Artificial intelligence to support the integration of variable renewable energy sources to the power system
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[1] Ciwei Gao,et al. Internet data centers participating in demand response: A comprehensive review , 2020 .
[2] Audun Botterud,et al. The value of energy storage in decarbonizing the electricity sector , 2016 .
[3] Madhur Behl,et al. Sometimes, Money Does Grow on Trees: DR- Advisor, A Data Driven Demand Response Recommender System , 2016 .
[4] R. Wiser,et al. Changes in the Economic Value of Photovoltaic Generation at High Penetration Levels: A Pilot Case Study of California , 2013, IEEE Journal of Photovoltaics.
[5] Thomas Palmé,et al. Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant , 2010 .
[6] BalakrishnanHari,et al. Cutting the electric bill for internet-scale systems , 2009, SIGCOMM '09.
[7] Hao Wang,et al. Proactive Demand Response for Data Centers: A Win-Win Solution , 2015, IEEE Transactions on Smart Grid.
[8] Robert Jenssen,et al. Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning , 2018, International Journal of Electrical Power & Energy Systems.
[9] Di Shi,et al. Wide-Area Measurement System-Based Low Frequency Oscillation Damping Control Through Reinforcement Learning , 2020, IEEE Transactions on Smart Grid.
[10] Jie Zhang,et al. Machine learning based multi-physical-model blending for enhancing renewable energy forecast - improvement via situation dependent error correction , 2015, 2015 European Control Conference (ECC).
[11] Sarvapali D. Ramchurn,et al. Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey , 2015, IEEE Transactions on Intelligent Transportation Systems.
[12] Zhang Yan,et al. A review on the forecasting of wind speed and generated power , 2009 .
[13] Fredrik Wallin,et al. Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting , 2012 .
[14] Enda Barrett,et al. Deep reinforcement learning for home energy management system control , 2021 .
[15] Yuan-Kang Wu,et al. Optimization of the Wind Turbine Layout and Transmission System Planning for a Large-Scale Offshore WindFarm by AI Technology , 2014 .
[16] Mohammed Y. Hassan,et al. The role of intelligent generation control algorithms in optimizing battery energy storage systems size in microgrids: A case study from Western Australia , 2019, Energy Conversion and Management.
[17] Wanderley Cardoso Celeste,et al. An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes , 2021 .
[18] Federico Milano,et al. Demand response algorithms for smart-grid ready residential buildings using machine learning models , 2019, Applied Energy.
[19] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[20] Jie Shi,et al. Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration , 2020, IEEE Transactions on Smart Grid.
[21] G. Luderer,et al. System LCOE: What are the Costs of Variable Renewables? , 2013 .
[22] Jin Zhao,et al. Predicting the state of charge and health of batteries using data-driven machine learning , 2020, Nature Machine Intelligence.
[23] Pio Lombardi,et al. Accentuating the renewable energy exploitation: Evaluation of flexibility options , 2018, International Journal of Electrical Power & Energy Systems.
[24] Jiacheng Ni,et al. A review of air conditioning energy performance in data centers , 2017 .
[25] N. Rahim,et al. Solar photovoltaic generation forecasting methods: A review , 2018 .
[26] Lion Hirth,et al. Balancing power and variable renewables: Three links , 2015 .
[27] Qie Sun,et al. The future potential for Carbon Capture and Storage in climate change mitigation – an overview from perspectives of technology, economy and risk , 2015 .
[28] Adam Wierman,et al. Data center demand response: Avoiding the coincident peak via workload shifting and local generation , 2013, Perform. Evaluation.
[29] Pierre Pinson,et al. Wind Energy: Forecasting Challenges for Its Operational Management , 2013, 1312.6471.
[30] Samveg Saxena,et al. Clean vehicles as an enabler for a clean electricity grid , 2018 .
[31] Sarvapali D. Ramchurn,et al. Agent-based control for decentralised demand side management in the smart grid , 2011, AAMAS.
[32] P. Joskow. Comparing the Costs of Intermittent and Dispatchable Electricity Generating Technologies , 2011 .
[33] Hamid Reza Shaker,et al. A probabilistic sequence classification approach for early fault prediction in distribution grids using long short-term memory neural networks , 2020 .
[34] Christian Gagné,et al. Demand-Side Management Using Deep Learning for Smart Charging of Electric Vehicles , 2019, IEEE Transactions on Smart Grid.
[35] Markus Kraft,et al. Incorporating seller/buyer reputation-based system in blockchain-enabled emission trading application , 2018 .
[36] Samuel J. Cooper,et al. Battery Safety: Data-Driven Prediction of Failure , 2019, Joule.
[37] K.R.M. Vijaya Chandrakala,et al. New interactive agent based reinforcement learning approach towards smart generator bidding in electricity market with micro grid integration , 2020, Appl. Soft Comput..
[38] Iain Staffell,et al. A systems approach to quantifying the value of power generation and energy storage technologies in future electricity networks , 2017, Comput. Chem. Eng..
[39] Byung-Seo Kim,et al. Towards Real-Time Energy Management of Multi-Microgrid Using a Deep Convolution Neural Network and Cooperative Game Approach , 2020, IEEE Access.
[40] Bruce M. Maggs,et al. Cutting the electric bill for internet-scale systems , 2009, SIGCOMM.
[41] S. Iniyan,et al. Applications of fuzzy logic in renewable energy systems – A review , 2015 .
[42] Consolación Gil,et al. Optimization methods applied to renewable and sustainable energy: A review , 2011 .
[43] Enrique Onieva,et al. Real-time predictive maintenance for wind turbines using Big Data frameworks , 2017, 2017 IEEE International Conference on Prognostics and Health Management (ICPHM).
[44] E. A. Jasmin,et al. Residential Load Scheduling With Renewable Generation in the Smart Grid: A Reinforcement Learning Approach , 2019, IEEE Systems Journal.
[45] M. Strubegger,et al. The role of electricity storage and hydrogen technologies in enabling global low-carbon energy transitions , 2018 .
[46] G. Colantuono,et al. RED WoLF: Combining a battery and thermal energy reservoirs as a hybrid storage system , 2020, Applied Energy.
[47] Christoph H. Glock,et al. Energy management for stationary electric energy storage systems: A systematic literature review , 2018, Eur. J. Oper. Res..
[48] Ken Weng Kow,et al. A review on performance of artificial intelligence and conventional method in mitigating PV grid-tied related power quality events , 2016 .
[49] Wei Lee Woon,et al. Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey , 2014, DARE.
[50] T. Faunce,et al. On-grid batteries for large-scale energy storage: Challenges and opportunities for policy and technology , 2018 .
[51] Katie McConky,et al. A hybrid machine learning model for forecasting a billing period’s peak electric load days , 2019, International Journal of Forecasting.
[52] Abbas Khosravi,et al. A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings , 2015 .
[53] Salman Mohagheghi,et al. Demand Response Architecture: Integration into the Distribution Management System , 2010, 2010 First IEEE International Conference on Smart Grid Communications.
[54] Ted Scully,et al. Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine , 2019, Applied Energy.
[55] Hamed Mohsenian-Rad,et al. Power systems big data analytics: An assessment of paradigm shift barriers and prospects , 2018, Energy Reports.
[56] O. Edenhofer,et al. Integration costs revisited – An economic framework for wind and solar variability ☆ , 2015 .
[57] Pierluigi Siano,et al. Demand response and smart grids—A survey , 2014 .
[58] J. Min,et al. Machine learning for accelerating the discovery of high-performance donor/acceptor pairs in non-fullerene organic solar cells , 2020, npj Computational Materials.
[59] D. Moher,et al. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement , 2009, BMJ : British Medical Journal.
[60] Shanlin Yang,et al. Big data driven smart energy management: From big data to big insights , 2016 .
[61] Bri-Mathias Hodge,et al. Cost-Causation and Integration Cost Analysis for Variable Generation , 2011 .
[62] Utkarsh Singh,et al. A new optimal feature selection scheme for classification of power quality disturbances based on ant colony framework , 2019, Appl. Soft Comput..
[63] Varun Rai,et al. Agent-based modelling of consumer energy choices , 2016 .
[64] Jianchun Peng,et al. A review of deep learning for renewable energy forecasting , 2019, Energy Conversion and Management.
[65] Jim Gao,et al. Machine Learning Applications for Data Center Optimization , 2014 .
[66] Iain Staffell,et al. Short-term integration costs of variable renewable energy: Wind curtailment and balancing in Britain and Germany , 2018 .
[67] Jaime Lloret,et al. A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings , 2014, IEEE Communications Surveys & Tutorials.
[68] David Seidl,et al. A holistic approach to power quality parameter optimization in AC coupling Off-Grid systems , 2017 .
[69] Faruk Kazi,et al. Support-Vector-Machine-Based Proactive Cascade Prediction in Smart Grid Using Probabilistic Framework , 2015, IEEE Transactions on Industrial Electronics.
[70] Brian C. Lovell,et al. Deep Inspection: An Electrical Distribution Pole Parts Study VIA Deep Neural Networks , 2019, 2019 IEEE International Conference on Image Processing (ICIP).
[71] Zoran Obradovic,et al. Big data analytics for future electricity grids , 2020, Electric Power Systems Research.
[72] Adam Hawkes,et al. The future cost of electrical energy storage based on experience rates , 2017, Nature Energy.
[73] Eklas Hossain,et al. Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review , 2019, IEEE Access.
[74] Haimonti Dutta,et al. Machine Learning for the New York City Power Grid , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[75] Soteris A. Kalogirou,et al. Machine learning methods for solar radiation forecasting: A review , 2017 .
[76] B. Hodge,et al. The value of day-ahead solar power forecasting improvement , 2016 .
[77] Paul J. Werbos,et al. Computational Intelligence for the Smart Grid-History, Challenges, and Opportunities , 2011, IEEE Computational Intelligence Magazine.
[78] Seung Ho Hong,et al. A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach , 2018, Applied Energy.
[79] Seung Ho Hong,et al. Incentive-based demand response for smart grid with reinforcement learning and deep neural network , 2019, Applied Energy.
[80] David Flynn,et al. Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review , 2020, Renewable and Sustainable Energy Reviews.
[81] Mahmoud Pesaran,et al. A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances , 2015 .
[82] Seung Wan Kim,et al. Online reconfiguration scheme of self-sufficient distribution network based on a reinforcement learning approach , 2020 .
[83] Iain Staffell,et al. Impact of myopic decision-making and disruptive events in power systems planning , 2018 .
[84] Josep M. Guerrero,et al. Microgrid Energy Management System With Embedded Deep Learning Forecaster and Combined Optimizer , 2020, IEEE Access.
[85] Pierre Pinson,et al. The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation , 2015 .
[86] Guoqiang Zhang,et al. Artificial neural network based multivariable optimization of a hybrid system integrated with phase change materials, active cooling and hybrid ventilations , 2019, Energy Conversion and Management.
[87] Valeriy Vyatkin,et al. An Artificial Intelligence Framework for Bidding Optimization with Uncertainty in Multiple Frequency Reserve Markets , 2020, Applied Energy.
[88] Guzmán Díaz,et al. Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression , 2019, Applied Energy.
[89] Ning Lu,et al. A Supervised Machine Learning Approach to Control Energy Storage Devices , 2019, IEEE Transactions on Smart Grid.
[90] Hari Om Bansal,et al. Real‐time implementation of adaptive PV‐integrated SAPF to enhance power quality , 2019, International Transactions on Electrical Energy Systems.
[91] Zita Vale,et al. Multi-agent simulation of competitive electricity markets: Autonomous systems cooperation for European market modeling , 2015 .
[92] Yonggang Wen,et al. Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning , 2017, IEEE Transactions on Cybernetics.
[93] Dong-Ki Kang,et al. Real-Time Control for Power Cost Efficient Deep Learning Processing With Renewable Generation , 2019, IEEE Access.
[94] Bri-Mathias Hodge,et al. Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting , 2015 .
[95] Kristen A. Severson,et al. Data-driven prediction of battery cycle life before capacity degradation , 2019, Nature Energy.
[96] Goran Strbac,et al. Demand side management: Benefits and challenges ☆ , 2008 .
[97] Sarah C. Darby,et al. Smart electric storage heating and potential for residential demand response , 2017 .
[98] Chan-Hyun Youn,et al. Deep Learning-Based Sustainable Data Center Energy Cost Minimization With Temporal MACRO/MICRO Scale Management , 2019, IEEE Access.
[99] A. N. Andersen,et al. Drivers of imbalance cost of wind power: A comparative analysis , 2010, 2010 7th International Conference on the European Energy Market.
[100] R. V. D. Veen,et al. The electricity balancing market: Exploring the design challenge , 2016 .
[101] Han Zhang,et al. Renewable energy: Present research and future scope of Artificial Intelligence , 2017 .
[102] Soteris A. Kalogirou,et al. Artificial neural networks in renewable energy systems applications: a review , 2001 .
[103] Yuekuan Zhou,et al. Machine-learning based hybrid demand-side controller for high-rise office buildings with high energy flexibilities , 2020 .
[104] T. Oreszczyn,et al. Champion the energy data revolution , 2019, Nature Energy.
[105] Kanendra Naidu,et al. Photovoltaic penetration issues and impacts in distribution network – A review , 2016 .
[106] Stuart E. Madnick,et al. A Systems Theoretic Approach to the Security Threats in Cyber Physical Systems Applied to Stuxnet , 2018, IEEE Transactions on Dependable and Secure Computing.
[107] Shiyu Yang,et al. Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization , 2020 .
[108] Burcin Becerik-Gerber,et al. HVAC system energy optimization using an adaptive hybrid metaheuristic , 2017 .
[109] Yi Wang,et al. Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges , 2018, IEEE Transactions on Smart Grid.
[110] Patrick D. McDaniel,et al. Security and Privacy Challenges in the Smart Grid , 2009, IEEE Security & Privacy.
[111] Sandia Report,et al. Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide A Study for the DOE Energy Storage Systems Program , 2010 .
[112] Alexander Jung,et al. PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING , 2018, 2018 IEEE Data Science Workshop (DSW).
[113] Hasmat Malik,et al. Fuzzy reinforcement learning based intelligent classifier for power transformer faults. , 2020, ISA transactions.
[114] R. Urraca,et al. Review of photovoltaic power forecasting , 2016 .
[115] Yacine Rezgui,et al. ANN–GA smart appliance scheduling for optimised energy management in the domestic sector , 2016 .
[116] Michael Milligan,et al. Integration of Variable Generation, Cost-Causation, and Integration Costs , 2011 .
[117] Joeri Van Mierlo,et al. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review , 2019, Renewable and Sustainable Energy Reviews.
[118] Yingchen Zhang,et al. Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems , 2021, IEEE Transactions on Smart Grid.
[119] Ramachandra Kota,et al. Cooperative Virtual Power Plant Formation Using Scoring Rules , 2012, AAAI.