Emerging Smart Grid Technologies Research Articles

Review on the Electric Spring Connected to the Renewable Energy System to Minimize Harmonics and Voltage Profile Enhancement

The emerging smart grid technology known as Electric Spring (ES) has been employed previously to maintain voltage and power stability in renewable energy-based grids that are minimally regulated or self-contained. This study thoroughly investigated the foundational theories, modeling approaches, practical uses, and restrictions associated with Electric Springs by analyzing existing Scholarly works in this domain Electric Springs implement a direct power control approach. solution for the next-generation single-phase Systems, addressing the limitations of current ES control methods. This solution aims to improve power management in microgrids, where integrating renewable energy sources (RESs) poses challenges due to the unpredictable power output. The challenge involves managing two aspects: the variable Power consumed by ES and the power fed into the grid. The suggested straightforward and accurate signal manipulation method, namely, ANN-based Electric Spring (ES), is set to be deployed. This technique can adeptly manage these aspects in both stable conditions and during transitions in Renewable Energy Sources (RES).

Cooperative Voltage-Balancing Control for Medium-Voltage Electric–Spring System Based on Diode-Clamped Converter and Dual Active Bridges

Electric spring (ES) is an emerging smart grid technology for stabilizing the voltage and frequency in a power network. Being fast demand-side-management technology, ES has recently been extended to the medium-voltage (MV) level in an AC-DC power conversion system that connects the distribution line of 6.6/11/22 kV to a DC grid of 800 V. To interface with the MV power grid, a diode-clamped converter (DCC) and multiple dual active bridges (DABs) can be employed to construct the MV-ES system. The balance of the capacitor voltages is crucial for DCCs, and thereby also for the MV-ES system. Conventional voltage-balancing approaches for DCCs generally compromise the quality of the grid-side voltage and current, which is undesirable especially in high-voltage high-power occasions. In this paper, a voltage-balancing control solution is proposed for the MV-ES system via cooperation of the DCC and DABs. By taking advantage of the DABs in voltage balancing, the DCC can adopt simple modulation and ensure grid-friendly voltage and current injection into the grid. The proposed voltage-balancing solution is verified using simulations in a five-level system and with practical experiments for a three-level system.

Pave the way for sustainable smart homes: A reliable hybrid AC/DC electricity infrastructure

The development of emerging smart grid technologies has led to more and more penetration of renewable energy resources and electric energy storage in the residential sectors. Besides, owing to the significant evolution of power electronic devices, there is a rapid growth in penetration of DC loads and generations, such as PV and electric vehicles (EVs), into the buildings and homes as a building block of the future smart cities. This is despite the fact that the electricity infrastructure of the conventional buildings is designed based on AC electricity and as a result, there would be a lot of losses due to the frequent power conversion from AC to DC and vice versa. Besides, according to a significant amount of energy consumption in the residential sector, buildings have a prominent role to confront environmental problems and obtain sustainability. In such circumstances, and considering the energy outlook, rethinking the electrification structure of the built environment is necessary. This work is an effort in this regard and looks for a sustainable energy infrastructure for the cyber–physical homes of the future. Three disparate electrification architectures are analyzed. The proposed framework, which is formulated as a mixed-integer linear programming (MILP) problem, not only considers costs associated with investment and operation but also evaluates the reliability of each structure by considering the different ratios of DC loads. Moreover, the optimal size of renewable energy resources and the effect of EV demand response, and different prices of PV and battery are precisely investigated. The efficacy of the proposed approach is evaluated via numerical simulation.

A two‐stage service restoration method for electric power distribution systems

Improving the reliability of power distribution systems is critically important for both utilities and customers. This calls for an efficient service restoration module within a distribution management system to support the implementation of self-healing smart grid networks. Although the emerging smart grid technologies, including distributed generators (DGs) and remote-controlled switches, enhance the self-healing capability and allow faster recovery, they still pose additional complexity to the service restoration problem, especially under cold load pickup (CLPU) conditions. Herein, a novel two-stage restoration framework is proposed to generate a restoration solutions with a sequence of control actions. The first stage generates a restoration plan that supports both the traditional service restoration using feeder reconfiguration and the grid-forming DG-assisted intentional islanding methods. The second stage generates an optimal sequence of switching operations to bring the outaged system quickly to the final restored configuration. The problem is formulated as a mixed-integer linear program that incorporates system connectivity, operating constraints, and the CLPU models. It is demonstrated that on using a multi-feeder test case, the proposed framework is effective in utilizing all available resources to quickly restore the service and generate an optimal sequence of switching actions to be used by the operator to reach the desired optimal configuration.

Energy Trading in Local Electricity Market With Renewables—A Contract Theoretic Approach

Emerging smart grid technologies and increased penetration of renewable energy sources (RESs) direct the power sector to focus on RESs as an alternative to meet both baseload and peak load demands in a cost-efficient way. A key issue in such schemes is the design and analysis of energy trading techniques involving complex interactions between an aggregator and multiple electricity suppliers (ESs) with RESs fulfilling a certain demand. This is challenging because ESs can be of various categories, such as small/medium/large scale, and they are self-interested and generally have different preferences toward trading based on their types and constraints. This article introduces a new contract theoretic framework to tackle this challenge by designing optimal contracts for ESs. To this end, a dynamic pricing scheme is developed such that the aggregator can utilize to incentivize the ESs to contribute to both baseload and peak load demands according to their categories. An algorithm is proposed that can be implemented in a distributed manner by trading partners to enable energy trading. It is shown that the trading strategy under a baseload scenario is feasible, and the aggregator only needs to consider the per unit generation cost of ESs to decide on its strategy. The trading strategy for a peak load scenario, however, is complex and requires consideration of different factors, such as variations in the wholesale price and its effect on the selling price of ESs, and the uncertainty of energy generation from RESs. Simulation results demonstrate the effectiveness of the proposed scheme for energy trading in the local electricity market.

A Generalized Framework for Service Restoration in a Resilient Power Distribution System

An electric power grid is one of the complex infrastructures, and because of its complex nature, there is simply no way that outages can be completely avoided. Thus, a modern society that depends on reliable electric supply requires a resilient electric system that can recover from disruptions while integrating emerging smart grid technologies. This article presents a novel approach for service restoration in a modern power distribution system with controllable switches and distributed generation (DG) resources for any kind of outage. The proposed framework supports both the traditional service restoration using feeder reconfiguration and the grid-forming DG-assisted intentional islands that are dynamically sized using algorithms based on the fault scenario, available resources, and priority of loads. The problem is formulated as a mixed-integer linear program that incorporates critical system connectivity and operating constraints. Simulations are performed to demonstrate the effectiveness of the proposed approach using a large-scale four-feeder 1069-bus three-phase unbalanced distribution test system. It is demonstrated that the framework is effective in utilizing all available resources in quickly restoring the power supply to improve resiliency during extreme events and is scalable for a large-scale unbalanced power distribution system.

Forecasting high resolution electricity demand data with additive models including smooth and jagged components

Short-Term Load Forecasting (STLF) is a fundamental instrument in the efficient operational management and planning of electric utilities. Emerging smart grid technologies pose new challenges and opportunities. Although load forecasting at the aggregate level has been extensively studied, electrical load forecasting at fine-grained geographical scales of households is more challenging. Among existing approaches, semi-parametric generalized additive models (GAM) have been increasingly popular due to their accuracy, flexibility, and interpretability. Their applicability is justified when forecasting is addressed at higher levels of aggregation, since the aggregated load pattern contains relatively smooth additive components. High resolution data are highly volatile, forecasting the average load using GAM models with smooth components does not provide meaningful information about the future demand. Instead, we need to incorporate irregular and volatile effects to enhance the forecast accuracy. We focus on the analysis of such hybrid additive models applied on smart meters data and show that it leads to improvement of the forecasting performances of classical additive models at low aggregation levels.

Grid Connected-Photovoltaic System (GC-PVS): Issues and Challenges

Smart grid is the key technology for an effective utilization of the Renewable Energy Sources (RES). The utilization of RES for the generation of electricity is increasingly gaining interest of researchers during the last decades. The main reason behind this is global incentivization, the increasing price of petroleum products, climate issues and deregulations in the energy market. As the Government of India, (MNRE i.e. Ministry of New & Renewable Energy) is targeting to generate 20000 MW power through grid-connected solar PVS by the year 2022 therefore, the main focus in this paper has been presented on power generation through grid-connected PVS. The emerging smart grid technology has enabled the grid-connected PVS as an evolving process in today’s world for electrical power generation. However, apart from so many advantages, there are several issues and challenges associated with the integration of PVS to the electric utility grid hence, the investigation to find out available possible solutions to overcome these issues becomes essential in order to enhance the performance of grid-connected PVS. The most severe constraint associated with this emerging technology is it high penetration level. If during low load conditions there is some mismatch found between the real power output and the load profile characteristics of PVS then it may result into large reverse power flow, high power losses or severe voltage violation. In this paper, several issues and challenges associated with the integration of solar PVS with the electric utility grid are presented.

Sequential Service Restoration for Unbalanced Distribution Systems and Microgrids

The resilience and reliability of modern power systems are threatened by increasingly severe weather events and cyber-physical security events. An effective restoration methodology is desired to optimally integrate emerging smart grid technologies and pave the way for developing self-healing smart grids. In this paper, a sequential service restoration (SSR) framework is proposed to generate restoration solutions for distribution systems and microgrids in the event of large-scale power outages. The restoration solution contains a sequence of control actions that properly coordinate switches, distributed generators, and switchable loads to form multiple isolated microgrids. The SSR can be applied for three-phase unbalanced distribution systems and microgrids and can adapt to various operation conditions. Mathematical models are introduced for three-phase unbalanced power flow, voltage regulators, transformers, and loads. The SSR problem is formulated as a mixed-integer linear programming model, and its effectiveness is evaluated via the modified IEEE 123 node test feeder.

Smart Distribution Course for 21st Century Power Sector Workforce

The current transition towards smart distribution systems necessitates the development of educational curricula that have the interdisciplinary breadth together with the technical depth to furnish the present and future generation of engineers with a new set of skills and tools to address the challenges associated with emerging smart grid technologies. This work presents a newly designed smart distribution course that is intended to meet the needs of the power distribution industry. The paper gives a high-level overview of the broad range of topics related to developments and trends in smart distribution technologies including automatic restoration, data management, cybersecurity, interoperability and standards, and future vision. It stresses the multidisciplinary nature of the course and the importance of the strong collaboration between academia and key stakeholders, and the support from utilities, industry, government, and consultants. The course has been developed and taught by distribution system experts who are currently implementing smart grid technologies along with smart grid pioneers who are known worldwide. Moreover, to measure the effectiveness of the instructional approach of the course, students learning outcomes were assessed and discussed. The lessons learned from the implementation of the course are also summarized.

Trans‐oceanic remote power hardware‐in‐the‐loop: multi‐site hardware, integrated controller, and electric network co‐simulation

Electric system operators are increasingly concerned with the potential system-wide impacts of the large-scale integration of distributed energy resources including voltage control, protection coordination, and equipment wear. This prompts a need for new simulation techniques that can simultaneously capture all the components of these large integrated smart grid systems. This study describes a novel platform that combines three emerging research areas: power systems co-simulation, power hardware in the loop (PHIL) simulation, and lab–lab links. The platform is distributed, real-time capable, allows for easy internet-based connection from geographically-dispersed participants, and is software platform agnostic. The authors demonstrate its utility by studying real-time PHIL co-simulation of coordinated solar photovoltaic (PV) firming control of two inverters connected in multiple electric distribution network models, prototypical of US and Australian systems. The novel trans-pacific closed-loop system simulation was conducted in real time using a power network simulator and physical PV/battery inverter at power at the National Renewable Energy Laboratory in Golden, CO, USA and a physical PV inverter at power at the Commonwealth Scientific and Industrial Research Organisation's Energy Centre in Newcastle, NSW, Australia. This capability enables smart grid researchers throughout the world to leverage their unique simulation capabilities for multi-site collaborations that can effectively simulate and validate emerging smart grid technology solutions.

Electromagnetic compatibility issues in critical smart grid infrastructure

The smart grid is an intelligent, efficient and dynamic grid system with endless possibilities but not without notable daunting technological challenges. At the core of these challenges are the electromagnetic compatibility (EMC) issues which may potentially thwart efforts on the smart grid if left unattended. This paper presents a survey on emerging smart grid technologies such as the power electronic interface, power line communication, flexible AC Transmission Systems (FACTS) and communication equipment highlighting their associated electromagnetic interference issues. We also provide some EMC/EMI mitigation techniques where available.

A control framework for the utilization of heating load flexibility in a day-ahead market

A flexible demand-side can have a positive influence on electricity markets and the entire electricity infrastructure once the flexibility is properly harnessed. Such a goal is becoming attainable with emerging smart grid technologies which allow the controlling of consumption and its aggregation to electricity markets. In this paper, we focus on the aggregation of detached houses with direct electric space heating (DESH) in terms of two main targets. Firstly, a basic framework for the aggregation is proposed and secondly, the benefit of the heating load flexibility for the aggregator and the consumers is investigated in the Nordic day-ahead electricity market Elspot. The loads are controlled with a simple strategy based on a centrally transmitted thermostat set-point signal, and this strategy is benchmarked against a more complex direct load control approach. As there is great flexibility potential in the heating load, the aggregattor is assumed to act as a price-maker in the market, where it seeks to minimize its energy cost and schedule the flexibility. The loads participating in the control are provided with a bonus based either on the caused inconvenience or the provided flexibility. In simulations studies, we use a detailed Finnish detached house population model and hourly market data from Nord Pool's Elspot.

Combined Online and Delayed Coordinated Charging of Plug-In Electric Vehicles Considering Wind and Rooftop PV Generations

Emerging smart grid technology is creating unique opportunity for wide applications of renewable distributed generation resources, smart appliances and plug-in electric vehicles (PEVs). This paper presents a combined online (real-time) and delayed (overnight and next day) PEV coordination scheme based on fuzzy reasoning and maximum sensitivity selections (MSS) considering wind and rooftop PV generations. The proposed combined online fuzzy-based and delayed MSS-based (OL-F/DL-MSS) algorithm aims at reducing energy generation cost by charging the electric vehicles during high renewable energy generation periods. To include vehicle owners’ satisfaction, they are classified into three groups; high priority consumers requiring expensive PEV charging as soon as arriving home during early evening hours, low priority consumers requesting cheap overnight PEV charging and medium priority consumers requiring inexpensive PEV charging within 24 hours. PEV coordination is performed to minimize the cost of generating energy and regulate node voltages while utilizing the available renewable energy resources for vehicle battery charging. Detailed simulations for uncoordinated and combined online/overnight coordinated PEV charging are presented for a 449 node smart grid test system consisting of the IEEE 31 node medium voltage distribution network, 3 wind turbines and 22 low voltage 19-node residential feeders with 88 rooftop PVs and 264 PEVs.

Towards Dynamic Demand Response On Efficient Consumer Grouping Algorithmics

The widespread monitoring of electricity consumption due to increasingly pervasive deployment of networked sensors in urban environments has resulted in an unprecedentedly large volume of data being collected. Particularly, with the emerging Smart Grid technologies becoming more ubiquitous, real-time and online analytics for discovering the underlying structure of increasing-dimensional (w.r.t. time) consumer time series data are crucial to convert the massive amount of fine-grained energy information gathered from residential smart meters into appropriate demand response (DR) insights. In this paper we propose READER and OPTIC, that are real-time and online algorithmic pre-processing frameworks respectively, for effective DR in the Smart Grid. READER (OPTIC) helps discover underlying structure from increasing-dimensional consumer consumption time series data in a provably optimal real-time (online) fashion. READER (OPTIC) catalyzes the efficacy of DR programs by systematically and efficiently managing the energy consumption data deluge, at the same time capturing in real-time (online), specific behavior, i.e., households or time instants with similar consumption patterns. The primary feature of READER (OPTIC) is a real-time (online) randomized approximation algorithm for grouping consumers based on their electricity consumption time series data, and provides two crucial benefits: (i) time efficiently tackles high volume, increasing-dimensional time series data and (ii) provides provable worst case grouping performance guarantees. We validate the grouping and DR efficacy of READER and OPTIC via extensive experiments conducted on both, a USC microgrid dataset as well as a synthetically generated dataset.

Emerging smart grid technology for mitigating global warming

Summary Global warming problem caused by greenhouse gas emissions from burning fossil fuels has caused wide public concern all over the world. This paper bases on renewable energy smart grid development as the background; the microgrid, power demand response, and vehicle to grid technologies are analyzed, and the basic structures and control strategies are also described. In order to transmit power from large-scale offshore wind farms and promote the utilization of renewable generation, the multi-terminal direct current (MTDC) based on voltage source converter (VSC) and solid state transformer are analyzed in detail, and the typical topologies and control methods are introduced. Finally, in order to improve the development of smart grid and VSC-MTDC in China, the microgrid park of the Institute of Electrical Engineering, Chinese Academy of Sciences, Yanqing direct current microgrid, and Nanao and Zhoushan VSC-MTDC are introduced. Copyright © 2015 John Wiley & Sons, Ltd.

Energy Storage in Pennsylvania: SEPTA's Novel and Innovative Integration of Emerging Smart Grid Technologies

The Southeastern Pennsylvania Transportation Authority?s (SEPTA?s) wayside energy storage initiatives have received national recognition for their innovative integration of emerging smart grid technologies with an aging transit infrastructure. These projects have the potential to transform the relationship between the transportation and energy industries.

Descriptive Models for Reserve and Regulation Prices in Competitive Electricity Markets

In addition to electric energy, ancillary services, such as operating reserves and frequency regulation service, are also traded in a competitive electricity market. With the emerging smart grid technologies and large scale integration of variables resources into the grid, the market for ancillary services is expected to grow, and thus, their prices become increasingly important. The prices of ancillary services feature different patterns and characteristics when compared to electric energy prices. The differences include lower price level, higher variability, and more frequent and extreme spikes. While electric energy prices have been broadly studied in the literature, research on features and modelling of ancillary services prices is limited. This paper investigates the application of established stochastic approaches for modelling the behavior of operating reserve and regulation prices in North American electricity markets. Such descriptive stochastic models are necessary for risk management and derivative pricing of these commodities. Mean-reverting jump-diffusion (MRJD) and Markov regime-switching (MRS) models with various specifications are analyzed. Historical prices from the Ontario and New York markets have been used for model calibration and simulation analysis. The performance of the two classes of models has been compared using various statistical measures.

Online capacity provisioning for carbon-neutral data center with demand-responsive electricity prices

Due to the huge electricity consumption and carbon emissions, data center operators have been increasingly pressured to reduce their net carbon footprints to zero, i.e., carbon neutrality. In this paper, we propose an efficient online algorithm, called CNDC (optimization for Carbon-Neutral Data Center), to control the number of active servers for minimizing the data center operational cost (defined as a weighted sum of electricity cost and delay cost) while satisfying carbon neutrality without requiring long-term future information. Unlike prior research on carbon neutrality, we explore demand-responsive electricity price enabled by the emerging smart grid technology and demonstrate that it can be incorporated in data center operation to reduce the operational cost. Leveraging the Lyapunov optimization technique, we prove that CNDC achieves a close-to-minimum operational cost compared to the optimal algorithm with future information, while bounding the potential violation of carbon neutrality, in an almost arbitrarily random environment. We also perform trace-based simulation as well as experiment studies to complement the analysis. The results show that CNDC reduces the cost by more than 20% (compared to state-of-the-art prediction-based algorithm) while resulting in a smaller carbon footprint. Moreover, by incorporating demand-response electricity prices, CNDC can further decrease the average cost by approximately 2.5%, translating into hundreds of thousands of dollars per year.

Smart Personal Sensor Network Control for Energy Saving in DC Grid Powered LED Lighting System

Emerging smart grid technologies aim to renovate traditional power grid by integrating intelligent devices and their communications for monitoring and automation of the power grid to enable efficient demand-side energy management. In this paper, energy management in smart dc building grid powered dc electrical appliances like lighting is investigated, in particular energy savings from proposed personal lighting management strategy. Unlike conventional fluorescent lamps powered mainly by ac grid, LED luminaires are dc in nature, thus results in significant power conversion losses, if operate on traditional ac powered system, are analyzed with proposed dc distribution building grid for LED lighting. This paper continues to explore the use of smart wireless sensors for personal control of the dc grid powered networked LED lighting. Experimental results show that the proposed smart LED lighting system with an energy saving mechanism incorporated is able to achieve similar lighting performance as the conventional lighting condition, while at the same time, able to attain about 44% energy saving as compared to the original ac fluorescent system. For a low voltage dc grid being implemented, the maximum power loss and voltage drop of the developed dc distribution building grid are 2.25% and 3% respectively.