Special Issue on advanced approaches, business models, and novel techniques for management and control of smart grids

©2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioimaton|en=nonPeerReviewed

Feasibility of Innovative Smart Mobility Solutions : A Case Study for Vaasa

The global trend of urbanization and growing environmental awareness have risen concerns and demands to develop cities to become smarter. There is a grave need for ambitious sustainability strategies and projects, which can aid cities intelligently and comprehensively in this task. European Union (EU) launched 2014 the Horizon 2020 program (aka Horizon Europe), aiming to encourage the EU nations and their cities to take action to reach carbon neutrality through projects striving to smart city development. By promoting innovative, efficient, far-reaching, and replicable solutions, from the fields of smart energy production and consumption, traffic and mobility, digitalization and information communication technology, and citizen engagement, the objectives of the smart city strategies can be achieved. Horizon 2020 funded IRIS Smart Cities project was launched in 2017. One of the follower cities in the project has been the City of Vaasa in Finland. Vaasa’s climate objective is to reach carbon neutrality by 2030. In order to achieve this goal, the city has taken several decisive measures to enhance de-carbonization during recent years. One essential target for de-carbonization activities has been traffic and mobility. The primary purpose of the research conducted was to study the smart mobility, vehicle-to-grid (V2G), and second life battery solutions in the IRIS Smart Cities project, demonstrated first by the Lighthouse cities and then to be replicated in the City of Vaasa. The aim was to study which importance and prioritization these particular integrated solutions would receive in the City of Vaasa’s replication plan led by the City of Vaasa’s IRIS project task team of 12 experts, with the contribution of the key partners and stakeholders. Additionally, the aim was to study the potential of the integrated solutions in question to be eventually implemented in the Vaasa environment, and the benefit for the city’s ultimate strategy to reach carbon neutrality by 2030. The secondary object was to study the solutions’ compatibility with the IRIS lighthouse cities’ demonstrations and gathered joined experiences concerning the smart and sustainable mobility and vehicle-to-grid solutions, and utilization of 2nd life batteries. The results of the research indicated, that the innovative smart mobility solutions, including vehicle-to-grid and second life battery schemes, are highly relevant not only to the IRIS Lighthouse cities, but they also present good potential for the City of Vaasa in the long run, being compatible with the city’s climate and de-carbonization goals.© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Uncertainty-observed virtual battery model for an electric vehicle parking lot enabling charger-sharing modelling

With the increase in the penetration of electric vehicles (EVs), there is a substantial need for a proper solution to meet the EVs’ charging demand. Due to the high investment cost of charging stations, the efficient operation of EV chargers is crucial. In this regard, in this paper, charger-sharing charging has been proposed to charge multiple EVs with a single EV charger. However, the existing models cannot model uncertain EV parking lots (EVPLs) with charger-sharing charging. In addition, most presented methods for uncertainty modelling of EVPLs are hard to implement in planning and large-scale system-level studies due to their complicated process and high computational burden. Therefore, in this paper, a virtual battery model has been proposed to model an EVPL enabling the charger-sharing charging modelling considering the uncertainty of arrival and departure. Our proposed approach models the EVPL as a battery with time-variant parameters obtained from EVs’ arrival and departure patterns. The proposed virtual battery model has been validated by comparing its performance on day-ahead (DA) and real-time (RT) power market participation of a 24-bus distribution system owning 12 EVPLs with the scenario-based method. The results show that its performance is similar to scenario-based uncertainty modelling while its computational burden is around 2.24% of the scenario-based model. In addition, the results indicate how by employing our proposed charging-sharing charging, EVPLs can dramatically increase their profit as a result of increasing the number of hosted EVs. In this context, a sufficiently high charging tariff motivates the EVPL owner to accommodate a substantial number of EVs. With only 200 EV chargers, the EVPL can host approximately 3200 EVs, given the characteristics of EVs and EV chargers outlined in the case study section. In contrast, the exclusive charger approach allows only 200 EVs to enter the parking facility and undergo charging.© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Linearized Stochastic Optimization Framework for Day-Ahead Scheduling of a Biogas-Based Energy Hub Under Uncertainty

Energy hubs (EHs), due to their multiple nature in the production, consumption, and storage of energy, as well as the ability to participate in different energy markets, have made their optimal and profitable scheduling important for operators. Considering the literature review, one of the main motivations of this paper is the use of biogas as a pivotal fuel and through production using biomass in the structure of EHs. Therefore, this paper proposes a linearized optimization framework for optimal scheduling of a biogas-based EH for participation in day-ahead (DA) electricity and thermal energy markets. The proposed EH directly converts local biomass into biogas, thereby providing the fuel to generate electricity and thermal. This EH comprises digester, biogas storage, electric heat pump (EHP), biogas burner CHP and boiler, solar farm, electrical storage, and internal electrical and thermal loads. In this framework, the uncertainties related to solar radiation and the DA price are modeled to generate random scenarios using the Monte-Carlo method. The proposed EH is simulated for numerical studies based on data from Finland’s two selected spring and autumn days. The results show the optimal performance of the EH because it can participate in the electricity and thermal markets by using the biogas produced inside it and providing complete internal loads, and earns a decent income. In the autumn, operating the EH is more economical than in the spring. Moreover, comparative results have shown that eliminating the biogas unit and using natural gas significantly increases the expected costs of EH.© 2021 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

A conservative framework for obtaining uncertain bands of multiple wind farms in electric power networks by proposed IGDT-based approach considering decision-maker's preferences

Exploiting clean energy resources (CERs) is an applicable way to enhance sustainable development and have the cleaner production of electricity. On the other hand, variability and intermittency of these clean resources are the important disadvantages for determining the reliable operation of electrical grids. Thus, using the uncertainty modeling techniques seems necessary to have more practical values for the decision-making variables. The current paper demonstrates a novel architecture based on Information Gap Decision Theory (IGDT) to model the randomness of multiple Wind Farms (WFs) existing in electric power networks. Note that employing only the IGDT technique cannot consider the preferences defined by the decision-maker. In contrast, the proposed method tackles this issue by considering different values for radii of uncertainty related to the uncertain parameters. It has been proven that the presented approach is time-saving if compared with Monte Carlo Simulation (MCS) and the Epsilon-constraint-based-IGDT. Moreover, the execution time of the presented methodology does not considerably depend on the number of WFs for a power system. It means that if the number of WFs increases in a particular case study, consequently, the execution time does not noticeably rise if compared with the MCS and the Epsilon-constraint-based-IGDT. Furthermore, the equivalent Mixed Integer Linear Programming (MILP) of the original model is employed to guarantee the optimum solution. The performances of the presented methodology have been demonstrated by utilizing IEEE 30 BUS and IEEE 62 BUS systems.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Integration of DERs in the Aggregator Platform for the Optimal Participation in Wholesale and Local Electricity Markets

Aging and abnormal stresses cause insulation degradation in underground cables, reducing their in-service lifetime. Partial discharge (PD) monitoring is an effective tool to monitor the insulation condition. For growing networks, monitoring solutions need more efficient diagnostics, particularly to classify PD activity by source type. One bottleneck here is feature extraction, for which many computationally expensive techniques have been proposed. This paper presents a more efficient approach, enabling real-time PD classification at high classification performance. It is applied to phase resolved PD cycles, measured on a medium voltage cable in a laboratory environment, containing either internal, corona, or surface discharge activity.©2021 IET. This paper is a postprint of a paper submitted to and accepted for publication in CIRED 2021 - The 26th International Conference and Exhibition on Electricity Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.fi=vertaisarvioitu|en=peerReviewed

Hierarchical Stochastic Frequency Constrained Micro-Market Model for Isolated Microgrids

With the developments of isolated microgrids (IMGs) and prosumers in remote areas, energy trading has emerged as a critical aspect of IMGs. However, the lack of an upstream network and the low inertia of the system may threaten the secure operation of these networks. This paper proposes a Micro-Market (lM) model for IMGs that includes a precise hierarchical control structure. To address the IMGs low inertia and high intermittency of renewable energy sources (RES), the proposed lM manages the active-reactive power and schedules primary and secondary active reserves to maintain the frequency within in a predefined range. Additionally, a bidirectional linearized AC power flow is established to schedule the reactive reserve and the proposed model is formulated as a two-stage stochastic mixed-integer linear problem (MILP) to maximize social welfare (SW) over the next 24 hours. To validate the effectiveness of the proposed model, the lM is tested on an IMG based on a CIGRE medium-voltage benchmark system, and different operational cases are simulated. The results demonstrate that the proposed model, which takes into account hierarchical control levels and technical issues of the IMG, is a cost-effective way to maximize social welfare while ensuring the secure operation of the IMG.©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioimaton|en=nonPeerReviewed

Stochastic-Risk Based Approach for Microgrid Participation in Joint Active, Reactive, and Ancillary Services Markets Considering Demand Response

In the restructured power systems, renewable energy sources (RES) have been developed. Uncertainties of these generators reduce the reliability and stability of power systems. The frequency and voltage for the correct operation of the power systems must always be maintained within a nominal value. Ancillary services (AS), energy storage systems (ESS), and demand response programs (DRPs) can be effective solutions for mentioned problems. Microgrids (MG) can make an improvement in their profits and efficiency by participating in various markets. This paper provides an optimal scheduling for the simultaneous participation of MGs in coupled active, reactive power and AS markets (regulation, spinning reserve and non-spinning reserve) by considering ESS, DRPs, call for deploying AS, and the uncertainties of wind and solar productions. Capability diagrams; mathematical equations are used to model active and reactive power of generation units. Risk management in this paper is done by the conditional value at risk (CVaR) method and probability distribution functions (PDF) are used for modeling uncertainties of wind speed and solar radiation. The ERCOT (Electric Reliability Council of Texas) market is simulated with real world data.©2022 the Authors, published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

Modeling a Local Electricity Market for Transactive Energy Trading of Multi-Aggregators

The present article aims at modeling a day-ahead local electricity market (DA LEM) for transactive energy trading at the distribution level. In this regard, a wide range of distributed energy resources (DERs) in the form of multiple aggregators (AGs) participates in the DA LEM in order to trade energy with the distribution system operator (DSO), the operator of the market. On the other hand, the DSO, as the owner of the system, has the responsibility to procure the required energy of its customers with respect to the technical constraints of the distribution network. To settle the designed local market, a Stackelberg game-based approach is exploited in this research work. In the raised Stackelberg scheme, the leader of the game, the DSO, seeks to maximize its expected profit, while followers of the game, DER AGs, tend to minimize their operating costs. Ultimately, to evaluate the proposed framework, a typical case study is implemented on a modified IEEE-33 bus test system.© Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

On the performance metrics for cyber-physical attack detection in smart grid

Supervisory Control and Data Acquisition (SCADA) systems play an important role in Smart Grid. Though the rapid evolution provides numerous advantages it is one of the most desired targets for malicious attackers. So far security measures deployed for SCADA systems detect cyber-attacks, however, the performance metrics are not up to the mark. In this paper, we have deployed an intrusion detection system to detect cyber-physical attacks in the SCADA system concatenating the Convolutional Neural Network and Gated Recurrent Unit as a collective approach. Extensive experiments are conducted using a benchmark dataset to validate the performance of the proposed intrusion detection model in a smart metering environment. Parameters such as accuracy, precision, and false-positive rate are compared with existing deep learning models. The proposed concatenated approach attains 98.84% detection accuracy which is much better than existing techniques.©The Author(s) 2022 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed