A review of demand response in an efficient smart grid environment

As the electricity system shifts from a conventional operating system into a smart grid system, the paper proposes an efficiency-enhancing mechanism comprised of communication-based demand response (CBDR) and a customer-friendly inclining block tariff (IBT) that takes into consideration customers’ income and consumption profiles.     

Typification of load curves for DSM in Brazil for a smart grid environment

The deployment of a smart grid environment is a worldwide trend and generates of a large volume of data. The load curve for each consumer in real time is an example of this. The challenge is the transformation of these data into useful information that may help to improve efficiency in the management, planning and operation of the power grid. The implementation of demand side management (DSM) requires an analysis of the data generated in a smart grid environment to determine which policies are most appropriate for each type of consumer. Because of the large number of customers, the application of these policies involves the selection of patterns for the load curve.This study discusses the use of DSM in a smart grid environment in Brazil and presents the simulation for creating load curve patterns using the k-means technique from the consumer data of a concessionaire for the Brazilian electric system. The result obtained in this research is the creation of the load curve patterns for selecting the policies of DSM.     

Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support

Demand fluctuation in electric power systems is undesirable from many points of view; this has sparked an interest in demand-side strategies that try to establish mechanisms that allow for a flatter demand curve. Particularly interesting is load shifting, a strategy that considers the shifting of certain amounts of energy demand from some time periods to other time periods with lower expected demand, typically in response to price signals.In this paper, an optimization-based model is proposed to perform load shifting in the context of smart grids. In our model, we define agents that are responsible for load, generation and storage management; in particular, some of them are electric vehicle aggregators. An important feature of the proposed approach is the inclusion of electric vehicles with vehicle-to-grid capabilities; with this possibility, electric vehicles can provide certain services to the power grid, including load shifting and congestion management. Results are reported for a test system based on the IEEE 37-bus distribution grid; the effectiveness of the approach and the effect of the hourly energy prices on flattening the load curve are shown.     

A new smart charging method for EVs for frequency control of smart grid

Nowadays, due to the shortage of fossil fuels on the one hand and their high prices on the other hand, using electric vehicles (EVs) has been increased. Charging of EVs has imposed new loads on power systems. These new and major loads have faced the frequency control and stability of power systems with new challenges. One way to deal with this new challenge is smart charging of EVs. In this method, grid condition is a key parameter that affects the charging of EV. In other words, in smart charging method, charging is performed with respect to power system parameters such as frequency. In this paper, a smart charging method based on fuzzy controller is proposed, in which charging process is performed with respect to the frequency deviation of grid and state of charge (SOC) of EV battery. To evaluate the performance of the proposed controller in control of grid frequency, IEEE 39-bus system in the presence of renewable energy sources is considered as test system. In order to the frequency analysis, this system is converted into a three-area system and, for each area, several EV categories with different numbers of EVs, battery capacity, start time of charging, and initial SOC are supposed. Moreover performance of proposed method is compared with an optimized PI controller in terms of frequency control. To investigate performance of proposed method in charging of EVs, a two area system is assumed and charging of EVs is verified by applying step loads to both areas. Simulations are carried out in MATLAB/SIMULINK environment. Results of the simulations reveal the good performance of the proposed controller in terms of frequency control of grid and charging of EVs.     

New development in relay protection for smart grid

This series of papers report on relay protection strategies that satisfy the demands of a strong smart grid. These strategies include ultra-high-speed transient-based fault discrimination, new co-ordination principles of main and back-up protection to suit the diversification of the power network, optimal co-ordination between relay protection and auto-reclosure to enhance robustness of the power network. There are also new development in protection early warning and tripping functions of protection based on wide area information. In this paper the principles, algorithms and techniques of single-ended, transient-based and ultra-high-speed protection for EHV transmission lines, buses, DC transmission lines and faulty line selection for non-solid earthed networks are presented. Tests show that the methods presented can determine fault characteristics with ultrahigh-speed (5 ms) and that the new principles of fault discrimination can satisfy the demand of EHV systems within a smart grid.     

A nonlinear control method for price-based demand response program in smart grid

This paper proposes a price-based demand response program by the nonlinear control method. The demand response program is formulated as a nonlinear power management system with price feedback. We give the conditions of the price parameters for both the global asymptotic stability of the system and the social welfare optimality of the equilibrium point. Furthermore, the system is shown to be input-to-state (ISS) stable when there are additive disturbances on the power measurements and the price, and the discrete-time implementation of the power management system is given. Simulation results demonstrate the balance between supply and demand and the stability of the system with and without disturbances.     

Combined environmental and economic dispatch of smart grids using distributed model predictive control

This paper presents an extended distributed model predictive control (DMPC) framework and its application to a smart grid case study. Specifically, a combined environmental and economic dispatch (EED) problem is formulated and solved, which is a non-trivial multi-objective optimization problem given the high number of agents, information exchanges and constraints associated to large-scale smart grids.In this line, the work proposed herein adopts a distributed Lagrange-based model predictive control with reduced computational demand making use of robust mixed-integer quadratic programming (MIQP) solvers. In addition, the model predictive control (MPC) nature of the framework accounts for renewable resource forecast while physical constraints are included in the formulation. The DMPC is herein extended to calculate market-based on-line energy pricing while minimizing the generation cost and emissions,and to include hard and soft constraints and ramp rate limits.The aforementioned control framework is applied to a smart grid composed of 11 consumer centers, 6 energy storages, 11 generation systems and 31 transmission lines. Simulation results show reductions of generation costs up to 40% when predictions are included in the formulation. Furthermore, the simulation of forecast errors results in up to 8% generation overcost. These results show that DMPC can be considered as an alternative versus other heuristic methods, which do not guarantee an optimal solution to the problem.     

A network scheme for process bus in smart substations without using external synchronization

The development of smart substations plays a crucial role in the development of smart grid. The major difference between a smart substation and an existing conventional substation lies in the process bus. Based on a thorough study over the network schemes of sampled values for process bus in smart substations, this paper summarizes and analyses major characteristics of existing network schemes. In order to overcome shortcomings of existing network schemes in time synchronization, the paper proposes a novel network scheme for process bus in smart substations without using external synchronizing clock. Compared to conventional network schemes, the proposed scheme does not need an external global synchronizing clock, thus can significantly improve the reliability of the process bus network. As a major research output of the key technological project, this work has been successfully applied to the 110 kV Shenxiang Substation in Suzhou, China.     

基于博弈论的智能电网与需求侧交互管理策略

针对智能电网与需求侧进行双向交互时,电网与需求侧的效益难以同时兼顾的问题,提出了一种基于博弈论的智能电网与需求侧交互管理策略。该策略利用斯塔克伯格(Stackelberg)博弈使电网与需求侧之间的对抗策略始终处于纳什均衡状态,从而确保双方的利益最大化。研究了需求基线预测不准确对交互策略的影响,并提出了增强型交互策略来应对不确定性。以某园区微电网为例进行仿真分析,验证了该策略的有效性。结果表明,所提策略可使电网的净利润增加8%,需求波动降低40%左右,需求侧节约电费2.5%~8.3%左右。所提交互策略可有效地缓解需求基线预测不准确时带来的负面影响。     

Quantifying economic benefits of second life batteries of gridable vehicles in the smart grid

Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV) with vehicle-to-grid capability, referred to as “gridable vehicles” (GVs), have become a useful choice for storage devices in the smart grid environment. The success of a smart grid system with GVs heavily depends on the market penetration of GVs and their participation rate in the vehicle-to-grid (V2G) program. Battery lifetime and revenue earning potential are influential factors for GV owners considering whether to participate in the V2G program. Second use of GV batteries, after their automotive life, can generate some revenue for the owners to recover a part of their battery purchase cost. However, this is dependent on the remaining capacity of the battery and the capacity degradation rate both in the automotive and second life. In this paper, capacity degradation and the remaining energy of a GV battery at different operating cycles have been quantified in both their automotive and second lives. Cost of battery energy both in automotive and second life have also been modelled that informs the owners of the revenue potentials, especially from the second life use. Finally, an economic load dispatch model with the inclusion of second life revenue has been developed to establish that using GV batteries in this way would earn extra revenue thus contributing to the initial buying price and encouraging more GV participation in the smart grid. Simulation results show that the proposed model can contribute up to 19.56% of the initial battery purchase cost, while still ensuring economic load dispatch.     

Distributed machine learning for energy trading in electric distribution system of the future

Machine Learning (ML) has seen a great potential to solve many power system problems along with its transition into Smart Grid. Specifically, electric distribution systems have witnessed a rapid integration of distributed energy resources (DERs), including photovoltaic (PV) panels, electric vehicles (EV), and smart appliances, etc. Electricity consumers, equipped with such DERs and advanced metering/sensing/computing devices, are becoming self-interested prosumers who can behave more actively for their electric energy consumption. In this paper, the potential of distributed ML in solving the energy trading problem among prosumers of a future electric distribution system - building DC grid cell, is explored, while considering the limited computation, communication, and data privacy issues of the edge entities. A fully distributed energy trading framework based on ML is proposed to optimize the load and price prediction accuracy and energy trading efficiency. Computation resource allocation, communication schemes, ML task scheduling, as well as user sensitive data preserving issues in the distributed ML framework are addressed with consideration of all the economic and physical constraints of the electric distribution systems.     

Multi-agent systems for reactive power control in smart grids

Power system monitoring is the most fundamental prerequisite for making reasonable decisions in a control center. In recent years, with the advent of smart grids, monitoring and control of reactive power in power systems has entered a new era. This issue have highlighted the need for effective methods of decentralized optimal reactive power control (DORPC), especially based on the new available frameworks which have emerged along with the smart grids. This paper presents a novel approach to the DORPC problem based on a Holonic architecture. The Holonic architecture has some unique features by which the objective of the DORPC problem could be optimized through a timely proposed strategy. In order to demonstrate the features of the proposed approach, it is compared with two other methods considering a set of indices. Accordingly, the proposed approach has great potential to reduce the active power losses and to fully exploit the available reactive power resources. It requires a limited set of data while improves the fault tolerance of the network.     

Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review

Meteorological changes urge engineering communities to look for sustainable and clean energy technologies to keep the environment safe by reducing CO2 emissions. The structure of these technologies relies on the deep integration of advanced data-driven techniques which can ensure efcient energy generation, transmission, and distribution. After conducting thorough research for more than a decade, the concept of the smart grid (SG) has emerged, and its practice around the world paves the ways for efcient use of reliable energy technology. However, many developing features evoke keen interest and their improvements can be regarded as the next-generation smart grid (NGSG). Also, to deal with the non-linearity and uncertainty, the emergence of data-driven NGSG technology can become a great initiative to reduce the diverse impact of non-linearity. This paper exhibits the conceptual framework of NGSG by enabling some intelligent technical features to ensure its reliable operation, including intelligent control, agent-based energy conversion, edge computing for energy management, internet of things (IoT) enabled inverter, agent-oriented demand side management, etc. Also, a study on the development of data-driven NGSG is discussed to facilitate the use of emerging data-driven techniques (DDTs) for the sustainable operation of the SG. The prospects of DDTs in the NGSG and their adaptation challenges in real-time are also explored in this paper from various points of view including engineering, technology, et al. Finally, the trends of DDTs towards securing sustainable and clean energy evolution from the NGSG technology in order to keep the environment safe is also studied, while some major future issues are highlighted. This paper can ofer extended support for engineers and researchers in the context of data-driven technology and the SG.     

Framework for minimum user participation rate determination to achieve specific demand response management objectives in residential smart grids

In this paper, we develop a framework for grid operators to determine the minimum required user participation rate to achieve specific Demand Response Management (DRM) objectives in residential smart grids. Under this framework, we propose power limit and inconvenience limit methodologies. In the power limit methodology, the grid communicates a power limit to participating users to achieve a desired amount of peak load reduction. In the inconvenience limit methodology, the grid communicates an inconvenience limit to participating users and demand is reduced accordingly to achieve the desired peak load reduction. We define indexes to measure user inconvenience and recommend physical and thermal load models for flexible appliances to measure temperature deviations and scheduling delays. We then analyze the performance of our developed framework in a residential community comprising of 780 homes. We generate power consumption data for individual devices of different users to match an aggregated load curve shape from the RELOAD database on a typical summer day. We determine the required user participation, experienced inconvenience temperature variations and scheduling delays for various DRM objectives. By determining the minimum required user participation, this framework can help the grid operators in designing proper incentives for specific DRM goals.     

Improved-ELM method for detecting false data attack in smart grid

Power grid is a complex system which closely links the power generation and power consumer through transmission and distribution networks. With the development of smart grid, smart grid is more open to external communication systems, it also has exposed some problems in the network attacks. A new false data injection attack (called the unobservable attack) that can bypass the traditional BDD and inject random errors into state estimation. We propose an improved extreme learning machine (ELM) for attack detection. The artificial bee colony (ABC) incorporates the thought of differential evolution algorithm (DE) to optimize ELM for improving detection precision. In this paper, Autoencoder is used to reduce the dimensionality of the measurement data, which makes the low-dimensional data information basically and fully represent high-dimensional data. We verify the performance of the proposed method on IEEE bus systems, and prove that the proposed method can effectively detect such unobservable attack.     

Valuing distributed energy resources: Combined heat and power and the modern grid

In this paper we examine how decision makers can more precisely assess the costs of disruptive weather events and the value of resilient distributed energy systems such as combined heat and power (CHP). CHP makes up a small percentage of the energy infrastructure in the United States despite its substantial efficiency and resilience benefits. In part this is because the resilience value of CHP is not fully accounted for in energy infrastructure project cost screenings. To capture this benefit in investment decisions, we propose a framework for a metric called the Distributed Energy Resource Resiliency Value (DERRV) and discuss how such a metric might be applied to CHP.     

智能电网框架下电动汽车智能充电系统设计研究

近年来,电动汽车和插电式电动汽车（Plug-in electric vehicles, PEVs）的销量稳步增长,充电桩数量急剧增加,这一现象可能会给电网和当地配电电路和变压器造成负担,而且可能会导致电力公司的成本增加。文中介绍了智能电网系统的设计和实现逻辑,使电动汽车、PEV与电网之间能够实现动态交互。这种智能和动态的交互能够减轻电网负荷,从而最大限度地减少成本,避免负载过大造成的损害。本文以开发算法为重点,提出了完整的系统设计,该系统充电速度快、效率高,可以成功地用于减轻电网的负担。     

智能电能表数据安全隐患多层次分析与系统设计

电能计量装置是智能电网的核心部分,对用户和电力企业提供多种先进功能,但多数电能计量装置缺乏安全性保护。针对此问题,提出了一种安全漏洞多层次分析与系统设计方案。该系统考虑了大量用电日志,以便向用户提供详细信息,允许其有效识别出用电高峰时段。使用安全文件传输协议(FTP)将用电数据从智能电能表传递至电力企业服务器,以确保数据传输的隐私性和安全性。系统中还使用了加密技术,通过传输层安全(TLS)/安全套接字层(SSL)来保护用户与Web门户之间的通信。通过对系统强化等分析表明,与同类系统相比,所提系统设计具有较大的安全性优势。     

考虑分布式可再生电源和储能的智能配电网可靠性评估综述

储能是提高消纳高比例分布式可再生能源(DRG)的有效措施,是智能电网发展的重要组成部分之一.分布式可再生能源和储能的快速发展给配电网安全运行和供电可靠性带来巨大挑战和机遇,为此国内外学者近几年开展了大量研究,是目前热点研究领域之一.文中对计及DRG和储能的配电网可靠性评估研究进行系统分析,重点介绍DRG和储能对配电网可...     

基于RFID技术的智能电网MIS的构建

RFID射频识别是一种非接触式的自动识别技术,它通过射频信号自动识别目标对象并获取相关数据,识别工作无需人工干预,可工作于各种恶劣环境。RFID射频识别技术在智能电网中的应用是网络技术发展到一定程度的必然产物。该技术的应用,可以有效地提高智能电网中各项电力指标的识别和测定。在此基础之上的信息管理系统,更可以有效地对电力系统基础设施资源进行整合,进而提高电力系统通信水平,改善当前电力系统基础设施的利用率。