Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

Journal Browser

Energy Management of Smart Grids with Renewable Energy Resource

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

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (5 December 2023) | Viewed by 11277

Share This Special Issue

Special Issue Editors

Dr. Lixiong Xu

E-Mail Website
Guest Editor

College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: schedule optimization; security analysis and stability control for smart grids with a high penetration of renewable energy

Dr. Shengli Liao

E-Mail Website
Guest Editor

College of Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China
Interests: hydropower system economic operation; multi-energy coordination and optimization

Dr. Jia Liu

E-Mail Website
Guest Editor

Department of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: power systems; integrated energy systems; optimal planning; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy crisis and climate change have prompted countries to make great efforts to develop renewable energy sources. Many WTs and PVs have been integrated into existing grids, bringing dramatic changes and new challenges for the energy management of smart grids. The introduction of significant intermittent generation will affect the way in which smart grids operate. It requires more accurate prediction of renewable energy generation, more flexible operation of power systems, more active response of distributed energy resources, and more extensive utilization of energy storages. The dynamics of power systems will likely be dominated by the dynamics of WTs and PVs in the near future. These challenges require more precise models of WTs and PVs, and more powerful simulations for smart grids. To face the above-mentioned challenges, novel ideas, methods and technologies have emerged. In this Special Issue, we invite original and unpublished submissions on the energy management of smart grids with renewable energy resources.

Topics of interest include, but are not limited to:

Power output prediction for renewable energy sources in different time scales.
Modeling of wind turbines, wind farms, photovoltaic units and plants.
Flexibility assessment and enhancement strategies in renewable-energy-based power grids.
Optimal scheduling and operation of power systems with significant renewable energy integration.
Security and stability assessment of power systems with high-penetration power electronic integration.
Business models, market mechanisms, modelling and scheduling of demand response in power networks.
Economic dispatch of micro-scale energy grids for promoting the utilization of distributed renewable energy.
Planning and operation of energy storage systems (ESSs) in power networks.
Applications of emerging technologies in smart grids with high penetration of renewable energy.

Dr. Lixiong Xu
Dr. Shengli Liao
Dr. Jia Liu
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. Energies 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 2600 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

high penetration
power electronic
energy storage systems
demand response
micro-scale energy grids
economic dispatch
data-driven
multi-time-scale

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

19 pages, 1079 KiB

Open AccessArticle

Energy Management Scheme for Optimizing Multiple Smart Homes Equipped with Electric Vehicles

by Puthisovathat Prum, Prasertsak Charoen, Mohammed Ali Khan, Navid Bayati and Chalie Charoenlarpnopparut

Energies 2024, 17(1), 254; https://doi.org/10.3390/en17010254 - 3 Jan 2024

Cited by 1 | Viewed by 1701

Abstract

The rapid advancement in technology and rise in energy consumption have motivated research addressing Demand-Side Management (DSM). In this research, a novel design for Home Energy Management (HEM) is proposed that seamlessly integrates Battery Energy Storage Systems (BESSs), Photovoltaic (PV) installations, and Electric Vehicles (EVs). Leveraging a Mixed-Integer Linear Programming (MILP) approach, the proposed system aims to minimize electricity costs. The optimization model takes into account Real-Time Pricing (RTP) tariffs, facilitating the efficient scheduling of household appliances and optimizing patterns for BESS charging and discharging, as well as EV charging and discharging. Both individual and multiple Smart Home (SH) case studies showcase noteworthy reductions in electricity costs. In the case of multiple SHs, a remarkable cost reduction of

46.38 %

was achieved compared to a traditional SH scenario lacking integration of a PV, BESS, and EV. Full article

(This article belongs to the Special Issue Energy Management of Smart Grids with Renewable Energy Resource)

► Show Figures

Figure 1

17 pages, 3519 KiB

Open AccessFeature PaperArticle

Day-Ahead Electricity Price Probabilistic Forecasting Based on SHAP Feature Selection and LSTNet Quantile Regression

by Huixin Liu, Xiaodong Shen, Xisheng Tang and Junyong Liu

Energies 2023, 16(13), 5152; https://doi.org/10.3390/en16135152 - 4 Jul 2023

Cited by 3 | Viewed by 1574

Abstract

Electricity prices are a central element of the electricity market, and accurate electricity price forecasting is critical for market participants. However, in the context of increasingly integrated economic markets, the complexity of the electricity system has increased. As a result, the number of factors required to consider in electricity price forecasting is growing. In addition, the high percentage of renewable energy penetration has increased the volatility of electricity generation, making it more challenging to predict prices accurately. In this paper, we propose a probabilistic forecasting method based on SHAP (SHapley Additive exPlanation) feature selection and LSTNet (long- and short-term time-series network) quantile regression. First, to reduce feature redundancy and overfitting, we use the SHAP method to perform feature selection in a high-dimensional input feature set, and specifically analyze the magnitude and manner in which features affect electricity prices. Second, we apply the LSTNet quantile regression model to predict the electricity value under different quantiles. Finally, the probability density function and the prediction interval of the predicted electricity prices are obtained by kernel density estimation. The case of the Danish electricity market validates the effectiveness and accuracy of our proposed method. The accuracy of the proposed method is better than that of other methods, and we assess the importance and direction of the impact of features on electricity prices. Full article

(This article belongs to the Special Issue Energy Management of Smart Grids with Renewable Energy Resource)

► Show Figures

Figure 1

25 pages, 5338 KiB

Open AccessArticle

Emergency Dispatch Approach for Power Systems with Hybrid Energy Considering Thermal Power Unit Ramping

by Buxiang Zhou, Jiale Wu, Tianlei Zang, Yating Cai, Binjie Sun and Yiwei Qiu

Energies 2023, 16(10), 4213; https://doi.org/10.3390/en16104213 - 19 May 2023

Cited by 2 | Viewed by 1275

Abstract

Future power systems will face more extreme operating condition scenarios, and system emergency dispatch will face more severe challenges. The use of distributed control is a well-designed way to handle this. It enables multi-energy complementation by means of autonomous communication, which greatly improves the flexibility of the grid. First, in the context of global energy conservation and emission reduction, this paper adopts the energy usage method of “renewable energy is the main source of energy, supplemented by thermal power and energy storage” to reduce the system abandoned wind (light) rate while supplementing the energy storage capacity. Second, a consensus algorithm is added to the system while considering the coordination between thermal units and energy storage. An “interface” for autonomous communication between thermal units and energy storage is created using the incremental cost of each agent. To address the recurring issue of power imbalance during emergency dispatch of the system, the consensus algorithm is enhanced so that the communication interval varies with the unit rate. This is based on the climbing characteristics of each thermal power unit. Finally, the effectiveness of the proposed method is verified in an IEEE-30 bus system. Full article

(This article belongs to the Special Issue Energy Management of Smart Grids with Renewable Energy Resource)

► Show Figures

Figure 1

25 pages, 4690 KiB

Open AccessArticle

Energy Management in a Standalone Microgrid: A Split-Horizon Dual-Stage Dispatch Strategy

by Aslam Amir, Hussain Shareef and Falah Awwad

Energies 2023, 16(8), 3400; https://doi.org/10.3390/en16083400 - 12 Apr 2023

Cited by 2 | Viewed by 1796

Abstract

Microgrid technology has recently gained global attention over increasing demands for the inclusion of renewable energy resources in power grids, requiring constant research and development in aspects such as control, protection, reliability, and management. With an ever-increasing scope for maximizing renewable energy output, there is also a need to reduce the curtailment of power on both the generation and demand sides by increasing forecasting accuracies and using resources more effectively. This paper proposes a dual-stage dispatch employing a novel “split-horizon” strategy, in a bid to enhance energy management in a standalone microgrid. The split-horizon is essentially the considered time horizon split into equal operational periods of the dual-stage dispatch. The proposed strategy utilizes a custom-designed novel variant of the inertia-weight-based particle swarm optimization (PSO), termed customized PSO, to perform the optimal schedule and dispatch operation by benefitting from the simplicity of PSO and customization as per the considered objectives. A modified IEEE 34-node test system is derived into a standalone microgrid with added distributed energy resources to test the proposed strategy, while another standalone microgrid, a modified IEEE 69-node test feeder, is also considered for scalability. Furthermore, the validation of the strategy is performed appropriately with a case study while also validating the proposed optimization algorithm. It is observed that the proposed energy management strategy provides approximatelya 7% reduction in costs. Full article

(This article belongs to the Special Issue Energy Management of Smart Grids with Renewable Energy Resource)

► Show Figures

Figure 1

11 pages, 1572 KiB

Open AccessArticle

Optimal Transmission Switching for Short-Circuit Current Limitation Based on Deep Reinforcement Learning

by Sirui Tang, Ting Li, Youbo Liu, Yunche Su, Yunling Wang, Fang Liu and Shuyu Gao

Energies 2022, 15(23), 9200; https://doi.org/10.3390/en15239200 - 5 Dec 2022

Cited by 1 | Viewed by 1175

Abstract

The gradual expansion of power transmission networks leads to an increase in short-circuit current (SCC), which has an impact on the secure operation of transmission networks when the SCC exceeds the interrupting capacity of the circuit breakers. In this regard, optimal transmission switching (OTS) is proposed to reduce the short-circuit current while maximizing the loadability with respect to voltage stability. However, the OTS model is a complex combinatorial optimization problem with binary decision variables. To address this problem, this paper employs the deep Q-network (DQN)-based RL algorithm to solve the OTS problem. Case studies on the IEEE 30-bus system and 118-bus system are presented to demonstrate the effectiveness of the proposed method. The numerical results show that the DQN-based agent can select the effective branches at each step and reduce the SCC after implementing the OTS strategies. Full article

(This article belongs to the Special Issue Energy Management of Smart Grids with Renewable Energy Resource)

► Show Figures

Figure 1

21 pages, 3242 KiB

Open AccessArticle

Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis

by Shengli Liao, Xudong Tian, Benxi Liu, Tian Liu, Huaying Su and Binbin Zhou

Energies 2022, 15(17), 6287; https://doi.org/10.3390/en15176287 - 29 Aug 2022

Cited by 8 | Viewed by 1685

Abstract

With the expansion of wind power grid integration, the challenges of sharp fluctuations and high uncertainty in preparing the power grid day-ahead plan and short-term dispatching are magnified. These challenges can be overcome through accurate short-term wind power process prediction based on mining historical operation data and taking full advantage of meteorological forecast information. In this paper, adopting the ERA5 reanalysis dataset as input, a short-term wind power prediction framework is proposed, combining light gradient boosting machine (LightGBM), mutual information coefficient (MIC) and nonparametric regression. Primarily, the reanalysis data of ERA5 provide more meteorological information for the framework, which can help improve the model input features. Furthermore, MIC can identify effective feature subsets from massive feature sets that significantly affect the output, enabling concise understanding of the output. Moreover, LightGBM is a prediction method with a stronger ability of goodness-of-fit, which can fully mine the effective information of wind power historical operation data to improve the prediction accuracy. Eventually, nonparametric regression expands the process prediction to interval prediction, which significantly improves the utility of the prediction results. To quantitatively analyze the prediction results, five evaluation criteria are used, namely, the Pearson correlation coefficient (CORR), the root mean square error (RMSE), the mean absolute error (MAE), the index of agreement (IA) and Kling–Gupta efficiency (KGE). Compared with support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost) models, the present framework can make full use of meteorological information and effectively improve the prediction accuracy, and the generated output prediction interval can also be used to promote the safe operation of power systems. Full article

(This article belongs to the Special Issue Energy Management of Smart Grids with Renewable Energy Resource)

► Show Figures

Figure 1

15 pages, 6998 KiB

Open AccessArticle

Multi-Level Dependent-Chance Model for Hydropower Reservoir Operations

by Xinyu Wu, Xilong Cheng, Meng Zhao, Chuntian Cheng and Qilin Ying

Energies 2022, 15(13), 4899; https://doi.org/10.3390/en15134899 - 4 Jul 2022

Viewed by 1130

Abstract

Some hydropower reservoirs are operated under different constraint levels. For these reservoirs, a multi-level (ML) dependent-chance (DC) model is established. In the model, only when the higher-level constraints are satisfied are the lower-level constraints or system benefits considered. The multi-level dependent-chance (MLDC) model is specified by two models. One is based on existing reliability-constrained (RC) dynamic programming (DP), in which the soft constraints are addressed using reliability constraints of 1, and the priorities are reflected using the order of magnitudes of Lagrange multipliers. The other is the explicit dependent-chance reasoning in the DP recursive function, in which each soft constraint is represented as an objective function of negative expected failure time and the optimum is the solution with a larger value for all higher-level objective functions. The proposed models are applied to derive long-term operation rules for the hydropower system on the middle-lower Lancang River. The results show the feasibility and performances of the explicit graded constraint control of the proposed model and the solution methods. Full article

(This article belongs to the Special Issue Energy Management of Smart Grids with Renewable Energy Resource)

► Show Figures

Journal Menu

Journal Browser

Energy Management of Smart Grids with Renewable Energy Resource

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI