Demand response on the residential market is becoming a solution to adapt customer consumption to the offer available and therefore lower the electricity peak prices. Tariff incentives and direct load control of residential air-conditioners and electric heaters are flexible solutions to reduce the peak demand. To include residential demand response resources in planning operators, quantifying the demand reduction is becoming a major issue for all electrical stakeholders. Current methods are based on day or weather matching, regressions and control group approaches. In general, methods using available data from a control group give more accurate results. With the introduction of smart meters, the electric utilities generate a large amount of quality data, available almost in real time. In this paper, we suggest using these available residential load curves to select a control group based on individual load curves. One of the advantages of our method is that the selected control group could adapt at anytime to the number of individuals belonging to the demand reduction program, as this number evolves with customers entering and leaving the program. Constrained regression methods and an algorithm are developed and evaluated on real data, providing a reliable solution for an operational use.

This paper presents the modeling and control for a novel Compressed Air Energy Storage (CAES) system for wind turbines. The system captures excess power prior to electricity generation so that electrical components can be downsized for demand instead of supply. Energy is stored in a high pressure dual chamber liquid-compressed air storage vessel. It takes advantage of the power density of hydraulics and the energy density of pneumatics in the “open accumulator” architecture. A liquid piston air compressor/expander is utilized to achieve near-isothermal compression/expansion for efficient operation. A cycle-average approach is used to model the dynamics of each component in the combined wind turbine and storage system. Standard torque control is used to capture the maximum power from wind through a hydraulic pump attached to the turbine rotor in the nacelle. To achieve both accumulator pressure regulation and generator power tracking, a nonlinear controller is designed based on an energy based Lyapunov function. The nonlinear controller is then modified to distribute the control effort between the hydraulic and pneumatic elements based on their bandwidth capabilities. As a result, liquid piston air compressor/expander will loosely maintain the accumulator pressure ratio, while the down-tower hydraulic pump/motor precisely tracks the desired generator power. This control scheme also allows the accumulator to function as a damper for the storage system by absorbing power disturbances from the hydraulic path generated by the wind gusts. A set of simulation case studies demonstrate the operation of the combined system when the nonlinear controller is utilized and illustrates how this system can be used for load leveling, downsizing electrical system and maximizing revenues.

This paper is concerned with maximum efficiency or power tracking for pneumatically-driven electric generator of a stand-alone small scale compressed air energy storage system (CAES). In this system, an air motor is used to drive a permanent magnet DC generator, whose output power is controlled by a buck converter supplying a resistive load. The output power of the buck converter is controlled power such that the air motor operates at a speed corresponding to either maximum power or maximum efficiency. The maximum point tracking controller uses a linearised model of the air motor together with integral control action. The analysis and design of the controller is based on a small injected-absorbed current signal-model of the buck converter. The controller was implemented experimentally using a dSPACE system. Test results are presented to validate the design and demonstrate its capabilities.

In this paper a new concept for control and performance assessment of compressed air energy storage (CAES) systems in a hybrid energy system is introduced. The proposed criterion, based on the concept of energy harvest index (HEI), measures the capability of a storage system to capture renewable energy. The overall efficiency of the CAES system and optimum control and design from the technical and economic point of view is presented. A possible application of this idea is an isolated community with significant wind energy resource. A case study reveals the usefulness of the proposed criterion in design, control and implementation of a small CAES system in a hybrid power system (HPM) for an isolated community. Energy harvested index and its effectiveness in increasing the wind penetration rate in the total energy production is discussed.

Distributed energy storage has been recognized as a valuable and often indispensable complement to small-scale power generation based on renewable energy sources. Small- scale energy storage positioned at the demand side would open the possibility for enhanced predictability of power output and easier integration of small-scale intermittent generators into functioning electricity markets, as well as offering inherent peak shaving abilities for mitigating contingencies and blackouts, for reducing transmission losses in local networks, profit optimization and generally allowing tighter utility control on renewable energy generation. Distributed energy storage at affordable costs and of low environmental footprint is a necessary prerequisite for the wider deployment of renewable energy and its deeper penetration into local networks.

Thermodynamic energy storage in the form of compressed air is an alternative to electrochemical energy storage in batteries and has been evaluated in various studies and tested commercially on a large scale. Small-scale distributed compressed air energy storage (DCAES) systems in combination with renewable energy generators installed at residential homes or small businesses are a viable alternative to large-scale energy storage, moreover promising lower specific investment than batteries. Flexible control methods can be applied to DCAES units, resulting in a complex system running either independently for home power supply, or as a unified and centrally controlled utility-scale energy storage entity.

This study aims at conceptualizing the plausible distributed compressed-air energy storage units, examining the feasibility for their practical implementation and analyzing their behavior, as well as devising the possible control strategies for optimal utilization of grid-integrated renewable energy sources at small scales. Results show that overall energy storage efficiency of around 70% can be achieved with comparatively simple solutions, offering less technical challenges and lower specific costs than comparable electrical battery systems. Furthermore, smart load management for improving the dispatchability can bring additional benefits by profit optimization and decrease.

Future energy systems will depend much more on renewable energy resources than the current ones. Renewable energy resources, in turn, fluctuate and are not permanently available to the same extent than fossil ones. In consequence, new approaches are required to balance electricity demand and production. One approach is to schedule the compressed-air production of industrial installations according to the current load and supply of the electric grid. To be able to do this, compressed-air has to be stored for peak load phases. Computer simulations are an efficient tool to judge the technical feasibility of such an approach and to compare it with other load management systems. This paper describes the thermodynamic fundamentals of compressed-air energy storage and their integration in a computer model. The obtained results from simulations were compared with results from measurements showing good consistency. Thus, the model was used to simulate different principles to store compressed-air. Systems with low pressure level and with high storage volume appear to be the most energy-efficient ones. In general the technology has the potential to be utilized in the electric load management. However, further simulations are required to determine the most economical solution.

@ARTICLE{7160638,
author={Shi, X. and Li, Y. and Cao, Y. and Tan, Y.},
journal={Power and Energy Systems, CSEE Journal of},
title={Cyber-physical electrical energy systems: challenges and issues},
year={2015},
month={June},
volume={1},
number={2},
pages={36-42},
abstract={Cyber-physical electrical energy systems (CPEES) combine computation, communication and control technologies with physical power system, and realize the efficient fusion of power, information and control. This paper summarizes and analyzes related critical scientific problems and technologies, which are needed to be addressed with the development of CPEES. Firstly, since the co-simulation is an effective method to investigate infrastructure interdependencies, the co-simulation platform establishment of CPEES and its evaluation is overviewed. Then, a critical problem of CPEES is the interaction between energy and information flow, especially the influence of failures happening in information communication technology (ICT) on power system. In order to figure it out, the interaction is analyzed and the current analysis methods are summarized. For the solution of power system control and protection in information network environment, this paper outlines different control principles and illustrates the concept of distributed coordination control. Moreover, mass data processing and cluster analysis, architecture of communication network, information transmission technology and security of CPEES are summarized and analyzed. By solving the above problems and technologies, the development of CPEES will be significantly promoted.},
keywords={Cyber-physical electrical energy systems;informationcommunication technology;power system;smart grid},
doi={10.17775/CSEEJPES.2015.00017},}

@INPROCEEDINGS{7170976,
author={Risbeck, Michael J. and Maravelias, Christos T. and Rawlings, James B. and Turney, Robert D.},
booktitle={American Control Conference (ACC), 2015},
title={Cost optimization of combined building heating/cooling equipment via mixed-integer linear programming},
year={2015},
month={July},
pages={1689-1694},
abstract={In this paper, we propose a mixed-integer linear program to economically optimize equipment usage in a central heating/cooling plant subject to time-of-use and demand charges for utilities. The optimization makes both discrete on/off and continuous load decisions for equipment while determining utilization of thermal energy storage systems. This formulation allows simultaneous optimization of heating and cooling subsystems, which interact directly when heatrecovery chillers are present. Nonlinear equipment models are approximated as piecewise-linear to balance modeling accuracy with the computational constraints imposed by online implementation and to ensure global optimality for the computed solutions. The chief benefits of this formulation are its ability to tightly control on/off switching of equipment, its consideration of cost contributions from auxiliary equipment such as pumps, and its applicability to large systems with multiple heating and cooling units in which a combinatorial problem must be solved to pick the optimal mix of equipment. These features result in improved performance over heuristic scheduling rules or other formulations that do not consider discrete decision variables. We show optimization results for a system with four conventional chillers, two heat-recovery chillers, and one hot water boiler. With a timestep of 1 h and a horizon of 48 h, the optimization problem can be solved to optimality within 5 minutes, indicating suitability for online implementation.},
keywords={Biological system modeling;Cooling;Generators;Load modeling;Optimization;Production;Switches},
doi={10.1109/ACC.2015.7170976},}

Design and Implementation of Real-Time Task’s Scheduling on ARM processor

Author : Boppani Krishna Kanth and G. Bhaskar Phani Ram
Pages : 2666-2670
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Abstract

This paper is an RTOS based architecture designed for the purpose of mine detection. RTOS is a Process which will be done between hardware and application. Here, scheduling is the one which is used to avoid the delay between one application with another. We are using in the mobile communication to receiving the condition of the border level. Using mobile communication we are giving the indication to the monitoring section. The semantic time scheduling is done all applications at a time without any time delay.

Keywords: Robotics, RTOS, GSM, ARM.

Coordinated Electric Vehicle Charging Control with Aggregator Power Trading and Indirect Load Control
James J.Q. Yu, Junhao Lin, Albert Y.S. Lam, Victor O.K. Li
(Submitted on 4 Aug 2015)

Due to the increasing concern on greenhouse gas emmissions and fossil fuel security, Electric Vehicles (EVs) have attracted much attention in recent years. However, the increasing popularity of EVs may cause stability issues to the power grid if their charging behaviors are uncoordinated. In order to address this problem, we propose a novel coordinated strategy for large-scale EV charging. We formulate the energy trade among aggregators with locational marginal pricing to maximize the aggregator profits and to indirectly control the loads to reduce power network congestion. We first develop a centralized iterative charging strategy, and then present a distributed optimization-based heuristic to overcome the high computational complexity and user privacy issues. To evaluate our proposed approach, a modified IEEE 118 bus testing system is employed with 10 aggregators serving 30 000 EVs. The simulation results indicate that our proposed approach can effectively increase the total profit of aggregators, and enhance the power grid stability. 

Subjects: Systems and Control (cs.SY)

@INPROCEEDINGS{7174716,
author={Powroznik, Piotr and Michta, Emil},
booktitle={Nonsinusoidal Currents and Compensation (ISNCC), 2015 International School on},
title={Elastic model of energy management in micro smart grid},
year={2015},
month={June},
pages={1-6},
abstract={In modern electric power systems (EPS) solutions based on advanced metering infrastructure (AMI) are required. The data collected by AMI could help to detect an emergency situations in EPS. AMI two way communications allows both to collect measuring data and control devices referred to as a nodes. Nodes providing control and measurement functions create a smart grid (SG). The smart grid nodes are appliances, power generation and storage capacity. In the case of power storage, these nodes can also take power, in the case of having to be recharged. Analysis of all possible power combining, for all three categories of nodes in even micro SG (MSG) may not be feasible due to time. A novel approach to the power management in MSG by use of an elastic management energy model have been presented in the paper.},
keywords={Artificial intelligence;Heating;Reliability;Security;Smart grids;balanced power selection;elastic management energy model;micro smart grids},
doi={10.1109/ISNCC.2015.7174716},}

IEEE Xplore. Delivering full text access to the world's highest quality technical literature in engineering and technology.

IEEE Xplore. Delivering full text access to the world's highest quality technical literature in engineering and technology.

Alexander Uskov, Bhuvana Sekar

The nascent technologies—smart serious games and smart gamification—potentially present an effective fusion of smart technology and smart systems on one side, and applications of computer game mechanics in “serious” areas and gamification of business processes on the other side. They can combine the features and advantages of both areas, and, as a result, provide the end users with non-existing functionality, features and advances. This chapter is aimed to analyze current status of serious games and gamified applications in industry, examine “smartness” maturity levels of smart objects and systems, classify main components and features and present conceptual design model of smart serious games and smart gamified applications, identify technical skills required for a design and development of smart serious games and smart gamification of business, research and development processes and simulations.

The presence of energy hubs and the advancement in smart grid technologies have motivated system planners to deploy intelligent multicarrier energy systems entitled “smart energy hub” (S.E. Hub). In this paper, we model S.E. Hub, and propose a modern energy management technique in electricity and natural gas networks based on integrated demand side management (IDSM). In conventional studies, energy consumption is optimized from the perspective of each individual user without considering the interactions with each other. Here, the interaction among S.E. Hubs in IDSM program is formulated as a noncooperative game. The existence and uniqueness of a pure strategy Nash equilibrium (NE) is proved. Additionally, the strategies for each S.E. Hub are determined by proposing a distributed algorithm. We also address the IDSM game in a cloud computing (CC) framework to achieve efficient data processing and information management. Simulations are performed on a grid consisting of ten S.E. Hubs. We compare the CC framework with conventional data processing techniques to evaluate the efficiency of our proposed approach in determining NE. It is also shown that in the NE, the energy cost for each S.E. Hub and the peak-to-average ratio of the electricity demand decrease substantially.

Loesch, M.; Hufnagel, D. ; Steuer, S. ; Fabnacht, T. ; Schmeck, H.

A major component of the future Smart Grid is an adaptive demand side that allows to handle the fluctuating power supply based on renewable energies. In this paper, we present an evolutionary algorithm that allows for shifting electrical loads generated by heat pumps. Our approach is based on overheating the hot water storage in order to get a higher degree of freedom for scheduling. In our scenario, we assume time-variable price and load limitation signals as well as a prediction for local power generation from photovoltaic panels to incentivize the load shifting. Using these signals, we consider the future thermal demand to schedule the heat pump such that electricity costs are decreased. Our simulations show that heat pumps and hot water storages bear potential to shift loads over a time span of up to multiple hours, thus providing economical storage capacity. In doing so and based on electricity prices from the stock exchange, we were able to significantly decrease electricity costs for operating the heat pump.

Published in: Intelligent Energy and Power Systems (IEPS), 2014 IEEE International Conference on

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