Article
Economics
Georgios I. Maniatis, Nikolaos T. Milonas
Summary: This study investigates the impact of wind and solar power generation on wholesale electricity prices in the Greek market. The empirical findings suggest the existence of a merit-order effect, with wind power having a stronger effect. Controlling for regulatory mechanisms, it is found that renewable energy reduces price volatility overall, but wind power tends to increase it while solar power tends to decrease it. Furthermore, during peak hours, both wind and solar power generation reduce price volatility, supporting the hypothesis that renewables' output reduces wholesale electricity price volatility when positively correlated with electricity load. Additionally, an increase in the price-cap of the Greek wholesale electricity market is associated with a reduction in price volatility. This finding emphasizes the importance of market structure and participants' vertical integration in determining behavior and market price volatility.
Article
Computer Science, Information Systems
Donghun Lee, Kwanho Kim, Sang Hwa Song
Summary: Power generation companies in Korea aim to maximize operational profit by determining optimal capacity bidding strategy. This study proposes a machine learning methodology for predicting Pricing-setting Scheduled Energy, utilizing seasonal and price information. The experimentation shows that machine learning algorithms with price variable are more effective, with boosting approach outperforming single and bagging approaches.
Article
Thermodynamics
Jingyi Shang, Jinfeng Gao, Xin Jiang, Mingguang Liu, Dunnan Liu
Summary: This paper proposes a two-stage multi-objective bi-level framework to optimize the sizing of a grid-connected electricity-hydrogen system. It establishes a multi-objective bi-level capacity configuration optimization model considering the different functional orientations of hydrogen energy and electricity-price prediction. A two-stage solution algorithm is then proposed to solve the multi-objective bi-level model.
Article
Automation & Control Systems
Lina Ren, Mingming Yuan, Xiaohong Jiao
Summary: This paper designs a reinforcement learning framework of LSTM-ILP to control the V2G of EVs, which comprehensively considers the overall EV charging demand, discharge potential, large grid electricity price, aggregator, and users' interests demands. By establishing a dynamic electricity price based on LSTM and using an improved linear programming algorithm to solve the EV charging and discharging optimization problem, optimal electricity price and EV charging and discharging schedule can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Green & Sustainable Science & Technology
Iraj Moradpoor, Sanna Syri, Annukka Santasalo-Aarnio
Summary: This study investigates the production of green hydrogen for use in oil refining according to the draft of European union delegated act published in May 2022. The European union plans to set strict requirements for renewable electricity used in hydrogen production. Different types of electrolyzers are evaluated in various scenarios supplied by wind power. Power purchase agreement-based scenarios and wind power investment-based scenarios are assessed. The results show that alkaline electrolyzer with baseload power purchase agreement can achieve the cheapest green hydrogen production.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Energy & Fuels
Emil Hosius, Johann Seebass, Benjamin Wacker, Jan Chr. Schlueter
Summary: The aim of this paper is to estimate the effect of offshore wind energy on wholesale electricity prices and compare it with the impact of onshore wind. Different time series models were used to analyze the data from Germany, Western Denmark, and Great Britain. The results show that onshore and offshore wind power have significantly different impacts on wholesale electricity prices in these countries.
Article
Energy & Fuels
Yangrui Zhang, Peng Tao, Xiangming Wu, Chenguang Yang, Guang Han, Hui Zhou, Yinlong Hu
Summary: In an open electricity market, accurate electricity price forecasting is crucial for effective participation of market players. This paper proposes a model for forecasting electricity prices in markets with high proportions of wind and solar power and introduces an optimized algorithm for predictions. The results highlight the importance of wind-load ratio and solar-load ratio as key input variables for forecasting in these markets.
Article
Energy & Fuels
Meisam Mahdavi, Francisco Jurado, Ricardo Alan Verdu Ramos, Augustine Awaafo
Summary: The high dependence on imported fossil fuels and the need to reduce greenhouse gas emissions have driven Morocco to focus on renewable energy sources such as hydro, wind, and solar. However, in recent years, wind and solar energy usage has outpaced hydropower due to water level reductions caused by rainfall drop, global warming, and droughts. Morocco has great potential for solar and wind energy generation due to its sunny days, vast desert areas, long coastlines, and suitable wind conditions. However, integrating wind and solar units into the power grid has challenges due to variations in wind speed, solar irradiation uncertainty, and expensive energy storage systems. Biomass power generation can compensate for the decrease in wind and solar energy during cloudy or non-windy periods. Therefore, this study evaluates the hybrid utilization of biomass, wind, and solar energy in a Moroccan village and rural area, finding it to be a more appropriate approach compared to individual operation of wind turbines or other combinations of renewable energy sources.
Article
Green & Sustainable Science & Technology
Lazaro Endemano-Ventura, Javier Serrano Gonzalez, Juan Manuel Roldan Fernandez, Manuel Burgos Payan, Jesus Manuel Riquelme Santos
Summary: The paper presents a method to calculate the optimal bidding strategy for a wind power plant and validates it using real data and market conditions. The optimal bidding strategy mainly depends on the system deviation, showing more advantages in practical applications.
Article
Computer Science, Interdisciplinary Applications
Jian Liu, Rui Bo, Siyuan Wang, Haotian Chen
Summary: This paper analyzes how the market impact of large-scale energy storage merchants affects their profits, using dynamic programming theory to study optimal economic dispatch decisions accounting for market impact and storage system physical characteristics. Findings show that State-of-Charge based analytical solutions facilitate merchant decision-making and highlight the need for merchants to balance market impact intensity and dispatched power to maximize profit. Numerical simulations confirm the importance of considering market impact in energy arbitrage decisions for electricity merchants.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Chemical
Saniye Maihemuti, Weiqing Wang, Jiahui Wu, Haiyun Wang, Muladi Muhedaner, Qing Zhu
Summary: This paper proposes a hybrid adaptive velocity update relaxation particle swarm optimization algorithm (AVURPSO) and recursive least square (RLS) method to quickly estimate the DSSR boundary using hyper-plane expression. The AVURPSO algorithm is used to analyze the operating point data and identify key generators and critical points, while the RLS approach is used to fit the hyper-plane expression of the DSSR boundary. The proposed algorithm is validated using a simulation case study, demonstrating its effectiveness in capturing the security stability boundary of the new energy power system.
Article
Green & Sustainable Science & Technology
Noman Khan, Samee Ullah Khan, Sung Wook Baik
Summary: In order to address the issues with traditional machine learning approaches in energy forecasting, we propose a hybrid network model based on deep dilated depthwise separable convolutional neural network and bidirectional gated recurrent unit. By preprocessing the data, extracting spatial features, and learning temporal patterns, our model achieves high accuracy on multiple real-world datasets.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Review
Energy & Fuels
Hakan Acaroglu, Fausto Pedro Garcia Marquez
Summary: Forecasting electricity market prices and loads has been a critical area of research, with a focus on wind energy techniques. The complexity of forecasting is influenced by multiple variables and methodologies, impacting market operations and decision makers.
Article
Energy & Fuels
Vahid Arabzadeh, Panu Miettinen, Titta Kotilainen, Pasi Herranen, Alp Karakoc, Matti Kummu, Lauri Rautkari
Summary: Producing food sustainably for the growing population is a challenge to the global food system. Vertical farms are gaining interest as they use less water, pesticides, and land; however, their high energy demand and separation from cities pose challenges. This study evaluates the potential of demand response in reducing electricity consumption costs for vertical farms and analyzes the integration of vertical farms with urban energy systems.
Article
Computer Science, Information Systems
Geetha Anbazhagan, Daegeon Kim, M. Maragatharajan
Summary: Driving patterns require both average and momentary power demands, which can be met by batteries and ultracapacitors respectively. Smart energy management and IoT-based decision-making modules help optimize energy utilization and hybridization of energy storage systems. Experimental findings highlight the significance of intelligent energy management control for the overall performance of hybrid ESSs.
IEEE INTERNET OF THINGS JOURNAL
(2023)