The smart grid is conceived of as an electric grid that can deliver electricity in a controlled, smart way from points of generation to active consumers. Demand response (DR), by promoting the interaction and responsiveness of the customers, may offer a broad range of potential benefits on system operation and expansion and on market efficiency. Moreover, by improving the reliability of the power system and, in the long term, lowering peak demand, DR reduces overall plant and capital cost investments and postpones the need for network upgrades. In this paper a survey of DR potentials and benefits in smart grids is presented. Innovative enabling technologies and systems, such as smart meters, energy controllers, communication systems, decisive to facilitate the coordination of efficiency and DR in a smart grid, are described and discussed with reference to real industrial case studies and research projects.

Demand response and smart grids—A survey

Traditional power grids are being transformed into smart grids (SGs) to address the issues in the existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability, and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution, and utilization systems. SGs employ various devices for the monitoring, analysis, and control of the grid, deployed at power plants, distribution centers, and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation, and the tracking of such devices. This is achieved with the help of the Internet of Things (IoT). The IoT helps SG systems to support various network functions throughout the generation, transmission, distribution, and consumption of energy by incorporating the IoT devices (such as sensors, actuators, and smart meters), as well as by providing the connectivity, automation, and tracking for such devices. In this paper, we provide a comprehensive survey on the IoT-aided SG systems, which includes the existing architectures, applications, and prototypes of the IoT-aided SG systems. This survey also highlights the open issues, challenges, and future research directions for the IoT-aided SG systems.

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Internet of Things-Aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

In this study, a significant literature review on peak load shaving strategies has been presented. The impact of three major strategies for peak load shaving, namely demand side management (DSM), integration of energy storage system (ESS), and integration of electric vehicle (EV) to the grid has been discussed in detail. Discussion on possible challenges and future research directions for each type of the strategy has also been included in this review. For the energy storage system, different technologies used for peak load shaving purpose, which include their methods of operation and control have been elaborated further. Finally, the sizing of the ESS storage system is discussed. For the demand side management system, various management methods and challenges associated with DSM implementation have been thoroughly explained. A detailed discussion on the electric vehicle strategy has also been included in the review, which considers the integration, control and operation techniques for implementing the peak load shaving.

A review on peak load shaving strategies

The growth of advanced metering infrastructure, enhanced communication infrastructure in power grids, and the ability of end-user consumer to actively participate helps in realizing vision of sustainable energy systems. Demand response (DR) programs are developed in order to deploy this ability and make power grids more efficient, environmental friendly, and reliable. This paper presents a review of DR, existing application and a possible implementation strategy in a smart grid environment. Furthermore, classification and status of DR programs in different U.S. electricity markets have been also discussed.

Demand response for sustainable energy systems: A review, application and implementation strategy

https://dr.ntu.edu.sg/bitstream/10356/81241/1/The%20internet%20of%20energy_Smart%20sensor%20networks%20and%20big%20data%20management%20for%20smart%20grid.pdf

The Internet of Energy: Smart Sensor Networks and Big Data Management for Smart Grid

Demand response (DR) has an important role to play in the electricity market for maintaining the balance between supply and demand by introducing load flexibility instead of only adjusting generation levels, at almost all operational time scales. There are many players in the market who benefit from DR, like the TSO, DSOs, retailers and end-customers themselves. The recent advent of smart grid technologies advanced the integration of DR by providing the needed information and communication infrastructure to the existing grid. Available literature on DR talks about the concept and definitions of DR, possible DR models for various region-specific market structures along with few DR implementation experiences in a system with ever increasing levels of loads along with evolution of innovative technologies like renewables, micro-grids, PEVs, etc. In this paper, the available literature on DR is categorized into general concept papers and papers on DR models applicable to the wholesale or retail markets, and are presented in a precise manner.

Review of demand response under smart grid paradigm

In this study, a ‘two-stage’ deterministic algorithm an for efficient transmission expansion planning with renewable energy (RE) resources under the assumption that existing conventional generators provide the reserve to mitigate RE generation forecast error has been proposed. Zero-RE penetration has been considered as a ‘reference scenario’, as well as cost-minimisation objective has been considered as a planning criterion in Stage 1. In the proposed algorithm, Stage 2 is required to be solved only if the network performance degrades in relation to the reference scenario. In Stage 2, congestion cost is also incorporated as a sub-objective. Here, the locational marginal prices of the unit scheduling problem obtained from the solution of Stage 1 have been used to calculate the congestion cost. In addition to being computationally tractable, the proposed algorithm guarantees performance improvement in the network. The planning horizon is divided into smaller blocks to facilitate delayed investment. When implemented on an IEEE 24-bus reliability test system, the proposed algorithm generated minimum cost plan with better performance.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rpg.2016.0085

Two-stage algorithm for efficient transmission expansion planning with renewable energy resources

In recent times, a significant level of advancement has been observed in Distributed Generation (DG) technologies and in its deployment. This scenario demands a mandatory requirement of effective islanding detection techniques to avoid possible damages to the system. This paper proposes an advanced islanding detection method for inverter based DGs using wavelet transform and machine learning. The method uses wavelet transform decomposition to extract the detailed coefficients and are then processed to identify typical transient variations during islanding. K-Nearest Neighbor (KNN) is used to classify the condition as islanding or non-islanding. The method will reduce the Non-Detection Zone (NDZ) and effective detection time. MATLAB/SIMULINK simulations are done on test system to validate the proposed method.

Islanding Detection of Inverter based Distributed Generations using Wavelet Transform and KNN

SVM-based anti-islanding protection for DER near a modern industrial premise

The proposition of Electric Vehicles (EVs) for mobility is gaining prominence due to foreseen depletion of fossil fuels as well as the ease of charging them at home or in parking lots from renewable energy sources. Aggregators with smart communication facilitate the utilization of batteries of EVs as portable power plants for emergency and ancillary services as well as for daily load scheduling which helps to increase the reliability of the existing grid. But the penetration of such power sources from the distribution side should also ensure the stability of the existing power system. This paper investigates the voltage stability of a power system with participation of EVs in grid scheduling. A cost model of battery of EV to schedule the available active and reactive power is proposed. A hierarchical control strategy to aggregate the capacities of EV batteries is also proposed in this paper. Optimal scheduling of modified IEEE 9 bus test system is done in MATPOWER with EVs as virtual power plants (VPP). To evaluate the voltage stability of the system, L-indices of the load buses with and without the participation of EVs are determined using MATLAB.

R. M. Shereef

Papers

Review of demand response under smart grid paradigm

Two-stage algorithm for efficient transmission expansion planning with renewable energy resources

Islanding Detection of Inverter based Distributed Generations using Wavelet Transform and KNN

SVM-based anti-islanding protection for DER near a modern industrial premise

Voltage Stability Assessment of Transmission System with Electric Vehicles as Virtual Power Plants