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Planning, Optimization and Capacity Management of Future Distribution Networks

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Dr. Niyam Haque

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Guest Editor

Electrical Energy Systems, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
Interests: congestion management; voltage control; network planning; demand response; residential energy management; prediction and scheduling

Special Issue Information

Dear Colleagues,

Fundamental changes are expected in power and energy systems all over the world in terms of increasing shares of renewable energy sources (RES)-based local distributed generation (DG) units and price-responsive flexible loads. Due to the intermittent nature of the DG units and reduced diversity of the loads, more and more capacity challenges are appearing in distribution networks in terms of voltage variations, thermal overloading, and power quality issues. The conventional approach of reinforcing them to tackle such issues necessitates a huge investment and is also deemed redundant, as the frequency of such issues is unpredictable. Nevertheless, innovative concepts are being developed worldwide to circumvent the investment and deploy intelligent algorithms powered by advanced ICT-based tools to enhance the overall flexibility and controllability of the networks.

The focus of this Special Issue will therefore be to critically assess the state-of-the-art as well as to analyze and promote innovative solutions for long-term planning and operational constraint management of future distribution networks. The purpose is to bring forth comprehensive examples of suitable demand response programs, algorithms, methodologies, and relevant case studies from all over the world. Specific topics include but are not limited to:

Network planning;
Capacity management;
Data-driven algorithms;
Forecasting and operational planning;
Renewable integration;
Microgrids, virtual power plants, and other aggregation techniques;
Smart grids;
Residential and neighborhood energy management;
Integration of smart buildings;
Uncertainty and risk management;
Flexibility and demand response;
Integration of electric vehicles (EVs);
Integrated energy systems.

The Special Issue will address significant aspects associated with planning and operation of future distribution networks and provide a comprehensive overview of contemporary multidisciplinary research and relevant applications.

Dr. Niyam Haque
Guest Editor

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Research

24 pages, 13473 KiB

Open AccessArticle

Simulation Studies to Quantify the Impact of Demand Side Management on Environmental Footprint

by Sulaiman A. Almohaimeed, Siddharth Suryanarayanan and Peter O’Neill

Sustainability 2021, 13(17), 9504; https://doi.org/10.3390/su13179504 - 24 Aug 2021

Cited by 1 | Viewed by 1923

Abstract

The increased use of energy leads to increased energy-related emissions. Demand side management (DSM) is a potential means of mitigating these emissions from electric utility generating units. DSM can significantly reduce emissions and provide economic and reliability benefits. This work presents some DSM techniques, such as load shifting, energy conservation, and valley filling. Furthermore, this work explains the most common DSM programs. To quantify the effect of DSM in diminishing carbon footprint, this paper performs power flow analysis on a yearly load profile corresponding to Fort Collins, Colorado, U.S. This work used the IEEE 13-node test system to simulate several scenarios from the multi-criteria decision-making (MCDM) alternatives, both individually and integrated. For the base case, emissions decrease by 16% from the 2005 level. The “energy conservation” option achieved a 20% reduction in emissions, integrating both alternatives increased the emissions mitigation up to 22%. Simulation of the residential sector shows the “communication and intelligence” option reduces emissions about 14% from the 2005 level. A scenario that combines “electric stationary storage” with “communication and intelligence” diminishes the emissions by more than 15%. The last scenario examined all MCDM alternatives combined into one option, resulting in a 20% emissions reduction. We also conducted a cost benefit analysis (CBA) to investigate economic, technical, and environmental costs and benefits associated with each alternative. The economic evaluation shows that “electric stationary storage” is the best option since it charges during lower electricity prices and discharges during peaking demand. The economic analysis presents a trade-off chart, so the decision maker can select the alternative based on their preference. Full article

(This article belongs to the Special Issue Planning, Optimization and Capacity Management of Future Distribution Networks)

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22 pages, 4908 KiB

Open AccessArticle

Development of an Improved Bonobo Optimizer and Its Application for Solar Cell Parameter Estimation

by Reem Y. Abdelghany, Salah Kamel, Hamdy M. Sultan, Ahmed Khorasy, Salah K. Elsayed and Mahrous Ahmed

Sustainability 2021, 13(7), 3863; https://doi.org/10.3390/su13073863 - 31 Mar 2021

Cited by 26 | Viewed by 2419

Abstract

Recently, photovoltaic (PV) energy has been considered one of the most exciting new technologies in the energy sector. PV power plants receive considerable attention because of their wide applications. Consequently, it is important to study the parameters of the solar cell model to control and determine the characteristics of the PV systems. In this study, an improved bonobo optimizer (IBO) was proposed to improve the performance of the conventional bonobo optimizer (BO). Both the IBO and the BO were utilized to obtain the accurate values of the unknown parameters of different mathematical models of solar cells. The proposed IBO improved the performance of the conventional BO by enhancing the exploitation (local search) and exploration (global search) phases to find the best optimal solution, where the search space was reduced using Levy flights and the sine–cosine function. Levy flights enhance the explorative phase, whereas the sine–cosine function improves the exploitation phase. Both the proposed IBO and the conventional BO were applied on single, double, and triple diode models of solar cells. To check the effectiveness of the proposed algorithm, statistical analysis based on the results of 20 runs of the optimization program was performed. The results obtained by the proposed IBO were compared with other algorithms, and all results of the proposed algorithm showed their durability and exceeded other algorithms. Full article

(This article belongs to the Special Issue Planning, Optimization and Capacity Management of Future Distribution Networks)

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