest comprehensive literature review of AC and DC microgrid (MG) systems in connection with distributed generation (DG) units using renewable energy sources (RESs), energy storage systems (ESS) and loads. A survey on the alternative DG units' configurations in the low voltage AC (LVAC) and DC (LVDC) distribution networks with several applications of microgrid systems in the viewpoint of the current and the future consumer equipments energy market is extensively discussed. Based on the economical, technical and environmental benefits of the renewable energy related DG units, a thorough comparison between the two types of microgrid systems is provided. The paper also investigates the feasibility, control and energy management strategies of the two microgrid systems relying on the most current research works. Finally, the generalized relay tripping currents are derived and the protection strategies in microgrid systems are addressed in detail. From this literature survey, it can be revealed that the AC and DC microgrid systems with multiconverter devices are intrinsically potential for the future energy systems to achieve reliability, efficiency and quality power supply.

Energy models for demand forecasting—A review

Abstract: Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.