Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Browser

Control, Optimization and Planning of Power Distribution Systems

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 21456

Share This Special Issue

Special Issue Editors

Dr. Gian Giuseppe Soma

E-Mail Website
Guest Editor

Department of Electrical and Electronic Engineering, University of Cagliari, Via Marengo 2, 09123 Cagliari, Italy
Interests: distribution network planning; microgrids; distributed generation; optimization; distribution network optimization; electric vehicles; active distribution networks; ancillary services markets
Special Issues, Collections and Topics in MDPI journals

Dr. Giuditta Pisano

E-Mail Website
Co-Guest Editor

University of Cagliari, via Marengo 2, 09123 Cagliari, Italy
Interests: smart distribution network planning and operation; distributed generation; demand response; energy storage systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of renewable energy sources is moving generation from the top to the bottom of power systems, where, traditionally, only loads existed. Active demand, distribution energy storage devices, and electric vehicles are going to drastically change the way distribution systems will be operated. In opposition to conventional approaches, modern distribution planning algorithms should emulate the new environment to produce expansion and strategic plans for guiding the evolution of system in times of financial austerity.

In addition, the integration of smart grid operation within planning algorithms is a key point for proper distribution planning that allows for the integration of renewable resources, and cost minimization, for new electrical infrastructures.

In this Special Issue, we invite original submissions of new research outcomes that highlight innovations in the areas of control, optimization, and the planning of power distribution networks.

Topics of interests include but are not limited to the following:

Innovative planning techniques of MV and LV distribution networks;
Probabilistic approach to power distribution network planning;
Multi-objective approach to power distribution network planning;
Smart management of distributed energy resources in power distribution networks;
Regulatory requirements for innovative power distribution network planning;
Power distribution network planning with innovative no-network solutions (flexibility exploitation);
Risk analysis in power distribution network planning;
Control of power distribution networks;
Optimization of power distribution networks;
Impact of the electric vehicles in distribution network planning;
MV and LV distribution networks with microgrids/nanogrids architectures.

Dr. Gian Giuseppe Soma
Dr. Giuditta Pisano
Guest Editors

Manuscript Submission Information

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

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

Distribution network planning
Distribution network management
Distribution network control
Distribution network optimization
Multi-objective optimization
Probabilistic planning
Active distribution networks
Active management
Flexibility
Distributed energy resources
Risk assessment
Electric vehicles
Microgrids
Nanogrids.

Published Papers (10 papers)

Download All Papers

Editorial

Jump to: Research

3 pages, 176 KiB

Open AccessEditorial

Special Issue: Control, Optimization and Planning of Power Distribution Systems

by Gian Giuseppe Soma

Appl. Sci. 2022, 12(19), 9978; https://doi.org/10.3390/app12199978 - 04 Oct 2022

Viewed by 623

Abstract

The use of renewable energy sources is moving the generation from the top to the bottom of power systems, where traditionally only loads existed [...] Full article

(This article belongs to the Special Issue Control, Optimization and Planning of Power Distribution Systems)

Research

Jump to: Editorial

21 pages, 7708 KiB

Open AccessArticle

Quantitative Assessment of Flexibility at the TSO/DSO Interface Subject to the Distribution Grid Limitations

by Nicola Natale, Fabrizio Pilo, Giuditta Pisano and Gian Giuseppe Soma

Appl. Sci. 2022, 12(4), 1858; https://doi.org/10.3390/app12041858 - 11 Feb 2022

Cited by 4 | Viewed by 1717

Abstract

In the last years, renewable energy sources have been changing the power system by making it more challenging to balance the generation and demand at every single point in time. The increasing penetration of distributed generation represents another trend at the distribution level that impacts the exploitation of existing distribution assets. In this context, the flexibility of distributed energy resources connected to the distribution systems may play an important role. The flexibility products are represented by variations in the scheduled/expected active and reactive power setpoints. Recently, regulatory bodies suggested many proposals and undertook actions for enabling new players, such as the distributed energy resources connected to the distribution systems, to provide both system and local services. However, currently, there are still barriers that might limit their effective involvement. Market schemes have been proposed for opening the participation of distributed energy resources in the service markets. This paper proposes an analytical quantification of how much the use of flexibility by the transmission system operator can influence the distribution system operator activities and the expected costs. The final goal is quantifying the flexibility that the transmission system operator can procure from the distribution system without a harmful impact on the distribution network operation. The paper investigates the expected interactions between the use of flexibility for power system balancing and security and the operation of distribution systems. The application of the methodology to a significant Case Study showed that even though the fit and forget approach causes a hypertrophic development of distribution systems to host distributed generation, the transmission system operator cannot obtain the required flexibility services or has to pay extra costs for bottlenecks caused by distribution system operational issues. Full article

(This article belongs to the Special Issue Control, Optimization and Planning of Power Distribution Systems)

► Show Figures

Figure 1

24 pages, 1967 KiB

Open AccessFeature PaperArticle

Decentralized Voltage Optimization Based on the Auxiliary Problem Principle in Distribution Networks with DERs

by Anna Rita Di Fazio, Chiara Risi, Mario Russo and Michele De Santis

Appl. Sci. 2021, 11(10), 4509; https://doi.org/10.3390/app11104509 - 15 May 2021

Cited by 7 | Viewed by 1598

Abstract

This paper addresses the problem of optimizing the voltage profile of radially-operated distribution systems by acting on the active and reactive powers provided by distributed energy resources (DERs). A novel voltage optimization procedure is proposed by adopting a decentralized control strategy. To this aim, a centralized voltage optimization problem (VOP), minimizing the distance of all the nodal voltages from their reference values, is firstly formulated as a strictly-convex quadratic program. Then, the centralized VOP is rewritten by partitioning the network into voltage control zones (VCZs) with pilot nodes. To overcome the lack of strictly convexity determined by the reduction to the pilot nodes, the dual centralized VOP working on the augmented Lagrangian function is reformulated and iteratively solved by the method of multipliers. Finally, a fully-distributed VOP solution is obtained by applying a distributed algorithm based on the auxiliary problem principle, which allows for solving in each VCZ a quadratic programming problem of small dimension and to drive the VCZ solutions toward the overall optimum by an iterative coordination process that requires to exchange among the VCZs only scalar values. The effectiveness and feasibility of the proposed method have been demonstrated via numerical tests on the IEEE 123-bus system. Full article

(This article belongs to the Special Issue Control, Optimization and Planning of Power Distribution Systems)

► Show Figures

Graphical abstract

18 pages, 294 KiB

Open AccessArticle

Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks

by Oscar Danilo Montoya, Walter Gil-González and Luis Fernando Grisales-Noreña

Appl. Sci. 2020, 10(23), 8616; https://doi.org/10.3390/app10238616 - 02 Dec 2020

Cited by 5 | Viewed by 1842

Abstract

This research addresses the problem of the optimal location and sizing distributed generators (DGs) in direct current (DC) distribution networks from the combinatorial optimization. It is proposed a master–slave optimization approach in order to solve the problems of placement and location of DGs, respectively. The master stage applies to the classical Chu & Beasley genetic algorithm (GA), while the slave stage resolves a second-order cone programming reformulation of the optimal power flow problem for DC grids. This master–slave approach generates a hybrid optimization approach, named GA-SOCP. The main advantage of optimal dimensioning of DGs via SOCP is that this method makes part of the exact mathematical optimization that guarantees the possibility of finding the global optimal solution due to the solution space’s convex structure, which is a clear improvement regarding classical metaheuristic optimization methodologies. Numerical comparisons with hybrid and exact optimization approaches reported in the literature demonstrate the proposed hybrid GA-SOCP approach’s effectiveness and robustness to achieve the global optimal solution. Two test feeders compose of 21 and 69 nodes that can locate three distributed generators are considered. All of the computational validations have been carried out in the MATLAB software and the CVX tool for convex optimization. Full article

(This article belongs to the Special Issue Control, Optimization and Planning of Power Distribution Systems)

► Show Figures

Figure 1

20 pages, 1098 KiB

Open AccessArticle

Energy Commitment for a Power System Supplied by Multiple Energy Carriers System using Following Optimization Algorithm

by Mohammad Dehghani, Mohammad Mardaneh, Om Parkash Malik, Josep M. Guerrero, Ruben Morales-Menendez, Ricardo A. Ramirez-Mendoza, José Matas and Abdullah Abusorrah

Appl. Sci. 2020, 10(17), 5862; https://doi.org/10.3390/app10175862 - 24 Aug 2020

Cited by 20 | Viewed by 1682

Abstract

In today’s world, the development and continuation of life require energy. Supplying this energy demand requires careful and scientific planning of the energy provided by a variety of products, such as oil, gas, coal, electricity, etc. A new study on the operation of energy carriers called Energy Commitment (EC) is proposed. The purpose of the EC is to set a pattern for the use of energy carriers to supply energy demand, considering technical and economic constraints. EC is a constrained optimization problem that can be solved by using optimization methods. This study suggests the Following Optimization Algorithm (FOA) to solve the EC problem to achieve technical and economic benefits. Minimizing energy supply costs for the total study period is considered as an objective function. The FOA simulates social relationships among the community members who try to improve their community by following each other. Simulation is carried out on a 10-unit energy system supplied by various types of energy carriers that includes transportation, agriculture, industrial, residential, commercial, and public sectors. The results show that the optimal energy supply for a grid with 0.15447 Millions of Barrels of Oil Equivalent (MBOE) of energy demand costs 9.0922 millions dollar for a 24-h study period. However, if the energy supply is not optimal, the costs of operating energy carriers will increase and move away from the optimal economic situation. The economic distribution of electrical demand between 10 power plants and the amount of production units per hour of the study period is determined. The EC outputs are presented, which include an appropriate pattern of energy carrier utilization, energy demand supply costs, appropriate combination of units, and power plant production. The behavior and process of achieving the answer in the convergence curve for the implementation of FOA on EC indicates the exploration and exploitation capacity of FOA. Based on the simulated results, EC provides more information than Unit Commitment (UC) and analyzes the network more efficiently and deeply. Full article

(This article belongs to the Special Issue Control, Optimization and Planning of Power Distribution Systems)

► Show Figures

Graphical abstract

22 pages, 5098 KiB

Open AccessArticle

A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids

by Mojtaba Mehrzadi, Yacine Terriche, Chun-Lien Su, Peilin Xie, Najmeh Bazmohammadi, Matheus N. Costa, Chi-Hsiang Liao, Juan C. Vasquez and Josep M. Guerrero

Appl. Sci. 2020, 10(14), 4889; https://doi.org/10.3390/app10144889 - 16 Jul 2020

Cited by 7 | Viewed by 4372

Abstract

The dynamic positioning (DP) system is a progressive technology, which is used in marine vessels and maritime structures. To keep the ship position from displacement in operation mode, its thrusters are used automatically to control and stabilize the position and heading of vessels. Hence, the DP load forecasting is already an essential part of DP vessels, which the DP power demand from the power management system (PMS) for thrusting depends on weather conditions. Furthermore, the PMS is used to control power generation, and prevent power failure, limitation. To perform station keeping of vessels by DPS in environmental changes such as wind, waves, capacity, and reliability of the power generators. Hence, a lack of power may lead to lower DP performance, loss of power, and position, which is called shutdown. Therefore, precise DP power demand prediction for maintaining the vessel position can provide the PMS with sufficient information for better performance in a complex decision-making process for the DP vessel. In this paper, the concept of deep learning techniques is introduced into DPS for DP load forecasting. A Levenberg–Marquardt algorithm based on a nonlinear recurrent neural network is employed in this paper for predicting thrusters’ power consumption in sea state variations due to challenges in power generation with the relative degree of accuracy by combining weather parameter dependencies as environmental disturbances. The proposed method evaluates with three traditional forecasting methods through a set of practical real-time DP load and weather parametric data. Numerical analysis has shown that with the proposed method, the future DP load behavior can be predicted more accurately than that obtained from the traditional methods, which greatly assists in operation and planning of power system to maintain system stability, security, reliability, and economics. Full article

(This article belongs to the Special Issue Control, Optimization and Planning of Power Distribution Systems)

► Show Figures

Figure 1

16 pages, 1757 KiB

Open AccessArticle

Stochastic Predictive Energy Management of Multi-Microgrid Systems

by Najmeh Bazmohammadi, Amjad Anvari-Moghaddam, Ahmadreza Tahsiri, Ahmad Madary, Juan C. Vasquez and Josep M. Guerrero

Appl. Sci. 2020, 10(14), 4833; https://doi.org/10.3390/app10144833 - 14 Jul 2020

Cited by 42 | Viewed by 3823

Abstract

Next-generation power systems will require innovative control strategies to exploit existing and potential capabilities of developing renewable-based microgrids. Cooperation of interconnected microgrids has been introduced recently as a promising solution to improve the operational and economic performance of distribution networks. In this paper, a hierarchical control structure is proposed for the integrated operation management of a multi-microgrid system. A central energy management entity at the highest control level is responsible for designing a reference trajectory for exchanging power between the multi-microgrid system and the main grid. At the second level, the local energy management system of individual microgrids adopts a two-stage stochastic model predictive control strategy to manage the local operation by following the scheduled power trajectories. An optimal solution strategy is then applied to the local controllers as operating set-points to be implemented in the system. To distribute the penalty costs resulted from any real-time power deviation systematically and fairly, a novel methodology based on the line flow sensitivity factors is proposed. Simulation and experimental analyses are carried out to evaluate the effectiveness of the proposed approach. According to the simulation results, by adopting the proposed operation management strategy, a reduction of about 47% in the average unplanned daily power exchange of the multi-microgrid system with the main grid can be achieved. Full article

(This article belongs to the Special Issue Control, Optimization and Planning of Power Distribution Systems)

► Show Figures

Figure 1

27 pages, 5615 KiB

Open AccessArticle

Location and Capacity Selection Method for Electric Thermal Storage Heating Equipment Connected to Distribution Network Considering Load Characteristics and Power Quality Management

by Xin-Rui Liu, Fu-Jia Zhang, Qiu-Ye Sun and Peng Jin

Appl. Sci. 2020, 10(8), 2666; https://doi.org/10.3390/app10082666 - 12 Apr 2020

Cited by 6 | Viewed by 1658

Abstract

High-permeability distributed wind power and photovoltaic systems are connected to the distribution network, which exacerbates the volatility and uncertainty of the distribution network. Furthermore, with the increasing demand of heating in winter and environmental protection, the wide use of electric thermal storage heating equipment (ETSHE) can promote distributed renewable energy utilization. However, an unplanned ETSHE connection to the distribution network may cause serious power quality problems. A new method of equipment location and capacity is proposed, which considered the improvement of power quality and load demand characteristics of the distribution network. First, based on heat load portrait technology, the node’s thermal load classification prediction was carried out to provide the data basis for the model solution. Second, the multi-objective optimal location and capacity programming model including harmonic distortion rate, voltage deviation, voltage fluctuation, and ETSHE cost was established. Then, the system nodes were preprocessed based on the sensitivity analysis method to reduce the number of installation nodes to be selected, and a feasible alternative set of installation nodes for the optimal configuration model of ETSHE could be obtained. Finally, the improved multi-objective particle swarm optimization algorithm was used to solve the model, and the data envelope analysis method was used to evaluate the power quality of each access scheme. The analysis of the numerical example shows that it can not only satisfy the user′s heat demand, but also effectively improve the power quality by rationally planning the location and capacity of ETSHE, which achieves the safe and efficient utilization of energy. Full article

(This article belongs to the Special Issue Control, Optimization and Planning of Power Distribution Systems)

► Show Figures

Figure 1

12 pages, 2401 KiB

Open AccessFeature PaperArticle

An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation

by Yaqiong Li, Tongxun Wang and Zhanfeng Deng

Appl. Sci. 2020, 10(3), 1145; https://doi.org/10.3390/app10031145 - 08 Feb 2020

Cited by 1 | Viewed by 1745

Abstract

Due to fluctuating characteristics of loads, dynamic reactive power optimization over a certain time period is essential to provide effective strategies to maintain the security and economic operation of distribution systems. In operation, reactive power compensation devices cannot be adjusted too frequently due to their lifetime constraints. Thus, in this paper, an online reactive power optimization strategy based on the segmentation of multiple predicted load curves is proposed to address this issue, aiming to minimize network losses and at the same time to minimize reactive power-compensation device adjustment times. Based on forecasted time series of loads, the strategy first segments each load curve into several sections by means of thresholding a filtered signal, and then optimizes reactive power dispatch based on average load in each section. Through case studies using a modified IEEE 34-bus system and field measurement of loads, the merits of the proposed strategy is verified in terms of both optimization performance and computational efficiency compared with state-of-the-art methods. Full article

(This article belongs to the Special Issue Control, Optimization and Planning of Power Distribution Systems)

► Show Figures

Figure 1

28 pages, 6658 KiB

Open AccessArticle

Research on Dynamic Risk Assessment and Active Defense Strategy of Active Distribution Network under Ice Weather

by Xin-Rui Liu, Hao Wang, Qiu-Ye Sun and Peng Jin

Appl. Sci. 2020, 10(2), 672; https://doi.org/10.3390/app10020672 - 17 Jan 2020

Cited by 4 | Viewed by 1549

Abstract

With the continuous development of the active distribution network (ADN), the problem of security and stability has become increasingly prominent. From the perspective of improving the defense capability of ADN, a new a multi-angle dynamic risk assessment index system based on the comprehensive vulnerability rate model is proposed in this paper. Risk threshold is used to monitor the status of the distribution network, which determine whether ADN needs to enter the active defense period. The minimum amount of load shedding outside the fault isolation region is regarded as the objective function, considering other constraints such as limited resources, the coordinated active defense strategy (CADS) is formed in this paper. Finally, the accuracy of the comprehensive vulnerability rate and the risk assessment value are verified by example analysis, and the superiority of the CADS is verified by comparing different defense strategies. Full article

(This article belongs to the Special Issue Control, Optimization and Planning of Power Distribution Systems)

► Show Figures