Smart grids are the backbone of existing energy production and supply scenario in today’s society. Gradual increasing demand of energy has certainly emphasized the significance and provenance of reliability and predictive aspects over smart grids. Conventional smart grids under perform in terms of self-aware behavior, especially in delay tolerance, energy requirement, and dissemination of monitoring notions. Highly distributed geographical distribution sometimes cause problems for smart grids to provide necessary services to both the consumers and prosumers. Internet of Things (IoT) is thus integrated with smart grids to facilitate distributed monitoring services for smooth running of the smart grid. Although, IoT has remarkably supported smart grids to perform smarter than ever, it lacks in security, decentralization, transparency, and trust-less approaches. Thus, blockchain is envisaged to leverage minimizing such gaps and to pave new horizon in the blockchain-IoT enabled smart grid monitoring. In this paper, we investigate how blockchain and IoT together can improve existing smart grid ecosystem toward facilitation of better monitoring services. We do this via a Systematic Literature Review. Firstly, we present preliminaries behind the study, followed by in-depth review of different domains of IoT-based smart grid monitoring. Next, we discuss various attributes of blockchain-IoT derived smart grid management schemes. Then, we discuss possible opportunities and benefits of using blockchain with respect to blockchain-IoT based smart grid monitoring. Finally, we illustrate open research challenges and future directions in the aforementioned aspects. The article concludes that with certain changes in current blockchain technology, it can surely encompass the direction of enhanced monitoring of IoT based smart grid.

Reviewing the opportunities, challenges, and future directions for the digitalization of energy

The electrification of transportation has been developed to support energy efficiency and CO2 reduction. As a result, electric vehicles (EVs) have become more popular in the current transport system to create more efficient energy. In recent years, this increase in EVs as well as renewable energy resources (RERs) has led to a major issue for power system networks. This paper studies electrical vehicles (EVs) and their applications in the smart grid and provides practical solutions for EV charging strategies in a smart power system to overcome the issues associated with large-scale EV penetrations. The research first reviews the EV battery infrastructure and charging strategies and introduces the main impacts of uncontrolled charging on the power grid. Then, it provides a practical overview of the existing and future solutions to manage the large-scale integration of EVs into the network. The simulation results for two controlled strategies of maximum sensitivity selection (MSS) and genetic algorithm (GA) optimization are presented and reviewed. A comparative analysis was performed to prove the application and validity of the solution approaches. This also helps researchers with the application of the optimization approaches on EV charging strategies. These two algorithms were implemented on a modified IEEE 23 kV medium voltage distribution system with switched shunt capacitors (SSCs) and a low voltage residential network, including EVs and nonlinear EV battery chargers.

An insight into practical solutions for electric vehicle charging in smart grid

The concept of smart grid has been realized in the last few decades with the evolution of metering from mechanical meter to electronic meter and has progressed from Automated Meter Reading (AMR) to Advanced Metering Infrastructure (AMI). Smart grids are those networks that carries and delivers electricity based on specific smart technologies. Smart grid overcomes the limitations of conventional grids with the incorporation of smart meters in the grid through AMI. Smart meter can be defined as an energy meter that measures energy consumption with many incorporated smart features like demand side management, demand response, load control, load curtailment, etc. Smart meters forms the backbone of a smart grid imparting all of its smart technologies to the smart grid. The development of smart meter with Indian Standards is described in this paper.

Development of Indigenous Smart Energy Meter adhering Indian Standards for Smart Grid

A major challenge in renewable energy planning and integration with existing systems is the management of intermittence of the resources and customer demand uncertainties that are attributed to climates. In emerging distributed grids, state-of-the-art optimization techniques were used for cost and reliability objectives. In the existing literature, power dispatch and demand side management schemes were implemented for various techno-economic objectives. In renewable energy-based distributed grids, power dispatch is strategic to system operations. However, demand side management is preferred, as it allows more options for customer participation and active management of energy in buildings. Moreover, the demand side management can simply follow supplies. This paper investigates the implications of demand side management as it affects planning and operations in renewable energy-based distributed grids. Integration of demand side management in customer-oriented plans such as the time-of-use and real-time-pricing on residential and commercial demands is conceptualised to ensure effective customer participation which maintains the valued comforts. Moreover, the optimised tariff integrated demand side management implementations based on the utility-initiated demand response programmes are envisaged to offset conflicting objectives of the economy and customer comforts within residential and commercial demands and are also viewed as a step towards efficient management of energy in buildings. 

/pdf/a-comprehensive-review-of-demand-side-management-in-3s713yux.pdf

A comprehensive review of demand side management in distributed grids based on real estate perspectives

With the development of smart grid technology, residents can schedule their power consumption pattern in their home to minimize electricity expense, reducing peak-to-average ratio (PAR) and peak load demand. The two-way flow of information between electric utilities and consumers in smart grid opened new areas of applications. In this chapter, the general architectures of the home energy management systems (HEMS) are introduced in a home area network (HAN) based on the smart grid scenario. Efficient scheduling methods for home power usage are discussed. The energy management controller (EMC) receives the demand response (DR) information indicating the Time-of use electricity price (TOUP) through the home gateway (HG). With the DR signal, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG.

Smart Home Energy Management System

The demand side management (DSM) comprises techniques and policies which aim at equalizing energy consumption levels over the day. As opposed to the supply side management involving the addition of new generation units and total installed capacity, the idea here is not only to increase the energy to be supplied, but also to control the shape of consumption by applying energy management techniques. The main challenge in the implementation of a DSM program is the quest for knowledge of the daily behavior of loads in the electrical system, which is generally not available from the systems based on conventional electromechanical meters. In such a scenario, the emergence of novel technologies like Smart Grid technology, creates an environment for convergence between the infrastructures of generation, transmission, distribution, information technology and digital communication infrastructure which enables the exchange of information and control actions among the various segments of the power grid. The paper throws light on the research trends within the area of demand side management in a smart grid environment and proposes a scheduling scheme using genetic algorithm for load management. Simulation results confirm that the proposed algorithm efficiently reduces the PAR and electricity consumption cost.

Demand side management in a smart grid environment

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Demand side management in a smart grid environment