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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (28 February 2024) | Viewed by 21285

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Special Issue Editors

Prof. Dr. Jesús María López-Lezama

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Guest Editor

Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Antioquia, Medellín 050010, Colombia
Interests: smart grids; protection coordination, optimization, integration of distributed energy resources

Dr. Oscar Danilo Montoya

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Guest Editor

Grupo de Compatibilidad e Interferencia Electromagnética (GCEM), Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia
Interests: distribution system planning; renewable energy resources integration; energy storage devices and their applications; nonlinear control in power systems; distribution grids and microgrids; convex and combinatorial optimization
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Nicolás Muñoz-Galeano

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Guest Editor

Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Antioquia, Medellín 050010, Colombia
Interests: power electronics; smart grids; renewable generation; protection coordination; AC/DC power converters

Special Issue Information

Dear Colleagues,

Smart grids allow the integration of distributed energy resources such as battery energy storage systems, distributed generation, demand response, etc. Furthermore, they incentive more active participation of end users and the creation of energy communities. Nonetheless, smart grids also bring along new challenges for system planners and system operators.

The main purpose of this Special Issue is to publish high-quality original research papers as well as review articles addressing recent advances in smart grids. Any research topic contributing to the advancement of smart grid technologies and applications will be considered in this Special Issue. Topics of interest include, but are not limited to, the following:

Smart grid design and operation;
DC power microgrids design and operation;
Hybrid microgrids with DC and AC power system integration;
Power electronics to integrate distributed PV and battery storage in microgrids;
Innovative power converter topologies applied in smart grids;
Smart grids protection coordination issues;
Power converters design and modeling in smart grids;
Control strategies for grid-connected mode and island modes in smart grids;
Communication networks and protocols in smart grids;
DC power system protection and earthing;
Demand response and flexible loads with grid integration;
Vehicle to grid (V-2-G) integration in smart grids;
Smart grid design focusing on resilience and disruptive events;
Nonlinear control for energy storage systems and renewable energy.

Prof. Dr. Jesús María López-Lezama
Dr. Oscar Danilo Montoya
Prof. Dr. Nicolás Muñoz-Galeano
Guest Editors

Manuscript Submission Information

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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. Sustainability 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 2400 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
energy communities
communication systems
power converters
distributed energy resources
demand response
storage systems

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Published Papers (10 papers)

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Research

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20 pages, 2815 KiB

Open AccessArticle

A Transformer Heavy Overload Spatiotemporal Distribution Prediction Ensemble under Imbalanced and Nonlinear Data Scenarios

by Yanzheng Liu, Chenhao Sun, Xin Yang, Zhiwei Jia, Jianhong Su and Zhijie Guo

Sustainability 2024, 16(8), 3110; https://doi.org/10.3390/su16083110 - 9 Apr 2024

Cited by 1 | Viewed by 1063

Abstract

As a crucial component of power systems, distribution transformers are indispensable to ensure the sustainability of power supply. In addition, unhealthy transformers can lead to wasted energy and environmental pollution. Thus, accurate assessments and predictions of their health statuses have become a top priority. Unlike assumed ideal environments, however, some complex data distributions in practical scenarios lead to more difficulties in diagnosis. One challenge here is the potential imbalanced distribution of data factors since sparsely occurring factors along with some Unusual High-Risk (UHR) components, whose appearance may also damage transformer operations, can easily be neglected. Another is that the importance weight of data components is simply calculated according to their frequency or proportion, which may not always be reasonable in real nonlinear data scenes. With such motivations, this paper proposes a novel integrated method combining the Two-fold Conditional Connection Pattern Recognition (TCCPR) and Component Significance Diagnostic (CSD) models. Initially, the likely environmental factors that could result in distribution transformer heavy overloads were incorporated into an established comprehensive evaluation database. The TCCPR model included the UHR time series and factors that are associated with heavy overload in both spatial and temporal dimensions. The CSD model was constructed to calculate the risk impact weights of each risky component straightforwardly, in line with the total risk variation levels of the whole system caused by them. Finally, the results of one empirical case study demonstrated their adaptation capability and enhanced performance when applied in complex and imbalanced multi-source data scenes. Full article

(This article belongs to the Special Issue Smart Grid Technologies and Applications)

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21 pages, 5290 KiB

Open AccessArticle

A Fully Decentralized Optimal Dispatch Scheme for an AC–DC Hybrid Distribution Network Formed by Flexible Interconnected Distribution Station Areas

by Xu Tang, Jingwen Zheng, Zhichun Yang, Xiangling He, Huaidong Min, Sihan Zhou, Kaipei Liu and Liang Qin

Sustainability 2023, 15(14), 11338; https://doi.org/10.3390/su151411338 - 20 Jul 2023

Cited by 1 | Viewed by 1189

Abstract

Due to unbalanced load growth among different regions and the increasing integration of distributed generators (DGs), distribution station areas (DSAs) currently face issues such as voltage violations, curtailment of renewable energy generation, and imbalanced load rates among DSAs. Interconnecting DSAs to form an AC–DC hybrid distribution network (DN) can not only address the aforementioned problems but also provides more efficient interfaces for DC devices. In order to coordinate the controllable devices within the flexible interconnected DSAs and achieve an optimal operational state, centralized optimal dispatch strategies are mainly used, which requires the deployment of an additional central controller and entails heavy communication and computation burdens. To overcome the drawbacks of centralized dispatch, a fully decentralized optimal dispatch scheme based on the alternating direction method of multipliers (ADMM) is proposed. Based on the network partitioning results, the introduction of auxiliary variables that replicate the coupling variables between areas further eliminates the need for a coordinating center in the standard ADMM, achieving a fully decentralized optimal dispatch. Additionally, two network partitioning methods are proposed for implementing decentralized dispatch. Both partitioning methods can achieve the goals of load rate balance and voltage profile improvement when implementing decentralized dispatch. Their key distinction lies in their effectiveness in improving the voltage profiles on the DC side. The partitioning method that treats the entire DC side as a separate area, resulting in higher investment, achieves better results in improving the DC voltage profiles than the other one. The choice of partitioning method can be based on practical engineering requirements. Full article

(This article belongs to the Special Issue Smart Grid Technologies and Applications)

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17 pages, 10825 KiB

Open AccessArticle

Optimal Scheduling of AC–DC Hybrid Distribution Network Considering the Control Mode of a Converter Station

by Xu Tang, Liang Qin, Zhichun Yang, Xiangling He, Huaidong Min, Sihan Zhou and Kaipei Liu

Sustainability 2023, 15(11), 8715; https://doi.org/10.3390/su15118715 - 28 May 2023

Cited by 2 | Viewed by 1743

Abstract

Due to the difference in types of loads between regions and the increasing integration of random elements such as electric vehicles (EVs) and distributed generations (DGs), distribution station areas (DSAs) are facing challenges such as unbalanced load rates and voltage violations. An AC–DC hybrid distribution network formed by interconnecting AC-DSAs using flexible DC technology can not only address these issues, but also offer more efficient interfaces for EV charging piles and DC devices on the DC side. To fully leverage the advantages of the technology and coordinate dispatchable elements within each DSA, this paper proposes an optimal scheduling model, which balances the load rate between DSAs, improves voltage profiles, and considers the control mode of the converter station as a dispatchable element, taking into account its impact on the voltage deviation on the DC side. Simulation results demonstrate the effectiveness of the proposed model in balancing load rate and improving voltage profiles. Moreover, rational decision-making regarding the selection of the control mode for converter stations can effectively mitigate the voltage deviation on the DC side without deteriorating the voltage deviation on the AC side. Full article

(This article belongs to the Special Issue Smart Grid Technologies and Applications)

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24 pages, 2026 KiB

Open AccessArticle

Optimal Allocation and Sizing of Distributed Generation Using Interval Power Flow

by Wallisson C. Nogueira, Lina P. Garcés Negrete and Jesús M. López-Lezama

Sustainability 2023, 15(6), 5171; https://doi.org/10.3390/su15065171 - 14 Mar 2023

Cited by 5 | Viewed by 2008

Abstract

Modern distribution systems and microgrids must deal with high levels of uncertainty in their planning and operation. These uncertainties are mainly due to variations in loads and distributed generation (DG) introduced by new technologies. This scenario brings new challenges to planners and system operators that need new tools to perform more assertive analyses of the grid state. This paper presents an optimization methodology capable of considering uncertainties in the optimal allocation and sizing problem of DG in distribution networks. The proposed methodology uses an interval power flow (IPF) that adds uncertainties to the combinatorial optimization problem in charge of sizing and allocating DG units in the network. Two metaheuristics were implemented for comparative purposes, namely, symbiotic organism search (SOS) and particle swarm optimization (PSO). The proposed methodology was implemented in Python^® using as benchmark distribution systems the IEEE 33-bus and IEEE 69-bus test distribution networks. The objective function consists of minimizing technical losses and regulating network voltage levels. The results obtained from the proposed IPF on the tested networks are compatible with those obtained by the PPF, thus evidencing the robustness and applicability of the proposed method. For the solution of the optimization problem, the SOS metaheuristic proved to be robust, since it was able to find the best solutions (lowest losses) while keeping voltage levels within the predetermined range. On the other hand, the PSO metaheuristic showed less satisfactory results, since for all test systems, the solutions found were of lower quality than the ones found by the SOS. Full article

(This article belongs to the Special Issue Smart Grid Technologies and Applications)

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20 pages, 517 KiB

Open AccessArticle

Optimal Feeder Reconfiguration and Placement of Voltage Regulators in Electrical Distribution Networks Using a Linear Mathematical Model

by Luis A. Gallego Pareja, Jesús M. López-Lezama and Oscar Gómez Carmona

Sustainability 2023, 15(1), 854; https://doi.org/10.3390/su15010854 - 3 Jan 2023

Cited by 8 | Viewed by 2399

Abstract

Power distribution systems face continuous challenges from increased demand and lengthening of feeders, resulting in power loss augmentation and unacceptable voltage drops. Thus, to reduce technical losses and improve the voltage profile, common techniques such as reactive compensation, network reconfiguration, and placing of voltage regulators are employed. Distribution network reconfiguration (DNR) consists of modifying the system topology with the aim of minimizing power losses, enhancing voltage profile, and improving network reliability. Optimal placement of voltage regulators (OPVRs) improves the voltage profile and helps to reduce power losses. DNR and OPVRs are challenging optimization problems involving both integer and continuous decision variables. In this paper, a mixed-integer linear programming (MILP) model is presented to simultaneously solve the problems of DNR and OPVRs in radial distribution networks. The combined optimal DNR and OPVRs aim at both the minimization of power losses and the improvement of the voltage profile. This approach has not been reported in the specialized literature. The proposed MILP model may be solved through commercially available software, obtaining global optimal solutions with lower computational effort than metaheuristic techniques applied for the same purpose. Several tests were conducted on three benchmark distribution test systems to demonstrate the efficacy and applicability of the proposed approach. Full article

(This article belongs to the Special Issue Smart Grid Technologies and Applications)

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22 pages, 364 KiB

Open AccessArticle

Optimal Phase-Balancing in Three-Phase Distribution Networks Considering Shunt Reactive Power Compensation with Fixed-Step Capacitor Banks

by Daniel Federico A. Medina-Gaitán, Ian Dwrley Rozo-Rodriguez and Oscar Danilo Montoya

Sustainability 2023, 15(1), 366; https://doi.org/10.3390/su15010366 - 26 Dec 2022

Viewed by 2076

Abstract

The black hole optimization (BHO) method is applied in this research to solve the problem of the optimal reactive power compensation with fixed-step capacitor banks in three-phase networks considering the phase-balancing problem simultaneously. A master–slave optimization approach based on the BHO in the master stage considers a discrete codification and the successive approximation power flow method in the slave stage. Two different evaluations are proposed to measure the impact of the shunt reactive power compensation and the phase-balancing strategies. These evaluations include a cascade solution methodology (CSM) approach and a simultaneous solution methodology (SSM). The CSM approach solves the phase-balancing problem in the first stage. This solution is implemented in the distribution network to determine the fixed-step capacitor banks installed in the second stage. In the SSM, both problems are solved using a unique codification vector. Numerical results in the IEEE 8- and IEEE 27-bus systems demonstrate the effectiveness of the proposed solution methodology, where the SSM presents the better numerical results in both test feeders with reductions of about

32.27 %

and

33.52 %

, respectively, when compared with the CSM. To validate all the numerical achievements in the MATLAB programming environment, the DIgSILENT software was used for making cross-validations. Note that the selection of the DIgISLENT software is based on its wide recognition in the scientific literature and industry for making quasi-experimental validations as a previous stage to the physical implementation of any grid intervention in power and distribution networks. Full article

(This article belongs to the Special Issue Smart Grid Technologies and Applications)

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25 pages, 548 KiB

Open AccessArticle

An Energy Management System for PV Sources in Standalone and Connected DC Networks Considering Economic, Technical, and Environmental Indices

by Luis Fernando Grisales-Noreña, Jauder Alexander Ocampo-Toro, Andrés Alfonso Rosales-Muñoz, Brandon Cortes-Caicedo and Oscar Danilo Montoya

Sustainability 2022, 14(24), 16429; https://doi.org/10.3390/su142416429 - 8 Dec 2022

Cited by 14 | Viewed by 1971

Abstract

This research proposes an efficient energy management system for standalone and grid-connected direct current (DC) distribution networks that consider photovoltaic (PV) generation sources. A complete nonlinear programming model is formulated to represent the efficient PV dispatch problem while taking three different objective functions into account. The first objective function corresponds to the minimization of the operational costs with respect to the energy purchasing costs at terminals of the substation, including the maintenance costs of the PV sources. The second objective function is the reduction of the expected daily energy losses regarding all resistive effects of the distribution lines. The third objective function concerns the minimization of the total emissions of CO

_{2}

into the atmosphere by the substation bus or its equivalent (diesel generator). These objective functions are minimized using a single-objective optimization approach through the application of the Salp Swarm Algorithm (SSA), which is combined with a matrix hourly power flow formulation that works by using a leader–follower operation scheme. Two test feeders composed of 27 and 33 nodes set for standalone and grid-connected operation are used in the numerical validations. The standalone grid corresponds to an adaptation of the generation and demand curves for the municipality of Capurganá, and the grid-connected system is adapted to the operating conditions in the metropolitan area of Medellín, i.e., a rural area and a major city in Colombia. A numerical comparison with three additional combinatorial optimizers (i.e., particle swarm optimization (PSO), the multiverse optimizer (MVO), and the crow search algorithm (CSA)) demonstrates the effectiveness and robustness of the proposed leader–follower optimization approach to the optimal management of PV generation sources in DC grids while considering different objective function indices. Full article

(This article belongs to the Special Issue Smart Grid Technologies and Applications)

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35 pages, 857 KiB

Open AccessArticle

Energy Management System for the Optimal Operation of PV Generators in Distribution Systems Using the Antlion Optimizer: A Colombian Urban and Rural Case Study

by Brandon Cortés-Caicedo, Luis Fernando Grisales-Noreña, Oscar Danilo Montoya, Miguel Angel Rodriguez-Cabal and Javier Alveiro Rosero

Sustainability 2022, 14(23), 16083; https://doi.org/10.3390/su142316083 - 1 Dec 2022

Cited by 17 | Viewed by 2014

Abstract

This paper presents an Energy Management System (EMS) for solving the problem regarding the optimal daily operation of Photovoltaic (PV) distributed generators in Alternate Current (AC) distribution grids. To this effect, a nonlinear programming problem (NLP) was formulated which considered the improvement of [...] Read more.

{C O}_{2}

emission) grid indices as objective functions, considering all technical and operating constraints for the operation of AC networks with the presence of PV sources. To solve this mathematical formulation, a master–slave methodology was implemented, whose master stage employed the antlion optimizer to find the power dispatch of PV sources in each period of time considered (24 h). In the slave stage, an hourly power flow based on the successive approximations method was used in order to obtain the values of the objective functions and constraints associated with each possible PV power configuration proposed by the master stage. To evaluate the effectiveness and robustness of the proposed methodology, two test scenarios were used, which included three installed PV sources in an urban and a rural network, considering the PV power generation and demand located reported for Medellín and Capurganá, respectively. These systems correspond to connected and standalone grids located in two different regions of Colombia. Furthermore, the proposed methodology was compared with three optimization methodologies reported in the literature: the Chu and Beasley genetic algorithm, the particle swarm optimization algorithm, and the vortex search optimization algorithm. Simulation results were obtained via the MATLAB software for both test scenarios with all the optimization methodologies. It was demonstrated that the proposed methodology yields the best results in terms of solution quality and repeatability, with shorter processing times. Full article

(This article belongs to the Special Issue Smart Grid Technologies and Applications)

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32 pages, 1163 KiB

Open AccessArticle

Optimal Power Dispatch of DGs in Radial and Mesh AC Grids: A Hybrid Solution Methodology between the Salps Swarm Algorithm and Successive Approximation Power Flow Method

by Andrés Alfonso Rosales-Muñoz, Jhon Montano, Luis Fernando Grisales-Noreña, Oscar Danilo Montoya and Fabio Andrade

Sustainability 2022, 14(20), 13408; https://doi.org/10.3390/su142013408 - 18 Oct 2022

Cited by 5 | Viewed by 1560

Abstract

In this paper, we address the problem of the optimal power dispatch of Distributed Generators (DGs) in Alternating Current (AC) networks, better known as the Optimal Power Flow (OPF) problem. We used, as the objective function, the minimization of power losses [...] Read more.

(P_{l o s s})

associated with energy transport, which are subject to the set of constraints that compose AC networks in an environment of distributed generation. To validate the effectiveness of the proposed methodology in solving the OPF problem in any network topology, we employed one 10-node mesh test system and three radial text systems: 10, 33, and 69 nodes. In each test system, DGs were allowed to inject

20 %

40 %

, and

60 %

of the power supplied by the slack generator in the base case. To solve the OPF problem, we used a master–slave methodology that integrates the optimization method Salps Swarm Algorithm (SSA) and the load flow technique based on the Successive Approximation (SA) method. Moreover, for comparison purposes, we employed some of the algorithms reported in the specialized literature to solve the OPF problem (the continuous genetic algorithm, the particle swarm optimization algorithm, the black hole algorithm, the antlion optimization algorithm, and the Multi-Verse Optimizer algorithm), which were selected because of their excellent results in solving such problems. The results obtained by the proposed solution methodology demonstrate its superiority and convergence capacity in terms of minimization of

P_{l o s s}

in both radial and mesh systems. It provided the best reduction in minimum

P_{l o s s}

in short processing times and showed excellent repeatability in each test system and scenario under analysis. Full article

(This article belongs to the Special Issue Smart Grid Technologies and Applications)

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Review

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21 pages, 1871 KiB

Open AccessReview

Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting

by Putri Nor Liyana Mohamad Radzi, Muhammad Naveed Akhter, Saad Mekhilef and Noraisyah Mohamed Shah

Sustainability 2023, 15(4), 2942; https://doi.org/10.3390/su15042942 - 6 Feb 2023

Cited by 21 | Viewed by 4251

Abstract

Advancements in renewable energy technology have significantly reduced the consumer dependence on conventional energy sources for power generation. Solar energy has proven to be a sustainable source of power generation compared to other renewable energy sources. The performance of a photovoltaic (PV) system is highly dependent on the amount of solar penetration to the solar cell, the type of climatic season, the temperature of the surroundings, and the environmental humidity. Unfortunately, every renewable’s technology has its limitation. Consequently, this prevents the system from operating to a maximum or optimally. Achieving a precise PV system output power is crucial to overcoming solar power output instability and intermittency performance. This paper discusses an intensive review of machine learning, followed by the types of neural network models under supervised machine learning implemented in photovoltaic power forecasting. The literature of past researchers is collected, mainly focusing on the duration of forecasts for very short-, short-, and long-term forecasts in a photovoltaic system. The performance of forecasting is also evaluated according to a different type of input parameter and time-step resolution. Lastly, the crucial aspects of a conventional and hybrid model of machine learning and neural networks are reviewed comprehensively. Full article

(This article belongs to the Special Issue Smart Grid Technologies and Applications)

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