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A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (30 September 2017) | Viewed by 39788

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Special Issue Editor

Dr. Hongjian Sun

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Guest Editor

Department of Engineering, Durham University, Stockton Road, Durham DH1 3LE, UK
Interests: smart grids; cognitive radio; wireless power transfer; Internet of things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As an essential part of smart grids, Demand Side Management (DSM) can achieve many benefits, e.g.,

Facilitating carbon emission reduction;
Deferring further network investments;
Integrating more distributed generations to power network infrastructure;
Relieving congestions and system constraints;
Simplifying outage management;
Enhancing the quality, efficiency, and security of power supply.

At the core of DSM, smart metering system provides an important platform to engage electricity customers for participating in DSM programs. However, a number of significant challenges exist, such as:

How to properly use smart pricing schemes to incentivize customers?
What is an ideal data analytic platform for best enabling DSM or demand response?
How scalable the advanced metering infrastructure should be?
What ICT infrastructure is required for future smart metering system?

This Special Issue is dedicated to tackling these challenging issues. Potential research topics include, but are not limited to:

Advanced metering infrastructure design;
Smart pricing and real-time pricing schemes;
New DSM approaches;
Demand response aggregation;
Data fusion and data analytics;
Machine learning;
Demand and renewable energy predication;
Wireless or wired communication system for supporting smart metering;
Smart home and smart building technologies;
Emerging energy market architecture.

Dr. Hongjian Sun
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

smart grids
smart meter
advanced metering infrastructure
smart pricing
real-time pricing
demand response
demand side management
data fusion
decision making

Published Papers (7 papers)

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Research

29 pages, 5654 KiB

Open AccessArticle

Application and Comparison of Metaheuristic and New Metamodel Based Global Optimization Methods to the Optimal Operation of Active Distribution Networks

by Hao Xiao, Wei Pei, Zuomin Dong, Li Kong and Dan Wang

Energies 2018, 11(1), 85; https://doi.org/10.3390/en11010085 - 01 Jan 2018

Cited by 23 | Viewed by 5333

Abstract

As an imperative part of smart grids (SG) technology, the optimal operation of active distribution networks (ADNs) is critical to the best utilization of renewable energy and minimization of network power losses. However, the increasing penetration of distributed renewable energy sources with uncertain power generation and growing demands for higher quality power distribution are turning the optimal operation scheduling of ADN into complex and global optimization problems with non-unimodal, discontinuous and computation intensive objective functions that are difficult to solve, constituting a critical obstacle to the further advance of SG and ADN technology. In this work, power generation from renewable energy sources and network load demands are estimated using probability distribution models to capture the variation trends of load fluctuation, solar radiation and wind speed, and probability scenario generation and reduction methods are introduced to capture uncertainties and to reduce computation. The Open Distribution System Simulator (OpenDSS) is used in modeling the ADNs to support quick changes to network designs and configurations. The optimal operation of the ADN, is achieved by minimizing both network voltage deviation and power loss under the probability-based varying power supplies and loads. In solving the computation intensive ADN operation scheduling optimization problem, several novel metamodel-based global optimization (MBGO) methods have been introduced and applied. A comparative study has been carried out to compare the conventional metaheuristic global optimization (GO) and MBGO methods to better understand their advantages, drawbacks and limitations, and to provide guidelines for subsequent ADN and smart grid scheduling optimizations. Simulation studies have been carried out on the modified IEEE 13, 33 and 123 node networks to represent ADN test cases. The MBGO methods were found to be more suitable for small- and medium-scale ADN optimal operation scheduling problems, while the metaheuristic GO algorithms are more effective in the optimal operation scheduling of large-scale ADNs with relatively straightforward objective functions that require limited computational time. This research provides solution for ADN optimal operations, and forms the foundation for ADN design optimization. Full article

(This article belongs to the Special Issue From Smart Metering to Demand Side Management)

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2413 KiB

Open AccessArticle

A New Concept of Active Demand Side Management for Energy Efficient Prosumer Microgrids with Smart Building Technologies

by Andrzej Ożadowicz

Energies 2017, 10(11), 1771; https://doi.org/10.3390/en10111771 - 03 Nov 2017

Cited by 34 | Viewed by 6206

Abstract

Energy efficient prosumer microgrids (PMGs) with active and flexible demand side management (DSM) mechanisms are considered to be crucial elements of future smart grids. Due to an increasing share of renewable energy and the growing power demand, appropriate tools to manage not only the loads but also small generation units, heating and cooling systems, storage units and electric vehicles should be provided for them. Therefore, this paper proposes an innovative approach to both physical and logical organization of an active DSM system for future building-integrated prosumer microgrids (BIPMGs), based on standard building automation and control systems (BACS) as well as Internet of Things (IoT) paradigm. New event-triggered control functions with developed universal, logical interfaces for open BACS and IoT network nodes are presented and their implementation in smart metering as well as fully integrated energy management mechanisms is analyzed. Finally, potential energy efficiency improvements with proposed BACS functions are discussed, based on BACS efficiency requirements defined in the EN 15232 standard. Full article

(This article belongs to the Special Issue From Smart Metering to Demand Side Management)

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2473 KiB

Open AccessArticle

Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms

by Krzysztof Gajowniczek and Tomasz Ząbkowski

Energies 2017, 10(10), 1547; https://doi.org/10.3390/en10101547 - 08 Oct 2017

Cited by 33 | Viewed by 7292

Abstract

Forecasting of electricity demand has become one of the most important areas of research in the electric power industry, as it is a critical component of cost-efficient power system management and planning. In this context, accurate and robust load forecasting is supposed to play a key role in reducing generation costs, and deals with the reliability of the power system. However, due to demand peaks in the power system, forecasts are inaccurate and prone to high numbers of errors. In this paper, our contributions comprise a proposed data-mining scheme for demand modeling through peak detection, as well as the use of this information to feed the forecasting system. For this purpose, we have taken a different approach from that of time series forecasting, representing it as a two-stage pattern recognition problem. We have developed a peak classification model followed by a forecasting model to estimate an aggregated demand volume. We have utilized a set of machine learning algorithms to benefit from both accurate detection of the peaks and precise forecasts, as applied to the Polish power system. The key finding is that the algorithms can detect 96.3% of electricity peaks (load value equal to or above the 99th percentile of the load distribution) and deliver accurate forecasts, with mean absolute percentage error (MAPE) of 3.10% and resistant mean absolute percentage error (r-MAPE) of 2.70% for the 24 h forecasting horizon. Full article

(This article belongs to the Special Issue From Smart Metering to Demand Side Management)

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1817 KiB

Open AccessArticle

Cost Analysis for a Hybrid Advanced Metering Infrastructure in Korea

by Sung-Won Park and Sung-Yong Son

Energies 2017, 10(9), 1308; https://doi.org/10.3390/en10091308 - 01 Sep 2017

Cited by 6 | Viewed by 4768

Abstract

Advanced metering infrastructure (AMI) refers to the electricity service infrastructure between electricity consumers and suppliers and is technically essential for the realization of a smart grid environment. To implement AMI, various communications technologies are being used based on the application environment according to the utility. However, using a single communications method can give rise to attenuation in the downtown underground distribution line section or cause higher supply costs due to decreased density in the range from farming to fishing areas. A hybrid AMI is one solution to this problem. According to an economic analysis of previous AMI deployment, the cost to install a communications network accounts on average for 45% of the total cost. Since the installation cost of a communications network is influenced by the density of the installation environment, a hybrid AMI, which allows the configuration of a flexible network using both wired and wireless communications, can be a good alternative, both technically and financially. This study conducted a simulation based on density of the installation environment and configuration of the communications network to analyze the economic effect of installing a hybrid AMI communications network. It assumed that a hybrid AMI was deployed in an overhead distribution line in a low-density area. The simulation outcomes were compared and analyzed against the power line communication (PLC)-only AMI method. The results showed that the hybrid AMI method had a 10% communications network cost reduction effect compared to the PLC-only AMI method. In addition, the analysis indicated that there was a maximum 19% cost reduction effect in communications network installation depending on the method of network installation, suggesting that the hybrid AMI was economically more effective than the PLC-only AMI method. Full article

(This article belongs to the Special Issue From Smart Metering to Demand Side Management)

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1091 KiB

Open AccessArticle

An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes

by Awais Manzoor, Nadeem Javaid, Ibrar Ullah, Wadood Abdul, Ahmad Almogren and Atif Alamri

Energies 2017, 10(9), 1258; https://doi.org/10.3390/en10091258 - 24 Aug 2017

Cited by 66 | Viewed by 5581

Abstract

Smart grid is an emerging technology which is considered to be an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of smart grid (SG), which aims at provision of demand side management mechanisms (DSM), such as demand response (DR). In this paper, we propose a hybrid technique named as teacher learning genetic optimization (TLGO) by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspects which must be addressed along with cost minimization. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power-flexible appliances on consumers’ bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort. Full article

(This article belongs to the Special Issue From Smart Metering to Demand Side Management)

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1032 KiB

Open AccessArticle

The Potential of Smart Technologies and Micro-Generation in UK SMEs

by Peter Warren

Energies 2017, 10(7), 1050; https://doi.org/10.3390/en10071050 - 20 Jul 2017

Cited by 13 | Viewed by 4518

Abstract

Small-to-medium-sized enterprises (SMEs) make up 99% of businesses and contribute 13% of energy demand globally. However, much of the demand-side energy research and policy attention to date has focused on the domestic, large commercial and industrial sectors. Previous research on SMEs has primarily concentrated on the drivers and barriers to the adoption of energy efficiency measures. However, less attention has been given to other areas of demand-side management in SMEs, such as the role of ‘smart’ technologies and micro-generation. The paper aims to contribute to filling this gap. To analyse the potential of smart technologies in UK SMEs, a quantitative model is developed to assess seven categories of smart technologies in ten non-domestic sectors. Overall, the results suggest that smart technologies within the UK SME market offer significant estimated annual energy savings potential of ~£8.6 billion against an estimated energy spend of ~£49.7 billion (representing ~17% savings potential on energy expenditures). From the smart technology categories examined, fleet management, integrated building management systems and smart meters have the potential to offer the greatest energy savings to SMEs, providing estimated total energy savings of ~£7.5 billion annually. To analyse the potential of micro-generation in UK SMEs, interview-based qualitative research was undertaken with 17 SMEs to explore the drivers and barriers to its adoption. The research found that the initial costs, technical feasibility and planning permission on historical buildings were the main barriers, and that the ‘green’ marketing potential of micro-generation, coupled with ethical reasons and feed-in tariffs, were the main drivers. Full article

(This article belongs to the Special Issue From Smart Metering to Demand Side Management)

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1079 KiB

Open AccessArticle

Optimal Power Allocation for a Relaying-Based Cognitive Radio Network in a Smart Grid

by Kai Ma, Xuemei Liu, Jie Yang, Zhixin Liu and Yazhou Yuan

Energies 2017, 10(7), 909; https://doi.org/10.3390/en10070909 - 03 Jul 2017

Cited by 8 | Viewed by 4573

Abstract

This paper obtains optimal power allocation to the data aggregator units (DAUs) and relays for cognitive wireless networks in a smart grid (SG). Firstly, the mutual interference between the primary user and the DAU are considered, and the expressions of the DAU transmission signal are derived based on the sensing information. Secondly, we use the particle swarm optimization (PSO) algorithm to search for the optimal power allocation to minimize the costs to the utility company. Finally, the impact of the sensing information on the network performance is studied. Then two special cases (namely, that only one relay is selected, and that the channel is not occupied by the primary user) are discussed. Simulation results demonstrate that the optimal power allocation and the sensing information of the relays can reduce the costs to the utility company for cognitive wireless networks in a smart grid. Full article

(This article belongs to the Special Issue From Smart Metering to Demand Side Management)

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