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Smart Energy Management for Smart Grids 2019

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 50708

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Special Issue Editors

Prof. Dr. José L. Bernal-Agustín

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Electrical Engineering Department, University of Zaragoza. Calle María de Luna, 3. 50018 Zaragoza, Spain
Interests: evolutionary computation applications to engineering; renewable energy; distribution power system; energy management; electric markets
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Prof. Dr. Rodolfo Dufo-López

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Department of Electrical Engineering, University of Zaragoza, Calle María de Luna, 3, 50018 Zaragoza, Spain
Interests: renewable energy; electricity storage; advanced batteries models; net metering; energy management; optimization algorithms
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Special Issue Information

Dear Colleagues,

Electricity networks have evolved over the last few years, improving their reliability and increasing their functionality. These changes have led to the development of smart grids.

Smart grids allow communication between the different agents of the electrical system, and the interaction of these agents with the equipment, devices and software that are part of the smart grids. One notable, but not the only, feature of smart grids is their application in energy management. The possibility of transmitting information from metering equipment to electricity companies and consumers allows for proper demand management, both from the point of view of the companies and the consumer. Energy management through smart grids improves safety and quality by adequately monitoring the different elements of the grid, and facilitates the use of new storage technologies, the renewable energies integration, the implementation of electric vehicles and self-consumption systems.

Many aspects must be taken into account to ensure that smart grids function properly, including the development of control and measurement devices, communication systems and the software required to manage all devices, reducing costs and maximizing reliability.

Taking into account all the above, this Special Issue is dedicated to topics related to “Smart Energy Management for Smart Grids”, including both technical and economic topics.

Prof. Dr. José L. Bernal-Agustín
Prof. Dr. Rodolfo Dufo-López
Guest Editors

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Keywords

Smart metering
Load management
Energy management
Energy storage
Communication systems
Electrical grid protection
Grid connected renewable generation
Distributed generation
Self-consumption
Vehicle-to-Grid (V2G)
Load forecasting

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Published Papers (9 papers)

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Research

15 pages, 1761 KiB

Open AccessArticle

Smart Load Management with Energy Storage for Power Quality Enhancement in Wind-Powered Oil and Gas Applications

by Erick Alves, Santiago Sanchez, Danilo Brandao and Elisabetta Tedeschi

Energies 2019, 12(15), 2985; https://doi.org/10.3390/en12152985 - 2 Aug 2019

Cited by 23 | Viewed by 5398

Abstract

This paper investigates power quality issues in a wind-powered offshore oil and gas platform operating in island mode. Topics of interest are the negative effects that load and wind power variability have on the electrical system frequency and voltage; and how those influence the gas turbine operation. The authors discuss how smart load management together with energy storage can mitigate those effects, and propose a control algorithm for that. Simulations in MATLAB/Simulink demonstrate that the proposed energy storage controller reduces frequency and voltage variations in a case study. Moreover, the paper presents a methodology to derive a simplified model of the hybrid energy system that reduces simulation time in at least two orders of magnitude. The latter can be a useful tool for optimization algorithms evaluating a huge number of scenarios, especially those dealing with economical dispatch of generators or sizing of energy storage systems. Full article

(This article belongs to the Special Issue Smart Energy Management for Smart Grids 2019)

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19 pages, 2998 KiB

Open AccessArticle

Bottom-Up Electrification Introducing New Smart Grids Architecture—Concept Based on Feasibility Studies Conducted in Rwanda

by Bartosz Soltowski, David Campos-Gaona, Scott Strachan and Olimpo Anaya-Lara

Energies 2019, 12(12), 2439; https://doi.org/10.3390/en12122439 - 25 Jun 2019

Cited by 15 | Viewed by 4351

Abstract

Over the past eight years, off-grid systems, in the form of stand-alone solar home systems (SHSs), have proved the most popular and immediate solution for increasing energy access in rural areas across the Global South. Although deployed in significant numbers, issues remain with the cost, reliability, utilization, sustainability and scalability of these off-grid systems to provide higher-tiered energy access. Interconnection of existing stand-alone solar home systems (SHSs) can form a microgrid of interconnected prosumers (i.e., households owning SHS capable of producing and consuming power) and consumers (i.e., households without an SHS, and only capable of consuming power). This paper focuses on the role of a smart energy management (SEM) platform in the interconnection of off-grid systems and making bottom-up electrification scalable, and how it can improve the overall sustainability, efficiency and flexibility of off-grid technology. An interconnected SHS microgrid has the potential to unlock latent generation and storage capacity, and so effectively promote connected customers to higher tiers of energy access. This approach can therefore extend the range of products currently used by people located in the remote areas of developing countries to include higher-power devices such as refrigerators, TVs and potentially, electric cookers. This paper shows the results of field studies in the Northern Province of Rwanda within off-grid villages where people mainly rely on SHSs as a source of electricity. These field studies have informed further simulation-based studies that define the principal requirements for the operation of a smart energy management platform for the interconnection of SHSs to form a community microgrid. Full article

(This article belongs to the Special Issue Smart Energy Management for Smart Grids 2019)

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20 pages, 5918 KiB

Open AccessArticle

A Heuristic Home Electric Energy Management System Considering Renewable Energy Availability

by Victor J. Gutierrez-Martinez, Carlos A. Moreno-Bautista, Jose M. Lozano-Garcia, Alejandro Pizano-Martinez, Enrique A. Zamora-Cardenas and Miguel A. Gomez-Martinez

Energies 2019, 12(4), 671; https://doi.org/10.3390/en12040671 - 19 Feb 2019

Cited by 22 | Viewed by 3960

Abstract

This paper presents the development of a heuristic-based algorithm for a Home Electric Energy Management System (HEEMS). The novelty of the proposal resides in the fact that solutions of the Pareto front, minimizing both the energy consumption and cost, are obtained by a Genetic Algorithm (GA) considering the renewable energy availability as well as the user activity level (AL) inside the house. The extensive solutions search characteristic of the GAs is seized to avoid the calculation of the full set of Pareto front solutions, i.e., from a reduced set of non-dominated solutions in the Pareto sense, an optimal solution with the best fitness is obtained, reducing considerably the computational time. The HEEMS considers models of the air conditioner, clothes dryer, dishwasher, electric stove, pool pump, and washing machine. Models of the wind turbine and solar PV modules are also included. The wind turbine model is written in terms of the generated active power exclusively dependent on the incoming wind profiles. The solar PV modules model accounts for environmental factors such as ambient temperature changes and irradiance profiles. The effect of the energy storage unit on the energy consumption and costs is evaluated adapting a model of the device considering its charge and discharge ramp rates. The proposed algorithm is implemented in the Matlab^® platform and its validation is performed by comparing its results to those obtained by a freeware tool developed for the energy management of smart residential loads. Also, the evaluation of the performance of the proposed HEEMS is carried out by comparing its results to those obtained when the multi-objective optimization problem is solved considering weights assigned to each objective function. Results showed that considerable savings are obtained at reduced computational times. Furthermore, with the calculation of only one solution, the end-user interaction is reduced making the HEEMS even more manageable than previously proposed approaches. Full article

(This article belongs to the Special Issue Smart Energy Management for Smart Grids 2019)

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30 pages, 10480 KiB

Open AccessArticle

Mitigating Energy System Vulnerability by Implementing a Microgrid with a Distributed Management Algorithm

by Juan M. Lujano-Rojas, José M. Yusta and José Antonio Domínguez-Navarro

Energies 2019, 12(4), 616; https://doi.org/10.3390/en12040616 - 15 Feb 2019

Cited by 1 | Viewed by 3045

Abstract

This work presents a management strategy for microgrid (MG) operation. Photovoltaic (PV) and wind generators, as well as storage systems and conventional units, are distributed over a wide geographical area, forming a distributed energy system, which is coordinated to face any contingency of the utility company by means of its isolated operation. The management strategy divides the system into three main layers: renewable generation, storage devices, and conventional units. Interactions between devices of the same layer are determined by solving an economic dispatch problem (EDP) in a distributed manner using a consensus algorithm (CA), and interactions between layers are determined by means of a load following strategy. In this way, the complex behaviour of PV and wind generation, the battery storage system, and conventional units has been effectively combined with CA to solve EDP in a distributed manner. MG performance and its vulnerability are deeply analysed by means of an illustrative case study. From the observed results, vulnerability under extreme conditions could be reduced up to approximately 30% by coupling distributed renewable generation and storage capacity with an energy system based on conventional generation. Full article

(This article belongs to the Special Issue Smart Energy Management for Smart Grids 2019)

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16 pages, 1736 KiB

Open AccessArticle

Applying Complex Network Theory to the Vulnerability Assessment of Interdependent Energy Infrastructures

by Jesus Beyza, Eduardo Garcia-Paricio and Jose M. Yusta

Energies 2019, 12(3), 421; https://doi.org/10.3390/en12030421 - 29 Jan 2019

Cited by 31 | Viewed by 5074

Abstract

In this paper, we evaluate the use of statistical indexes from graph theory as a possible alternative to power-flow techniques for analyzing cascading failures in coupled electric power and natural gas transmission systems. Both methodologies are applied comparatively to coupled IEEE and natural gas test networks. The cascading failure events are simulated through two strategies of network decomposition: Deliberate attacks on highly connected nodes and random faults. The analysis is performed by simulating successive N-k contingencies in a coupled network, where the network structure changes with the elimination of each node. The suitability of graph-theoretic techniques for assessing the vulnerability of interdependent electric power and natural gas infrastructures is demonstrated. Full article

(This article belongs to the Special Issue Smart Energy Management for Smart Grids 2019)

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22 pages, 8934 KiB

Open AccessArticle

A Methodology for Determination and Definition of Key Performance Indicators for Smart Grids Development in Island Energy Systems

by Dionysios Pramangioulis, Konstantinos Atsonios, Nikos Nikolopoulos, Dimitrios Rakopoulos, Panagiotis Grammelis and Emmanuel Kakaras

Energies 2019, 12(2), 242; https://doi.org/10.3390/en12020242 - 14 Jan 2019

Cited by 45 | Viewed by 8188

Abstract

There is a growing interest over the last decades in the field of autonomous island grids that is driven mainly by climate reasons. The common objective among the members of the European Union (EU) is the increase of Renewable Energy Sources (RES) penetration in the energy mixture, as well as turning the grid into a smart grid. Consequently, more and more state-of-the-art solutions are being proposed for the electricity generation and the optimization of the energy system management, taking advantage of innovations in all energy related sectors. The evaluation of all available solutions requires quantitative assessment, through the adoption of representative Key Performance Indicators (KPIs) for the projects that are related to smart grid development in isolated energy systems, providing the relevant stakeholders with a useful comparison among the proposed solutions. The evaluation approach that is described in this paper emphasizes the role of the various stakeholder groups who face the proposed solutions by different points of view. Apart from the domains of interest that are also observed in previous approaches, the proposed list also contains a set of legal KPIs, since the regulatory framework can either represent a serious barrier or grant a strong incentive for the implementation of state-of-the-art energy technology and grid management solutions in different countries. Full article

(This article belongs to the Special Issue Smart Energy Management for Smart Grids 2019)

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26 pages, 1134 KiB

Open AccessArticle

Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources

by Urooj Asgher, Muhammad Babar Rasheed, Ameena Saad Al-Sumaiti, Atiq Ur-Rahman, Ihsan Ali, Amer Alzaidi and Abdullah Alamri

Energies 2018, 11(12), 3494; https://doi.org/10.3390/en11123494 - 14 Dec 2018

Cited by 55 | Viewed by 7347

Abstract

Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the incorporation of energy resources (RESs) i.e., solar energy. For this purpose, a real-time electricity price (RTP), energy demand, user preferences and renewable energy parameters are taken as an inputs and genetic algorithm (GA) has been used to manage and schedule residential load with the objective of cost, user discomfort, and peak-to-average ratio (PAR) reduction. Initially, RTP is used to reduce the energy consumption cost. However, to minimize the cost along with reducing the peaks, a combined pricing model, i.e., RTP with inclining block rate (IBR) has been used which incorporates user preferences and RES to optimally schedule load demand. User comfort and cost reduction are contradictory objectives, and difficult to maximize, simultaneously. Considering this trade-off, a combined pricing scheme is modelled in such a way that users are given priority to achieve their objective as per their requirements. To validate and analyze the performance of the proposed algorithm, we first propose mathematical models of all utilized loads, and then multi-objective optimization problem has been formulated. Furthermore, analytical results regarding the objective function and the associated constraints have also been provided to validate simulation results. Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort. Full article

(This article belongs to the Special Issue Smart Energy Management for Smart Grids 2019)

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16 pages, 2438 KiB

Open AccessArticle

Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles

by Se-Hyeok Choi, Akhtar Hussain and Hak-Man Kim

Energies 2018, 11(10), 2646; https://doi.org/10.3390/en11102646 - 3 Oct 2018

Cited by 18 | Viewed by 3728

Abstract

The optimal operation of microgrids is challenging due to the presence of various uncertain factors, i.e., renewable energy sources, loads, market price signals, and arrival and departure times of electric vehicles (EVs). In order to incorporate these uncertainties into the operation model of microgrids, an adaptive robust optimization-based operation method is proposed in this paper. In particular, the focus is on the uncertainties in arrival and departure times of EVs. The optimization problem is divided into inner and outer problems and is solved iteratively by introducing column and constraint cuts. The unit commitment status of dispatchable generators is determined in the outer problem. Then, the worst-case realizations of all the uncertain factors are determined in the inner problem. Based on the values of uncertain factors, the generation amount of dispatchable generators, the amount of power trading with the utility grid, and the charging/discharging amount of storage elements are determined. The performance of the proposed method is evaluated using three different cases, and sensitivity analysis is carried out by varying the number of EVs and the budget of uncertainty. The impact of the budget of uncertainty and number of EVs on the operation cost of the microgrid is also evaluated considering uncertainties in arrival and departure times of EVs. Full article

(This article belongs to the Special Issue Smart Energy Management for Smart Grids 2019)

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16 pages, 755 KiB

Open AccessArticle

QoE-Aware Smart Home Energy Management Considering Renewables and Electric Vehicles

by Mingfu Li, Guan-Yi Li, Hou-Ren Chen and Cheng-Wei Jiang

Energies 2018, 11(9), 2304; https://doi.org/10.3390/en11092304 - 1 Sep 2018

Cited by 23 | Viewed by 8312

Abstract

To reduce the peak load and electricity bill while preserving the user comfort, a quality of experience (QoE)-aware smart appliance control algorithm for the smart home energy management system (sHEMS) with renewable energy sources (RES) and electric vehicles (EV) was proposed. The proposed algorithm decreases the peak load and electricity bill by deferring starting times of delay-tolerant appliances from peak to off-peak hours, controlling the temperature setting of heating, ventilation, and air conditioning (HVAC), and properly scheduling the discharging and charging periods of an EV. In this paper, the user comfort is evaluated by means of QoE functions. To preserve the user’s QoE, the delay of the starting time of a home appliance and the temperature setting of HVAC are constrained by a QoE threshold. Additionally, to solve the trade-off problem between the peak load/electricity bill reduction and user’s QoE, a fuzzy logic controller for dynamically adjusting the QoE threshold to optimize the user’s QoE was also designed. Simulation results demonstrate that the proposed smart appliance control algorithm with a fuzzy-controlled QoE threshold significantly reduces the peak load and electricity bill while optimally preserving the user’s QoE. Compared with the baseline case, the proposed scheme reduces the electricity bill by 65% under the scenario with RES and EV. Additionally, compared with the method of optimal scheduling of appliances in the literature, the proposed scheme achieves much better peak load reduction performance and user’s QoE. Full article

(This article belongs to the Special Issue Smart Energy Management for Smart Grids 2019)

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