Bargaining‐Based Smart Grid Pricing Model for Demand Side Management Scheduling

A smart grid is a modernized electrical grid that uses information about the behaviors of suppliers and consumers in an automated fashion to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity. In the operation of a smart grid, demand side management (DSM) plays an important role in allowing customers to make informed decisions regarding their energy consumption. In addition, it helps energy providers reduce peak load demand and reshapes the load profile. In this paper, we propose a new DSM scheduling scheme that makes use of the day‐ahead pricing strategy. Based on the Rubinstein–Stahl bargaining model, our pricing strategy allows consumers to make informed decisions regarding their power consumption, while reducing the peak‐to‐average ratio. With a simulation study, it is demonstrated that the proposed scheme can increase the sustainability of a smart grid and reduce overall operational costs.     

An Optimal Power Scheduling Method Applied in Home Energy Management System Based on Demand Response

In this paper, we first introduce a general architecture of an energy management system in a home area network based on a smart grid. Then, we propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real‐time electricity price, which is transferred to an energy management controller (EMC). Referring to the DR, the EMC achieves an optimal power scheduling scheme, which is delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost‐effective way possible. In our research, to avoid the high peak‐to‐average ratio (PAR) of power, we combine the real‐time pricing model with the inclining block rate model. By adopting this combined pricing model, our proposed power scheduling method effectively reduces both the electricity cost and the PAR, ultimately strengthening the stability of the entire electricity system.     

Adaptive algorithm for optimal real‐time pricing in cognitive radio enabled smart grid network

Integration of multiple communication technologies in a smart grid (SG) enables employing cognitive radio (CR) technology for improving reliability and security with low latency by adaptively and effectively allocating spectral resources. The versatile features of the CR enable the smart meter to select either the unlicensed or the licensed band for transmitting data to the utility company, thus reducing communication outage. Demand response management is regarded as the control unit of the SG that balances the load by regulating the real‐time price that benefits both the utility company and consumers. In this study, joint allocation of the transmission power to the smart meter and consumer's demand is formulated as a two stage multi‐armed bandit game in which the players select their optimal strategies noncooperatively without having any prior information about the media. Furthermore, based on historical rewards of the player, a real‐time pricing adaptation method is proposed. The latter is validated through numerical results.     

An optimal real‐time pricing algorithm under load uncertainty and user number variation in smart grid

The progress of real‐time communication systems for smart grid has led to the importance of real‐time pricing becoming more highlighted. There are many investigations that have already been done. Real‐time pricing frameworks have proposed an implemented distributed algorithm with or without consideration of effect of load uncertainty. In some existing literature the effect of different types of load uncertainty models on average consumption and generated capacity is considered. However, the number of users is considered to be constant. In this paper, it is assumed that the number of users is varying independently and randomly. In this case the effect of variation of number of users on the basis of Poisson process and uniform distribution is compared with results from previous works, when bounded uncertainty model was applied for added noise to the consumption. Simulation results indicate that when users vary on the basis of Poisson distribution, the waste of energy decreases and the welfare increases.     

Exploiting Multiuser Diversity With Imperfect One-Bit Channel State Feedback

It has been well recognized that significant throughput gains can be leveraged in multiuser wireless communication systems by exploiting multiuser diversity with a smart scheduler. This scheduler collects channel state information (CSI) from all users and allocates the resources to the user(s) experiencing favorable channel conditions. However, for a frequency-division-duplex system with a large number of users, how to efficiently collect the required CSI will be a challenging task, especially when the feedback links are of limited capacity. In this paper, we propose a scheduling algorithm to exploit multiuser diversity with possibly imperfect one-bit channel state feedback. The basic idea is to define a threshold lambda and let each user report one-bit information to the scheduler about the comparison between its measured channel fading level and lambda. Correspondingly, the scheduler uses these feedback bits to classify all users into two sets and assigns the channel to one user belonging to the set experiencing favorable channel conditions. Several implementation schemes are developed by attacking the optimization of lambda under different system configurations, covering both the case when the one-bit feedback is perfect and those when the one-bit feedback is imperfect. Computer simulations show that when the user number is large, say, more than ten users, the proposed scheduling supports significantly larger data rate over the round-robin scheduling, while in comparison with the optimum scheduling with complete CSI, the performance loss is limited if the one-bit feedback is of high reliability. In addition, our studies show that we can effectively enhance the robustness against feedback imperfectness by incorporating the feedback reliability into optimization of lambda     

Channel discovery algorithms for interference avoidance in smart grid communication networks: a survey

Smart grid technology has attracted a lot of attention in the communication and power engineering communities because of its high efficiency and environmental friendly power production and delivery. Smart grid communication networks act as not only a bridge between the end power users and the utility but also a backbone of the entire smart grid system. It is a challenging task to deploy smart grid communication networks because of interference‐intensive environment. The goal of this paper is to explore various issues on deployment of smart grid communication networks. The first half of this paper provides a survey on smart grid communication networks. The background of smart grid communication networks is introduced, and the IEEE 802.15.4g standard for smart utility networks is reviewed. In addition, the issues on network topology and routing protocols of smart utility networks are addressed, followed by the discussions on optimizing gateway deployment. Various methods for addressing coexistence issues in smart grid communication networks are presented. The second half of this paper is to propose a channel discovery algorithm exploiting the inherent CSMA/CA mechanism in the protocols. Because of the use of CSMA/CA mechanism and up‐to‐date channel status table, the proposed algorithm can quickly find an available channel for transmission when interferences occur. Copyright © 2014 John Wiley & Sons, Ltd.     

Robust Packet Scheduling in Wireless Cellular Networks

This paper addresses the following robust scheduling problem: Given that only coarse-grained channel state information (i.e., bounds on channel errors, but not the fine-grained error pattern) is available, how to design a robust scheduler that ensures worst-case optimal performance? To solve this problem, we consider two coarse-grained channel error models and take a zero-sum game theoretic approach, in which the scheduler and the channel error act as non-cooperative adversaries in the scheduling process. Our results show that in the heavy channel error case, the optimal scheduler adopts a threshold form. It does not schedule a flow if the price (the flow is willing to pay) is too small, in order to maximize the system revenue. Among the scheduled flows, the scheduler schedules a flow with a probability inversely proportional to the flow price such that the risk of being caught by the channel error adversary is minimized. We also show that in the mild channel error model, the robust scheduling policy exhibits a balanced trade-off between a greedy decision and a conservative policy. The scheduler is likely to take a greedy decision if it evaluates the risk of encountering the channel error adversary now to be small. Therefore, robust scheduling does not always imply conservative decision. The scheduler is willing to take “risks” to expect higher gain in some scenarios. Our solution also shows that probabilistic scheduling may lead to higher worst-case performance compared to traditional deterministic policies. Finally, the current efforts show the feasibility to explore a probabilistic approach to cope with dynamic channel error conditions.     

基于大数据的智能电网信息调度算法改进

在大数据时代，随着信息技术的发展，电网在运行过程中会产生海量的数据，且数据类型逐渐多样化，这对电网有着更高的要求，需要电网向着数字化、智能化电网转型。文中主要阐述了大数据智能电网信息调度算法，对目前的多种大数据智能电网信息调度算法进行了分析，并对基于大数据的智能电网信息调度算法提出了改进策略。     

Optimal and suboptimal packet scheduling over correlated time varying flat fading channels

We address the issue of optimal packet scheduling over correlated fading channels which trades off between minimization of three goals: average transmission power, average delay and average packet dropping probability. We show that the problem forms a weakly communicating Markov decision process and formulate the problem as both unconstrained and constrained problem. Relative value iteration (RVI) algorithm is used to find optimal deterministic policy for unconstrained problem, while optimal randomized policy for constrained problem is obtained using linear programming (LP) technique. Whereas with RVI only a finite number of scheduling policies can be obtained over the feasible delay region, LP can produce policies for all feasible delays with a fixed dropping probability and is computationally faster than the RVI. We show the structure of optimal deterministic policy in terms of the channel and buffer state and form a simple log functional suboptimal scheduler that approximately follows the optimal structure. Performance results are given for both constant and bursty Poisson arrivals, and the proposed suboptimal scheduler is compared with the optimal and channel threshold scheduler. Our suboptimal scheduler performs close to the optimal scheduler for every feasible delay and is robust to different channel parameters, number of actions and incoming traffic distributions.     

Lifetime analysis of wireless sensor nodes in different smart grid environments

Wireless sensor networks (WSNs) can help the realization of low-cost power grid automation systems where multi-functional sensor nodes can be used to monitor the critical parameters of smart grid components. The WSN-based smart grid applications include but not limited to load control, power system monitoring and control, fault diagnostics, power fraud detection, demand response, and distribution automation. However, the design and implementation of WSNs are constrained by energy resources. Sensor nodes have limited battery energy supply and accordingly, power aware communication protocols have been developed in order to address the energy consumption and prolong their lifetime. In this paper, the lifetime of wireless sensor nodes has been analyzed under different smart grid radio propagation environments, such as 500 kV substation, main power control room, and underground network transformer vaults. In particular, the effects of smart grid channel characteristics and radio parameters, such as path loss, shadowing, frame length and distance, on a wireless sensor node lifetime have been evaluated. Overall, the main objective of this paper is to help network designers quantifying the impact of the smart grid propagation environment and sensor radio characteristics on node lifetime in harsh smart grid environments.     

Power Optimization Strategy Considering Electric Vehicle in Home Energy Management System

With the development of smart grid, residents have the opportunity to schedule their household appliances (HA) for the purpose of reducing electricity expenses and alleviating the pressure of the smart grid. In this paper, we introduce the structure of home energy management system (EMS) and then propose a power optimization strategy based on household load model and electric vehicle (EV) model for home power usage. In this strategy, the electric vehicles are charged when the price is low, and otherwise, are discharged. By adopting this combined system model under the time-of-use electricity price (TOUP), the proposed scheduling strategy would effectively minimize the electricity cost and reduce the pressure of the smart grid at the same time. Finally, simulation experiments are carried out to show the feasibility of the proposed strategy. The results show that crossover genetic particle swarm optimization algorithm has better convergence properties than traditional particle swarm algorithm and better adaptability than genetic algorithm.     

基于Nash均衡的网格多调度节点的任务调度算法

 目前网格任务调度算法主要是针对1×n型即单调度节点多资源的网格环境,而针对m×n型的网格环境研究较少.论文用M/M/1排队系统对m×n型网格环境建模,然后以每个调度节点调度任务的平均完成时间为优化目标,提出了m×n型网格环境任务调度的Nash均衡问题,并利用粒子群算法求得该Nash均衡解.通过仿真验证了该算法在单位时间内平均完成的任务数,网络平均负载,以及系统的平均负载上均优于基于均匀调度策略的调度算法.     

An On-Line Scheduler over Hard Real-Time Communication System

By thorough research on the prominent periodic and aperiodic scheduling algorithms,anon-line hard real-time scheduler is presented,which is applicable to the scheduling of packets over a link.This scheduler,based on both Rate Monotonic,pinwheel scheduling algorithm Sr and Polling Serverscheduling algorithms,can rapidly judge the schedulability and then automatically generate a bus tablefor the scheduling algorithm to schedule the packets as the periodic packets.The implementation of thescheduler is simple and easy to use,and it is effective for the utilization of bus link.The orderly executionof the bus table can not only guarantee the performance of the hard real time but also avoid the blockageand interruption of the message transmission.So the scheduler perfectly meets the demand of hard real-time communication system on the field bus domain.     

Power efficient scheduling over fading channel for cross‐layer optimization

We consider the minimization of long‐term average power consumption for packet transmission between a mobile station and the base station over Nakagami‐m fading channel. Power consumption is minimized by intelligent transmission scheduling design, with the average queuing delay and joint packet loss across MAC and physical layers being confined below certain levels. The problem is formulated as an infinite horizon constrained Markov decision problem and solved by linear programming (LP) method. The primary intention of this paper is to provide a visible paradigm on using LP method to optimize the performance of mobile wireless communication systems. We elaborate the detailed mathematical solution with consistent simulation experiments and emphasize the effectiveness of adaptive transmission scheduling for cross‐layer QoS provisioning. Copyright © 2010 John Wiley & Sons, Ltd.     

Real Time Scheduling in TSCH for IoT Using Neurogenetic Algorithm

Internet of Things (IoT) is a widely adoptable technology in industrial, smart home, smart grid, smart city and smart healthcare applications. The real world objects are remotely connected through internet and it provides services with the help of friendly devices. Currently IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) standard is gaining a part of consideration among the IoT research community because of its effectiveness to improvise the reliability of communication which is orchestrated by the scheduling. As TSCH is an emerging Medium Access Control (MAC) protocol, it is used in the proposed work to enhance the network scheduling by throughput maximization and delay minimization. The paper focuses on proper utilization of the channel through node scheduling. NeuroGenetic Algorithm (NGA) has been proposed for TSCH scheduling and its performance is evaluated with respect to time delay and throughput. The system is implemented in real time IoT devices and results are perceived and analyzed. The proposed algorithm is compared with existing TSCH scheduling algorithms.

     

Renewable and Storage Energy Integration for Smart Grid Housing

This paper presents the design,implementation and testing of an embedded system that integrates solar and storage energy resources to smart homes within the smart microgrid.The proposed system provides the required home energy by installing renewable energy and storage devices.It also manages and schedules the power flow during peak and off-peak periods.In addition,a two-way communication protocol is developed to enable the home owners and the utility service provider to improve the energy flow and the consumption efficiency.The system can be an integral part for homes in a smart grid or smart microgrid power networks.A prototype for the proposed system was designed,implemented and tested by using a controlled load bank to simulate a scaled random real house consumption behavior.Three different scenarios were tested and the results and findings are reported.Moreover,data flow security among the home,home owners and utility server is developed to minimize cyber-attacks.     

高安全高性能RSA协处理器的设计与实现

为保证智能电网中的数据安全,防止电力通信过程中的数据被篡改,安全芯片的应用必不可少,而RSA算法是安全芯片中应用最广泛的公钥算法之一。RSA算法复杂度高,硬件实现功耗较大,在设计的过程中常常无法完全兼顾性能、功耗、安全性等各个方面。文章设计了一种高性能、能抵抗常见侧信道攻击及EMA电磁攻击的高安全RSA协处理器。提出的随机存储模幂算法真伪运算结果的防护策略,增强了协处理器抵抗侧信道攻击、差分功耗攻击以及EMA电磁攻击的能力。通过两个层级的算法优化来提升协处理器性能,并通过结合CIOS平方算法和Karatsuba算法的改进的Montgomery模乘算法,使得1 024位带防护的RSA算法在UMC 55 nm工艺下的面积为4.8万门@30 MHz,功耗为4.62 mW@30 MHz, FPGA开发板上进行API测试的性能为709.3 kbit/s。     

The Effect of On-Off Scheduling to the Required Transmit Power in Systems with Fast Power Control

Physical layer channel-aware scheduling may significantly improve coverage and throughput of IP based services in wireless cellular networks, and the feasibility of such schedulers is actively studied within 3G and 4G systems. A channel-aware scheduler requires access to instantaneous channel state information in order to direct transmission to users with favorable channel conditions. In frequency division duplex (FDD) systems, this requires a fast feedback channel between mobile and base stations, and the overhead of the feedback control channel should be kept as low as possible.In this paper, we study the effect of control channel overhead to on-off scheduling (OOS) when fast transmit power control is applied in data and control channels. On-off scheduling is a simple channel-aware scheduling algorithm, where transmission to a user is suspended if the transmit power exceeds a given threshold. On-off scheduling is applied on the data channel while control channel is always on so that the scheduler is able to obtain channel state information from active users. The gain of OOS strongly depends on the power ratio between control and data channels, and increased interference due to control signaling and decreased interference due to channel-aware scheduling should be jointly considered in system design. Gains in the required transmit power are translated into gains in coverage and capacity assuming WCDMA parameters, and the results can be applied, e.g., when designing scheduling algorithms and corresponding signaling formats for WCDMA uplink.     

Structured Threshold Policies for Dynamic Sensor Scheduling—A Partially Observed Markov Decision Process Approach

We consider the optimal sensor scheduling problem formulated as a partially observed Markov decision process (POMDP). Due to operational constraints, at each time instant, the scheduler can dynamically select one out of a finite number of sensors and record a noisy measurement of an underlying Markov chain. The aim is to compute the optimal measurement scheduling policy, so as to minimize a cost function comprising of estimation errors and measurement costs. The formulation results in a nonstandard POMDP that is nonlinear in the information state. We give sufficient conditions on the cost function, dynamics of the Markov chain and observation probabilities so that the optimal scheduling policy has a threshold structure with respect to a monotone likelihood ratio (MLR) ordering. As a result, the computational complexity of implementing the optimal scheduling policy is inexpensive. We then present stochastic approximation algorithms for estimating the best linear MLR order threshold policy.     

大数据背景下智能电网动态电力监控研究

本文针对我国电力使用过度集中造成的电力不稳定,供电压力骤增以及电力闲置造成电力浪费等主要问题,通过研究在大数据这一背景下如何有效实行智能电网动态电力监控,如何运用同步相量测量单元、高级计量架构等关键技术方法优化配用电网系统并提升电力供应的质量和可靠性,通过利用实时反馈的用电量及价格,控制消费者的用电量,将用电量控制在一个稳定的范围内,有效提高电网终端用电效率,平滑电网负荷曲线,降低电网负荷压力及电能损耗.