Sustainability

Journal Browser

► Journal Browser

Optimization Application to Power Systems with the High Penetration Rate of Renewable Energy

Share This Special Issue

Special Issue Editor

Special Issue Information

Dear Colleagues,

As climate change grows in importance and visibility around the world, reaching critical levels, one of the policies that different governments aim to adopt is the increased use of renewable energy (RE) for electric power generation, resulting in the reduction of fossil fuel use. However, as the penetration rate of renewable energy increases in power systems, it is also becoming more difficult to maintain adequate levels of system security. Thus, several countermeasures to guarantee security need to be employed in the stages of system operation, operational planning, and planning. To come up with those countermeasures, application of optimization techniques is desirable, as various types of control and operation means need to be well coordinated.

This Special Issue will highlight new technical challenges and advancements in applying optimization methods for power system security in the high RE penetration circumstances. The issue shall cover:

Countermeasure establishment for future power system planning;
Design of operational planning strategies for power system security enhancement;
Establishment of operation strategies, guidelines, and criteria for power systems;
Operation and control coordination for operation scheduling and strategy designs of microgrids and virtual power plants using optimization techniques;
Design of operation and planning strategies for renewable energy and energy storage systems;
Optimization application to forecast and to analyze fluctuation and intermittence characteristics of renewable energy.

Prof. Dr. Hwachang Song
Guest Editor

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. 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

Power system optimization
Renewable energy
High penetration
System security
Operation and planning

Published Papers (2 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

34 pages, 9600 KiB

Open AccessArticle

A Comprehensive Evaluation Model on Optimal Operational Schedules for Battery Energy Storage System by Maximizing Self-Consumption Strategy and Genetic Algorithm

by Yazhou Zhao, Xiangxi Qin and Xiangyu Shi

Sustainability 2022, 14(14), 8821; https://doi.org/10.3390/su14148821 - 19 Jul 2022

Cited by 5 | Viewed by 1519

Abstract

Building an energy storage system is beneficial when solar panels are not producing sufficient energy. However, there is a major issue in terms of feasibility and efficiency. These limitations could be overcome by the deployment of optimal operational strategies. In previous studies, researchers typically focused on finding problem-solving strategies in such situations with only one or two evaluation indicators, lacking a comprehensive evaluation of the integrated objective. Moreover, few studies propose a general model of battery systems suitable for forecast-based operation scenarios with different energy demand features. Therefore, this study developed a comprehensive evaluation model for the operational schedule optimization of a battery energy storage system with a detailed and holistic analysis as well as practicality in implementation. In order to consume the maximum allowable rate of PV generation as promptly and completely as possible, this model was based on a maximizing self-consumption strategy (MSC). A genetic algorithm was applied to time match PV generation and load demand with full consideration of comprehensive techno-economic indicators and total operation cost as well. The model was validated within a typical American house to select the best battery system according to techno-economic indicators for the three types of batteries analyzed. It was discovered that the three types of batteries including Discover AES, Electriq PowerPod2 and Tesla Powerwall+ could all be considered as options for energy storage, and there exist subtle differences in their technical performance during the short charging and discharging phases. Discover AES has the advantage of using PV generation in a timely manner to suit load demand during the long-term operation of a battery energy storage system. With the proper prediction of building energy demand by means of a machine learning approach, the model’s robustness and predictive performance could be further extended. The machine learning approach proved feasible for adapting our optimization model to various battery storage scenarios with different energy demand features. This study is novel in two ways. Firstly, hierarchical optimization was conducted with a genetic algorithm using the MSC strategy. Secondly, the machine learning approach was applied in conjunction with the genetic algorithm to perform online optimization for the predictive schedule. Additionally, three main advantages of the methodology proposed in this paper for producing an optimal operational schedule were identified, which are as follows: generic applicability, convenient implementation and good scalability. However, the charging and discharging performance of the battery energy storage system was simulated under short-term operation with regular solar radiation. Long-term operation considering solar fluctuation should be investigated in the future. Full article

(This article belongs to the Special Issue Optimization Application to Power Systems with the High Penetration Rate of Renewable Energy)

► Show Figures

Figure 1

26 pages, 10410 KiB

Open AccessArticle

Optimal Design of an Isolated Hybrid Microgrid for Enhanced Deployment of Renewable Energy Sources in Saudi Arabia

by Mohammed Kharrich, Salah Kamel, Ali S. Alghamdi, Ahmad Eid, Mohamed I. Mosaad, Mohammed Akherraz and Mamdouh Abdel-Akher

Sustainability 2021, 13(9), 4708; https://doi.org/10.3390/su13094708 - 22 Apr 2021

Cited by 43 | Viewed by 3723

Abstract

Hybrid microgrids are presented as a solution to many electrical energetic problems. These microgrids contain some renewable energy sources such as photovoltaic (PV), wind and biomass, or a hybrid of these sources, in addition to storage systems. Using these microgrids in electric power generation has many advantages such as clean energy, stability in supplying power, reduced grid congestion and a new investment field. Despite all these microgrids advantages, they are not widely used due to some economic aspects. These aspects are represented in the net present cost (NPC) and the levelized cost of energy (LCOE). To handle these economic aspects, the proper microgrids configuration according to the quantity, quality and availability of the sustainable source of energy in installing the microgrid as well as the optimal design of the microgrid components should be investigated. The objective of this paper is to design an economic microgrid system for the Yanbu region of Saudi Arabia. This design aims to select the best microgrid configuration while minimizing both NPC and LCOE considering some technical conditions, including loss of power supply probability and availability index. The optimization algorithm used is Giza Pyramids Construction (GPC). To prove the GPC algorithm’s effectiveness in solving the studied optimization problem, artificial electric field and grey wolf optimizer algorithms are used for comparison purposes. The obtained results demonstrate that the best configuration for the selected area is a PV/biomass hybrid microgrid with a minimum NPC and LCOE of $319,219 and $0.208/kWh, respectively. Full article

(This article belongs to the Special Issue Optimization Application to Power Systems with the High Penetration Rate of Renewable Energy)

► Show Figures

Journal Menu

Journal Browser

Optimization Application to Power Systems with the High Penetration Rate of Renewable Energy

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (2 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI