Modelling energy storage systems using Fourier analysis: An application for smart grids optimal management

In this paper, a new and efficient model for variables representation, named F-coding, in optimal power dispatch problems for smart electrical distribution grids is proposed. In particular, an application devoted to optimal energy dispatch of Distributed Energy Resources including ideal storage devices is here considered. Electrical energy storage systems, such as any other component that must meet an integral capacity constraint in optimal dispatch problems, have to show the same energy level at the beginning and at the end of the considered timeframe for operation. The use of zero-integral functions, such as sinusoidal functions, for the synthesis of the charge and discharge course of batteries is thus consequential. The issue is common to many other engineering problems, such as any dispatch problem where resources must be allocated within a given amount in a considered timeframe. Many authors have proposed different methods to deal with such integral constraints in the literature on smart grids management, but all of them do not seem very efficient. The paper is organized as follows. First, the state of the art on the optimal management problem is outlined with special attention to treatment of integral constraints, then the proposed new model for variables representation is described. Finally, the multiobjective optimization method and its application to the optimal dispatch problem considering different variables representations are considered.     

Power and energy management for server systems

This survey shows that heterogeneous server clusters can be made more efficient by conserving power and energy while exploiting information from the service level, such as request priorities established by service-level agreements.     

A density-based topology optimization methodology for thermal energy storage systems

Structural and Multidisciplinary Optimization - As many renewable energy resources are prone to an intermittent production of energy and the electric energy demand varies on daily and seasonal...     

New optimization method based on energy management in microgrids based on energy storage systems and combined heat and power

Microgrids can be assumed as a solution model for green energy sources, energy storage systems, and combined heat and power (CHP) systems. In this work, the cost and emission minimization based on a demand response (DR) program is considered an optimization problem. To solve the mentioned problem a new multiobjective optimization algorithm (improved particle swarm optimization) is proposed based on a fuzzy mechanism to select the optimal value. The microgrid system includes two CHP units, fuel cell and battery systems, and the heat buffer tank. In this problem, two different feasible operating regions have been assumed in CHPs. Accordingly, to decrease the operational cost, time-of-use, and real-time pricing DR programs have been simulated, and the impacts of the mentioned models are evaluated overload profiles. The effectiveness of proposed models has been applied on different cases studies by different scenarios. The proposed model solved the DR program, time of use-DR and real-time pricing-DR problems. The proposed model could reduce the cost about 10%.     

Fuzzy logic-based coordinated control method for multi-type battery energy storage systems

In order to take full advantage of the complementary nature of multi-type energy storage and maximally increase the capability of tracking the scheduled wind power output, a charging–discharging control strategy for a battery energy storage system (BESS) comprising many control coefficients is established, and a power distribution method employing fuzzy control principles to optimize the multi-type BESS is proposed, so as to reduce the error of day-ahead short-term wind power prediction. A simulation analysis, taking a typical wind farm output as an actual data sample, showed that the proposed fuzzy logic control method for the multi-type BESS is uniquely flexible and adaptable in achieving the control effect of improving the capability of tracking the scheduled wind power output.     

Dismountable media management in tertiary storage systems

We describe an adaptive algorithm for tertiary storage media mount management. This predictive longest next reference mount management algorithm attempts to approximate the optimal page replacement policy, OPT     

Smart Sankey picturization for energy management systems in India

AI & SOCIETY - India’s energy demand is predicted to rise by 135% within a span of 20 years. Coping up with surging energy demands requires several reforms in both renewable and...     

Optimization strategies for Microgrid energy management systems by Genetic Algorithms

Grid-connected Microgrids (MGs) have a key role for bottom-up modernization of the electric distribution network forward next generation Smart Grids, allowing the application of Demand Response (DR) services, as well as the active participation of prosumers into the energy market. To this aim, MGs must be equipped with suitable Energy Management Systems (EMSs) in charge to efficiently manage in real time internal energy flows and the connection with the grid. Several decision making EMSs are proposed in literature mainly based on soft computing techniques and stochastic models. The adoption of Fuzzy Inference Systems (FISs) has proved to be very successful due to their ease of implementation, low computational run time cost, and the high level of interpretability with respect to more conventional models. In this work we investigate different strategies for the synthesis of a FIS (i.e. rule based) EMS by means of a hierarchical Genetic Algorithm (GA) with the aim to maximize the profit generated by the energy exchange with the grid, assuming a Time Of Use (TOU) energy price policy, and at the same time to reduce the EMS rule base system complexity. Results show that the performances are just 10% below to the ideal (optimal) reference solution, even when the rule base system is reduced to less than 30 rules.     

Minimizing residential electrical energy costs using microcomputer energy management systems

Load dependent or time dependent pricing structures provide electrical utilities with a means to use their existing capacities more effectively. Assuming that some type of load or time dependent price structure will be implemented by most utilities in the near future, it is worthwhile to assess the use of microcomputer energy control systems for minimizing residential electrical energy costs. This paper discusses the development of an optimizing energy management algorithm to reduce the cost of electricity under either a time-of-use or demand dependent price structure. A computer model which simulates the electrical demand and energy needs of a typical residence was developed to test the effectiveness of the optimizing algorithms. The results are presented for a period of 30 days for each season and price structure, under varying demand profiles. For a customer with an all-electric home using 2000 kWh each month, a savings of $140 a year is very realistic.     

HAT: an efficient buffer management method for flash-based hybrid storage systems

Flash solid-state drives (SSDs) provide much faster access to data compared with traditional hard disk drives (HDDs). The current price and performance of SSD suggest it can be adopted as a data buffer between main memory and HDD, and buffer management policy in such hybrid systems has attracted more and more interest from research community recently. In this paper, we propose a novel approach to manage the buffer in flash-based hybrid storage systems, named hotness aware hit (HAT). HAT exploits a page reference queue to record the access history as well as the status of accessed pages, i.e., hot, warm, and cold. Additionally, the page reference queue is further split into hot and warm regions which correspond to the memory and flash in general. The HAT approach updates the page status and deals with the page migration in the memory hierarchy according to the current page status and hit position in the page reference queue. Compared with the existing hybrid storage approaches, the proposed HAT can manage the memory and flash cache layers more effectively. Our empirical evaluation on benchmark traces demonstrates the superiority of the proposed strategy against the state-of-the-art competitors.     

Optimal scheduling of battery storage with grid tied PV systems for trade-off between consumer energy cost and storage health

With the advent of renewable energy in India has initiated consumers to get energy storage systems to mange solar power variation. To solve intermittency issues from weather related events that occur with residential photovoltaic generation, intelligent power management strategies have been carried outto tune efficacy of the consumer's renewable energy system while reducing cost. The proposed method decides the state of charge schedule for the battery storage based on a dynamic programming algorithm that minimizes consumer energy cost and maximizes energy storage state of health. The battery state of health was introduced into the model as an ageing coefficient that forces conservative battery behaviour to preserve its lifetime with continued use.Simulation results show a high potential to increase the profitability of a grid connected PV- BESS system using time of use (TOU) tariff.     

Reliability-conscious energy management for fixed-priority real-time embedded systems with weakly hard QoS-constraint

Aggressive scaling in technology size has dramatically increased the power density and degraded the reliability of real-time embedded systems. In this paper, we study the problem of reliability-conscious energy minimization for scheduling fixed-priority real-time embedded systems with weakly hard QoS-constraint. The weakly hard QoS-constraint is modeled with (m, k)-constraint, which requires that at least m out of any k consecutive jobs of a task meet their deadlines. We first propose a technique that can balance the static and dynamic energy consumption for real-time jobs with better speed determination than the classical strategies during their feasible intervals. Then based on it, we propose an adaptive fixed-priority scheduling scheme to reduce the energy consumption for the system while preserving its reliability. Through extensive simulations, our experiment results demonstrate that the proposed techniques can significantly outperform the previous research in energy performance while satisfying the weakly hard QoS-constraint under the reliability requirement.     

A reliable power management scheme for consistent hashing based distributed key value storage systems

Distributed key value storage systems are among the most important types of distributed storage systems currently deployed in data centers. Nowadays, enterprise data centers are facing growing pressure in reducing their power consumption. In this paper, we propose GreenCHT, a reliable power management scheme for consistent hashing based distributed key value storage systems. It consists of a multi-tier replication scheme, a reliable distributed log store, and a predictive power mode scheduler (PMS). Instead of randomly placing replicas of each object on a number of nodes in the consistent hash ring, we arrange the replicas of objects on nonoverlapping tiers of nodes in the ring. This allows the system to fall in various power modes by powering down subsets of servers while not violating data availability. The predictive PMS predicts workloads and adapts to load fluctuation. It cooperates with the multi-tier replication strategy to provide power proportionality for the system. To ensure that the reliability of the system is maintained when replicas are powered down, we distribute the writes to standby replicas to active servers, which ensures failure tolerance of the system. GreenCHT is implemented based on Sheepdog, a distributed key value storage system that uses consistent hashing as an underlying distributed hash table. By replaying 12 typical real workload traces collected from Microsoft, the evaluation results show that GreenCHT can provide significant power savings while maintaining a desired performance. We observe that GreenCHT can reduce power consumption by up to 35%–61%.     

Energy efficient control of HVAC systems with ice cold thermal energy storage

In heating, ventilation and air conditioning (HVAC) systems of medium/high cooling capacity, energy demands can be matched with the help of thermal energy storage (TES) systems. If properly designed, TES systems can reduce energy costs and consumption, equipment size and pollutant emissions. In order to design efficient control strategies for TES systems, we present a model-based approach with the aim of increasing the performance of HVAC systems with ice cold thermal energy storage (CTES). A simulation environment based on Matlab/Simulink^® is developed, where thermal behaviour of the plant is analysed by a lumped formulation of the conservation equations. In particular, the ice CTES is modelled as a hybrid system, where the water phase transitions (solid–melting–liquid and liquid–freezing–solid) are described by combining continuous and discrete dynamics, thus considering both latent and sensible heat. Standard control strategies are compared with a non-linear model predictive control (NLMPC) approach. In the simulation examples model predictive control proves to be the best control solution for the efficient management of ice CTES systems.     

Advanced chance-constrained predictive control for the efficient energy management of renewable power systems

This study presents a complete advanced control structure aimed at the optimal and most efficient energy management for a Grid-Connected Hybrid Power plant. This control scheme is composed of process supervision and process control layers, and it is a possible technology to enable improvements in the energy consumption of industrial systems subject to constraints and process demands. The proposed structure consists of the combination of a Model-Based Predictive Controller, formulated within the Chance Constraints framework to deal with stochastic disturbances (renewable sources, as solar irradiance), an optimal finite-state machine decision system and the use of disturbance estimation techniques for the prediction of renewable sources. The predictive controller uses feedforward compensation of estimated future disturbances, obtained by the use of Nonlinear Auto-Regressive Neural Networks with time delays. The proposed controller aims to perform the management of which energy system to use and to decide where to store energy between multiple storage options. This has to be done while always maximizing the use of renewable energy and optimizing energy generation due to contract rules (maintain maximal economic profit). The proposed method is applied to a case study of energy generation in a sugar cane power plant, with non-dispatchable renewable sources (such as photovoltaic and wind power generation), as well as dispatchable sources (as biomass and biogas). This hybrid power system is subject to operational constraints, as to produce steam in different pressures, sustain internal demands and, imperiously, produce and maintain an amount of electric power throughout each month, defined by strict contract rules with a local Distribution Network Operator (DNO). This paper aims to justify the use of this novel approach to optimal energy generation in hybrid microgrids through simulation, illustrating the performance improvement for different cases.     

基于闪存的混合存储系统缓冲区管理算法

由于基于闪存的混合存储系统充分利用了闪存的高速随机读和磁盘的快速顺序写的特性,近年来已经成为了数据库管理系统的二级存储层的高效存储模式,但其I/O访问开销是一个继续提高存储性能的瓶颈.为了降低混合存储系统的I/O访问开销,提出了一种自适应缓冲区管理算法DLSB.该算法根据数据页的逻辑代价和物理代价进行自适应的数据域选择;并在选择的数据域中,比较闪存队列和磁盘队列容量的实际值与理想值来确定数据页的置换,达到了提高I/O访问效率的目的.实验结果表明,该算法有效且可行,显著降低了混合存储系统的I/O访问开销.     

Secure proof of storage with deduplication for cloud storage systems

Explosion of multimedia content brings forth the needs of efficient resource utilization using the state of the arts cloud computing technologies such as data deduplication. In the cloud computing environments, achieving both data privacy and integrity is the challenging issue for data outsourcing service. Proof of Storage with Deduplication (POSD) is a promising solution that addresses the issue for the cloud storage systems with deduplication enabled. However, the validity of the current POSD scheme stands on the strong assumption that all clients are honest in terms of generating their keys. We present insecurity of this approach under new attack model that malicious clients exploit dishonestly manipulated keys. We also propose an improved POSD scheme to mitigate our attack.     

An optimization-model-based interactive decision support system for regional energy management systems planning under uncertainty

In this study, an interactive decision support system (UREM-IDSS) has been developed based on an inexact optimization model (UREM, University of Regina Energy Model) to aid decision makers in planning energy management systems. Optimization modeling, scenario development, user interaction, policy analysis and visual display are seamlessly integrated into the UREM-IDSS. Uncertainties in energy-related parameters are effectively addressed through the interval linear programming (ILP) approach, improving the robustness of the UREM-IDSS for real-world applications. Thus, it can be used as an efficient tool for analyzing and visualizing impacts of energy and environmental policies, regional/community sustainable development strategies, emission reduction measures and climate change in an interactive, flexible and dynamic context. The Region of Waterloo has been selected to demonstrate the applicability and capability of the UREM-IDSS. A variety of scenarios (including a reference case) have been identified based on different energy management policies and sustainable development strategies for in-depth analysis of interactions existing among energy, socio-economy, and environment in the Region. Useful solutions for the planning of energy management systems have been generated, reflecting complex tradeoffs among energy-related, environmental and economic considerations. Results indicate that the UREM-IDSS can be successfully used for evaluating and analyzing not only the effects of an individual policy scenario, but also the variations between different scenarios compared with a reference case. Also, the UREM-IDSS can help tackle dynamic and interactive characteristics of the energy management system in the Region of Waterloo, and can address issues concerning cost-effective allocation of energy resources and services. Thus, it can be used by decision makers as an effective technique in examining and visualizing impacts of energy and environmental policies, regional/community development strategies, emission reduction measures, and climate change within an integrated and dynamic framework.