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18 pages, 2556 KiB

Open AccessArticle

Real Time Security Assessment of the Power System Using a Hybrid Support Vector Machine and Multilayer Perceptron Neural Network Algorithms

by Oyeniyi Akeem Alimi, Khmaies Ouahada and Adnan M. Abu-Mahfouz

Sustainability 2019, 11(13), 3586; https://doi.org/10.3390/su11133586 - 29 Jun 2019

Cited by 33 | Viewed by 3869

Abstract

In today’s grid, the technological based cyber-physical systems have continued to be plagued with cyberattacks and intrusions. Any intrusive action on the power system’s Optimal Power Flow (OPF) modules can cause a series of operational instabilities, failures, and financial losses. Real time intrusion [...] Read more.

In today’s grid, the technological based cyber-physical systems have continued to be plagued with cyberattacks and intrusions. Any intrusive action on the power system’s Optimal Power Flow (OPF) modules can cause a series of operational instabilities, failures, and financial losses. Real time intrusion detection has become a major challenge for the power community and energy stakeholders. Current conventional methods have continued to exhibit shortfalls in tackling these security issues. In order to address this security issue, this paper proposes a hybrid Support Vector Machine and Multilayer Perceptron Neural Network (SVMNN) algorithm that involves the combination of Support Vector Machine (SVM) and multilayer perceptron neural network (MPLNN) algorithms for predicting and detecting cyber intrusion attacks into power system networks. In this paper, a modified version of the IEEE Garver 6-bus test system and a 24-bus system were used as case studies. The IEEE Garver 6-bus test system was used to describe the attack scenarios, whereas load flow analysis was conducted on real time data of a modified Nigerian 24-bus system to generate the bus voltage dataset that considered several cyberattack events for the hybrid algorithm. Sising various performance metricion and load/generator injections, en included in the manuscriptmulation results showed the relevant influences of cyberattacks on power systems in terms of voltage, power, and current flows. To demonstrate the performance of the proposed hybrid SVMNN algorithm, the results are compared with other models in related studies. The results demonstrated that the hybrid algorithm achieved a detection accuracy of 99.6%, which is better than recently proposed schemes. Full article

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

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25 pages, 2405 KiB

Open AccessArticle

Smart Grid R&D Planning Based on Patent Analysis

by Jason Jihoon Ree and Kwangsoo Kim

Sustainability 2019, 11(10), 2907; https://doi.org/10.3390/su11102907 - 22 May 2019

Cited by 4 | Viewed by 4947

Abstract

A smart grid employs information and communications technology to improve the efficiency, reliability, economics, and sustainability of electricity production and distribution. The convergent and complex nature of a smart grid and the multifarious connection between its individual technology components, as well as competition [...] Read more.

A smart grid employs information and communications technology to improve the efficiency, reliability, economics, and sustainability of electricity production and distribution. The convergent and complex nature of a smart grid and the multifarious connection between its individual technology components, as well as competition between private companies, which will exert substantial influences on the future smart grid business, make a strategic approach necessary from the beginning of research and development (R&D) planning with collaborations among various research groups and from national, industry, company, and detailed technological levels. However, the strategic, technological, business environmental, and regulatory barriers between various stakeholders with collaborative or sometimes conflictive interests need to be clarified for a breakthrough in the smart grid field. A strategic R&D planning process was developed in this study to accomplish the complicated tasks, which comprises five steps: (i) background research of smart grid industry; (ii) selection of R&D target; (iii) societal, technological, economical, environmental, and political (STEEP) analysis to obtain a macro-level perspective and insight for achieving the selected R&D target; (iv) patent analysis to explore capabilities of the R&D target and to select the entry direction for smart grid industry; and, (v) nine windows and scenario planning analyses to develop a method and process in establishing a future strategic R&D plan. This R&D planning process was further applied to the case of a Korean company holding technological capabilities in the sustainable smart grid domain, as well as in the sustainable electric vehicle charging system, a global consumer market of smart grid. Four plausible scenarios were produced by varying key change agents for the results of this process, such as technology and growth rates, policies and government subsidies, and system standards of the smart grid charging system: Scenario 1, ‘The Stabilized Settlement of the Smart Grid Industry’; Scenario 2, ‘The Short-lived Blue Ocean of the Smart Grid Industry’; Scenario 3, ‘The Questionable Market of the Smart Grid’; and, Scenario 4, ‘The Stalemate of the Smart Grid Industry’. The R&D plan suggestions were arranged for each scenario and detailed ways to cope with dissonant situations were also implied for the company. In sum, in this case study, a future strategic R&D plan was suggested in regard to the electric vehicle charging technology business, which includes smart grid communication system, battery charging duration, service infrastructure, public charge station system, platform and module, wireless charging, data management system, and electric system solution. The strategic R&D planning process of this study can be applicable in various technologies and business fields, because of no inherent dependency on particular subject, like electric vehicle charging technology based on smart grid. Full article

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

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22 pages, 1510 KiB

Open AccessArticle

Game Theoretical Energy Management with Storage Capacity Optimization and Photo-Voltaic Cell Generated Power Forecasting in Micro Grid

by Aqdas Naz, Nadeem Javaid, Muhammad Babar Rasheed, Abdul Haseeb, Musaed Alhussein and Khursheed Aurangzeb

Sustainability 2019, 11(10), 2763; https://doi.org/10.3390/su11102763 - 14 May 2019

Cited by 40 | Viewed by 4991

Abstract

In order to ensure optimal and secure functionality of Micro Grid (MG), energy management system plays vital role in managing multiple electrical load and distributed energy technologies. With the evolution of Smart Grids (SG), energy generation system that includes renewable resources is introduced [...] Read more.

In order to ensure optimal and secure functionality of Micro Grid (MG), energy management system plays vital role in managing multiple electrical load and distributed energy technologies. With the evolution of Smart Grids (SG), energy generation system that includes renewable resources is introduced in MG. This work focuses on coordinated energy management of traditional and renewable resources. Users and MG with storage capacity is taken into account to perform energy management efficiently. First of all, two stage Stackelberg game is formulated. Every player in game theory tries to increase its payoff and also ensures user comfort and system reliability. In the next step, two forecasting techniques are proposed in order to forecast Photo Voltaic Cell (PVC) generation for announcing optimal prices. Furthermore, existence and uniqueness of Nash Equilibrium (NE) of energy management algorithm are also proved. In simulation, results clearly show that proposed game theoretic approach along with storage capacity optimization and forecasting techniques give benefit to both players, i.e., users and MG. The proposed technique Gray wolf optimized Auto Regressive Integrated Moving Average (GARIMA) gives 40% better result and Cuckoo Search Auto Regressive Integrated Moving Average (CARIMA) gives 30% better results as compared to existing techniques. Full article

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

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16 pages, 2193 KiB

Open AccessArticle

Three-Phase Unbalanced Optimal Power Flow Using Holomorphic Embedding Load Flow Method

by Bharath Varsh Rao, Friederich Kupzog and Martin Kozek

Sustainability 2019, 11(6), 1774; https://doi.org/10.3390/su11061774 - 24 Mar 2019

Cited by 16 | Viewed by 4009

Abstract

Distribution networks are typically unbalanced due to loads being unevenly distributed over the three phases and untransposed lines. Additionally, unbalance is further increased with high penetration of single-phased distributed generators. Load and optimal power flows, when applied to distribution networks, use models developed [...] Read more.

Distribution networks are typically unbalanced due to loads being unevenly distributed over the three phases and untransposed lines. Additionally, unbalance is further increased with high penetration of single-phased distributed generators. Load and optimal power flows, when applied to distribution networks, use models developed for transmission grids with limited modification. The performance of optimal power flow depends on external factors such as ambient temperature and irradiation, since they have strong influence on loads and distributed energy resources such as photo voltaic systems. To help mitigate the issues mentioned above, the authors present a novel class of optimal power flow algorithm which is applied to low-voltage distribution networks. It involves the use of a novel three-phase unbalanced holomorphic embedding load flow method in conjunction with a non-convex optimization method to obtain the optimal set-points based on a suitable objective function. This novel three-phase load flow method is benchmarked against the well-known power factory Newton-Raphson algorithm for various test networks. Mann-Whitney U test is performed for the voltage magnitude data generated by both methods and null hypothesis is accepted. A use case involving a real network in Austria and a method to generate optimal schedules for various controllable buses is provided. Full article

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

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18 pages, 10559 KiB

Open AccessArticle

Optimal Routing an Ungrounded Electrical Distribution System Based on Heuristic Method with Micro Grids Integration

by Wilson Pavón, Esteban Inga and Silvio Simani

Sustainability 2019, 11(6), 1607; https://doi.org/10.3390/su11061607 - 16 Mar 2019

Cited by 17 | Viewed by 3713

Abstract

This paper proposes a three-layer model to find the optimal routing of an underground electrical distribution system, employing the PRIM algorithm as a graph search heuristic. In the algorithm, the first layer handles transformer allocation and medium voltage network routing, the second layer [...] Read more.

This paper proposes a three-layer model to find the optimal routing of an underground electrical distribution system, employing the PRIM algorithm as a graph search heuristic. In the algorithm, the first layer handles transformer allocation and medium voltage network routing, the second layer deploys the low voltage network routing and transformer sizing, while the third presents a method to allocate distributed energy resources in an electric distribution system. The proposed algorithm routes an electrical distribution network in a georeferenced area, taking into account the characteristics of the terrain, such as streets or intersections, and scenarios without squared streets. Moreover, the algorithm copes with scalability characteristics, allowing the addition of loads with time. The model analysis discovers that the algorithm reaches a node connectivity of 100%, satisfies the planned distance constraints, and accomplishes the optimal solution of underground routing in a distribution electrical network applied in a georeferenced area. Simulating the electrical distribution network tests that the voltage drop is less than 2% in the farthest node. Full article

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

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16 pages, 3485 KiB

Open AccessArticle

A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning

by Seul-Gi Kim, Jae-Yoon Jung and Min Kyu Sim

Sustainability 2019, 11(5), 1501; https://doi.org/10.3390/su11051501 - 12 Mar 2019

Cited by 89 | Viewed by 10260

Abstract

Photovoltaic systems have become an important source of renewable energy generation. Because solar power generation is intrinsically highly dependent on weather fluctuations, predicting power generation using weather information has several economic benefits, including reliable operation planning and proactive power trading. This study builds [...] Read more.

Photovoltaic systems have become an important source of renewable energy generation. Because solar power generation is intrinsically highly dependent on weather fluctuations, predicting power generation using weather information has several economic benefits, including reliable operation planning and proactive power trading. This study builds a model that predicts the amounts of solar power generation using weather information provided by weather agencies. This study proposes a two-step modeling process that connects unannounced weather variables with announced weather forecasts. The empirical results show that this approach improves a base approach by wide margins, regardless of types of applied machine learning algorithms. The results also show that the random forest regression algorithm performs the best for this problem, achieving an R-squared value of 70.5% in the test data. The intermediate modeling process creates four variables, which are ranked with high importance in the post-analysis. The constructed model performs realistic one-day ahead predictions. Full article

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

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26 pages, 5986 KiB

Open AccessEditor’s ChoiceArticle

Energy Management and Optimization of a PV/Diesel/Battery Hybrid Energy System Using a Combined Dispatch Strategy

by Ali Saleh Aziz, Mohammad Faridun Naim Tajuddin, Mohd Rafi Adzman, Makbul A. M. Ramli and Saad Mekhilef

Sustainability 2019, 11(3), 683; https://doi.org/10.3390/su11030683 - 28 Jan 2019

Cited by 196 | Viewed by 9693

Abstract

In recent years, the concept of hybrid energy systems (HESs) is drawing more attention for electrification of isolated or energy-deficient areas. When optimally designed, HESs prove to be more reliable and economical than single energy source systems. This study examines the feasibility of [...] Read more.

In recent years, the concept of hybrid energy systems (HESs) is drawing more attention for electrification of isolated or energy-deficient areas. When optimally designed, HESs prove to be more reliable and economical than single energy source systems. This study examines the feasibility of a combined dispatch (CD) control strategy for a photovoltaic (PV)/diesel/battery HES by combining the load following (LF) strategy and cycle charging (CC) strategy. HOMER software is used as a tool for optimization analysis by investigating the techno-economic and environmental performance of the proposed system under the LF strategy, CC strategy, and combined dispatch CD strategy. The simulation results reveal that the CD strategy has a net present cost (NPC) and cost of energy (COE) values of $110,191 and $0.21/kWh, which are 20.6% and 4.8% lower than those of systems utilizing the LF and CC strategies, respectively. From an environmental point of view, the CD strategy also offers the best performance, with CO₂ emissions of 27,678 kg/year. Moreover, the results show that variations in critical parameters, such as battery minimum state of charge, time step, solar radiation, diesel price, and load growth, exert considerable effects on the performance of the proposed system. Full article

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

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24 pages, 8197 KiB

Open AccessArticle

A Model for Predicting Energy Usage Pattern Types with Energy Consumption Information According to the Behaviors of Single-Person Households in South Korea

by Sol Kim, Sungwon Jung and Seung-Man Baek

Sustainability 2019, 11(1), 245; https://doi.org/10.3390/su11010245 - 7 Jan 2019

Cited by 19 | Viewed by 5270

Abstract

Residential energy consumption accounts for the majority of building energy consumption. Physical factors and technological developments to address this problem have been researched continuously. However, physical improvements have limitations, and there is a paradigm shift towards energy research based on occupant behavior. Furthermore, [...] Read more.

Residential energy consumption accounts for the majority of building energy consumption. Physical factors and technological developments to address this problem have been researched continuously. However, physical improvements have limitations, and there is a paradigm shift towards energy research based on occupant behavior. Furthermore, the rapid increase in the number of single-person households around the world is decreasing residential energy efficiency, which is an urgent problem that needs to be solved. This study prepared a large dataset for analysis based on the Korean Time Use Survey (KTUS), which provides behavioral data for actual occupants of single-person households, and energy usage pattern (EUP) types that were derived through K-modes clustering. The characteristics and energy consumption of each type of household were analyzed, and their relationships were examined. Finally, an EUP-type predictive model, with a prediction rate of 95.0%, was implemented by training a support vector machine, and an energy consumption information model based on a Gaussian process regression was provided. The results of this study provide useful basic data for future research on energy consumption based on the behaviors of occupants, and the method proposed in this study will also be applicable to other regions. Full article

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

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17 pages, 1218 KiB

Open AccessArticle

Feasibility Analysis of Behind-the-Meter Energy Storage System According to Public Policy on an Electricity Charge Discount Program

by Byuk-Keun Jo, Seungmin Jung and Gilsoo Jang

Sustainability 2019, 11(1), 186; https://doi.org/10.3390/su11010186 - 1 Jan 2019

Cited by 15 | Viewed by 4439

Abstract

Energy storage systems are crucial in dealing with challenges from the high-level penetration of renewable energy, which has inherently intermittent characteristics. For this reason, various incentive schemes improving the economic profitability of energy storage systems are underway in many countries with an aim [...] Read more.

Energy storage systems are crucial in dealing with challenges from the high-level penetration of renewable energy, which has inherently intermittent characteristics. For this reason, various incentive schemes improving the economic profitability of energy storage systems are underway in many countries with an aim to expand the participation rate. The electricity charge discount program, which was introduced in 2015 in Korea, is one of the policies meant to support the economic feasibility of demand-side energy storage systems. This paper quantitatively evaluated the impact of the electricity charge discount program on the economic feasibility of behind-the-meter energy storage systems. In this work, we first summarized how electricity customers can benefit from behind-the-meter energy storage systems. In addition, we represented details of the structure that make up the electricity charge discount program, i.e., how the electricity charge is discounted through the discount scheme. An optimization problem that establishes a charge and discharge schedule of an energy storage system to minimize each consumer’s electricity expenditure was defined and formulated as well. The case study results indicated that the electricity charge discount program has improved the profitability of behind-the-meter energy storage systems, and this improved profitability led to investment in behind-the-meter energy storage systems in Korea. As a result of the electricity charge discount program, Korea’s domestic demand side energy storage system market size, which was only 27 billion dollars in 2015 in Korea, has grown to 825 billion dollars in 2018. Full article

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

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28 pages, 2305 KiB

Open AccessArticle

An Adaptable Engineering Support Framework for Multi-Functional Energy Storage System Applications

by Claudia Zanabria, Filip Pröstl Andrén and Thomas I. Strasser

Sustainability 2018, 10(11), 4164; https://doi.org/10.3390/su10114164 - 12 Nov 2018

Cited by 4 | Viewed by 3813

Abstract

A significant integration of energy storage systems is taking place to offer flexibility to electrical networks and to mitigate side effects of a high penetration of distributed energy resources. To accommodate this, new processes are needed for the design, implementation, and proof-of-concept of [...] Read more.

A significant integration of energy storage systems is taking place to offer flexibility to electrical networks and to mitigate side effects of a high penetration of distributed energy resources. To accommodate this, new processes are needed for the design, implementation, and proof-of-concept of emerging storage systems services, such as voltage and frequency regulation, and reduction of energy costs, among others. Nowadays, modern approaches are getting popular to support engineers during the design and development process of such multi-functional energy storage systems. Nevertheless, these approaches still lack flexibility needed to accommodate changing practices and requirements from control engineers and along the development process. With that in mind, this paper shows how a modern development approach for rapid prototyping of multi-functional battery energy storage system applications can be extended to provide this needed flexibility. For this, an expert user is introduced, which has the sole purpose of adapting the existing engineering approach to fulfill any new requirements from the control engineers. To achieve this, the expert user combines concepts from model-driven engineering and ontologies to reach an adaptable engineering support framework. As a result, new engineering requirements, such as new information sources and target platforms, can be automatically included into the engineering approach by the expert user, providing the control engineer with further support during the development process. The usefulness of the proposed solution is shown with a selected use case related to the implementation of an application for a battery energy storage system. It demonstrates how the expert user can fully adapt an existing engineering approach to the control engineer’s needs and thus increase the effectiveness of the whole engineering process. Full article

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

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25 pages, 4220 KiB

Open AccessArticle

Research on the Evaluation Model of a Smart Grid Development Level Based on Differentiation of Development Demand

by Jinchao Li, Tianzhi Li and Liu Han

Sustainability 2018, 10(11), 4047; https://doi.org/10.3390/su10114047 - 5 Nov 2018

Cited by 18 | Viewed by 4403

Abstract

In order to eliminate the impact of inter-regional differentiation of development demand on the objective evaluation of the development level of smart grid, this paper establishes the evaluation model of weight modification, transmission mechanism and combination of subjective and objective weights. Firstly, the [...] Read more.

In order to eliminate the impact of inter-regional differentiation of development demand on the objective evaluation of the development level of smart grid, this paper establishes the evaluation model of weight modification, transmission mechanism and combination of subjective and objective weights. Firstly, the Analytic Hierarchy Process method is used to calculate the weights of evaluation indices of effect layer and then the indices of development demand are used to modify the weights of them. The association analysis and the correlation coefficient are used to establish the weights conduction coefficient between the effect level and the base level. Then the subjective weights of the indices of the base layer are calculated. The objective weights of the indices of the base layer are obtained by using the entropy method. The subjective weights of the base layer and the objective weights obtained by the entropy method are averagely calculated, and the comprehensive weights of the evaluation indices of the base layer are obtained. Then each index is scored according to the weights and index values. Finally, the model is used to quantitatively inspect the level of development of smart grid in specific regions and make a horizontal comparison, which provides a useful reference for the development of smart grids. The relevant examples verify the correctness and validity of the model. Full article

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

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18 pages, 1867 KiB

Open AccessArticle

Introduction of Smart Grid Station Configuration and Application in Guri Branch Office of KEPCO

by Jaehong Whang, Woohyun Hwang, Yeuntae Yoo and Gilsoo Jang

Sustainability 2018, 10(10), 3512; https://doi.org/10.3390/su10103512 - 30 Sep 2018

Cited by 3 | Viewed by 4164

Abstract

Climate change and global warming are becoming important problems around the globe. To prevent these environmental problems, many countries try to reduce their emissions of greenhouse gases (GHGs) and manage the consumption of energy. The Korea Electric Power Corporation (KEPCO) introduced smart grid [...] Read more.

Climate change and global warming are becoming important problems around the globe. To prevent these environmental problems, many countries try to reduce their emissions of greenhouse gases (GHGs) and manage the consumption of energy. The Korea Electric Power Corporation (KEPCO) introduced smart grid (SG) technologies to its branch office in 2014. This was the first demonstration of a smart grid on a building, called the Smart Grid Station (SGS). However, the smart grid industry is stagnant despite of the efforts of KEPCO. The authors analyzed the achievements to date, and proved the effects of the SGS by comparing its early targets to its performance. To evaluate the performance, we analyzed the data of 2015 with the data of 2014 in three aspects: peak reduction, power consumption reduction, and electricity fee savings. Furthermore, we studied the economic analysis including photovoltaic (PV) and energy storage system (ESS) electricity fee savings, as well as running cost savings by electric vehicles. Through the evaluation, the authors proved that the performance surpassed the early targets and that the system is economical. With the advantages of the SGS, we suggested directions to expand the system. Full article

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

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25 pages, 5255 KiB

Open AccessArticle

Analyses of Distributed Generation and Storage Effect on the Electricity Consumption Curve in the Smart Grid Context

by Simona-Vasilica Oprea, Adela Bâra, Adina Ileana Uță, Alexandru Pîrjan and George Căruțașu

Sustainability 2018, 10(7), 2264; https://doi.org/10.3390/su10072264 - 1 Jul 2018

Cited by 16 | Viewed by 4119

Abstract

The householders’ electricity consumption is about 20–30% of the total consumption that is a significant space for demand response. Mainly, the householders are becoming more and more active and interested in diminishing their expenses related to the electricity consumption, considering different rates of [...] Read more.

The householders’ electricity consumption is about 20–30% of the total consumption that is a significant space for demand response. Mainly, the householders are becoming more and more active and interested in diminishing their expenses related to the electricity consumption, considering different rates of the advanced tariffs. Therefore, in the smart grid context, especially for prosumers with energy sources and storage devices (SD), the electricity consumption optimization becomes attractive since they obtain significant benefits. On the other hand, the electricity suppliers design appropriate tariffs in order to reduce the consumption peaks and avoid the occurrence of new peaks. Based on the effect of these tariffs on consumers’ behavior, the stress on generators decreases and the electricity suppliers improve the demand forecast and adjust their strategies on the market. In addition, the grid operators are interested in the minimization of the consumption peak that leads to loss reduction and avoidance of congestions that would ensure at least the delay of the onerous investment in grid capacities. In this paper, we will run several scenarios for electricity consumption optimization in the context of smart grid that includes: sensors, actuators, smart meters, advanced tariff schemes, smart appliances and electricity home control applications. Our goal is to analyze the effect of the Renewable Energy Systems (RES) distributed generation (such as photovoltaic panels—PV) and storage on the consumption curve. The results show that consumption optimization with RES distributed generation and SD brings sustainable development of the power systems and significant benefits from the consumption peak and savings point of view. Full article

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

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15 pages, 3452 KiB

Open AccessArticle

Two-Stage Coordinate Optimal Scheduling of Seawater Pumped Storage in Active Distribution Networks

by Ning Liang, Changhong Deng, Yahong Chen, Weiwei Yao, Dinglin Li, Man Chen and Peng Peng

Sustainability 2018, 10(6), 2014; https://doi.org/10.3390/su10062014 - 14 Jun 2018

Cited by 5 | Viewed by 2870

Abstract

The percentage of penetration in renewable energy generation (REG) in distribution networks has dramatically increased. Variable speed seawater pumped storage, which has a large power controllable range and flexible modes of operation, is an important tool to be applied in distribution networks to [...] Read more.

The percentage of penetration in renewable energy generation (REG) in distribution networks has dramatically increased. Variable speed seawater pumped storage, which has a large power controllable range and flexible modes of operation, is an important tool to be applied in distribution networks to realize peak shaving and valley filling, and to mitigate the negative effects of REG. This paper presents a two-stage coordinated optimal scheduling model for the day-ahead and real-time operation of active distribution networks containing seawater pumped storage, REG, and flexible loads. In the model, seawater pumped storage and flexible loads are dispatched in the first day-ahead stage based on short-term forecast information of REG and load demands to minimize total operational costs. Then in the second real-time stage, the operation schedule of seawater pumped storage is adjusted to mitigate the negative effects of forecast errors of REG on the operation of active distribution networks. Network nodes power quality is improved and power loss is reduced. Applying the model, disadvantages of low accuracy short-term forecast are minimized whereas advantages of high accuracy ultra-short term forecast are fully taken. This model is tested using a modified Institute of Electrical and Electronics Engineers 33-bus system. Numerical results demonstrate the effectiveness of the proposed approach. Full article

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

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Review

Jump to: Research

22 pages, 598 KiB

Open AccessReview

Framing Smart Meter Feedback in Relation to Practice Theory

by Hanna Mela, Juha Peltomaa, Marja Salo, Kirsi Mäkinen and Mikael Hildén

Sustainability 2018, 10(10), 3553; https://doi.org/10.3390/su10103553 - 3 Oct 2018

Cited by 19 | Viewed by 7397

Abstract

Smart metering is advancing rapidly and consumption feedback from smart meters is expected to help residents to reduce their energy and water consumption. In recent years, more critical views have been expressed based on theories of social practice, arguing that smart meter feedback [...] Read more.

Smart metering is advancing rapidly and consumption feedback from smart meters is expected to help residents to reduce their energy and water consumption. In recent years, more critical views have been expressed based on theories of social practice, arguing that smart meter feedback ignores the role of various mundane practices where energy and water are consumed and instead targets individuals as active decision-makers. We present a review of qualitative studies on smart meter feedback and results of a survey to European smart metering projects. We argue that theories of social practice can be used to reframe the challenges and potentials of smart meter feedback that have been identified in the literature and our survey. This presents challenges of smart meter feedback as resulting from normalised resource intensive practices rather than from uninterested and comfort-loving individuals. Potentials of improving the effectiveness of smart meter feedback relate to supporting communities and peer-learning and combining smart meter feedback with micro-generation of renewable energy. This has implications for how domestic energy and water consumption is targeted by policy. Full article

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

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23 pages, 644 KiB

Open AccessReview

Moral Values as Factors for Social Acceptance of Smart Grid Technologies

by Christine Milchram, Geerten Van de Kaa, Neelke Doorn and Rolf Künneke

Sustainability 2018, 10(8), 2703; https://doi.org/10.3390/su10082703 - 1 Aug 2018

Cited by 58 | Viewed by 8414

Abstract

Smart grid technologies are considered an important enabler in the transition to more sustainable energy systems because they support the integration of rising shares of volatile renewable energy sources into electricity networks. To implement them in a large scale, broad acceptance in societies [...] Read more.

Smart grid technologies are considered an important enabler in the transition to more sustainable energy systems because they support the integration of rising shares of volatile renewable energy sources into electricity networks. To implement them in a large scale, broad acceptance in societies is crucial. However, a growing body of research has revealed societal concerns with these technologies. To achieve sustainable energy systems, such concerns should be taken into account in the development of smart grid technologies. In this paper, we show that many concerns are related to moral values such as privacy, justice, or trust. We explore the effect of moral values on the acceptance of smart grid technologies. The results of our systematic literature review indicate that moral values can be both driving forces and barriers for smart grid acceptance. We propose that future research striving to understand the role of moral values as factors for social acceptance can benefit from an interdisciplinary approach bridging literature in ethics of technology with technology acceptance models. Full article

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

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