IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v198y2022icp440-454.html
   My bibliography  Save this article

Combined model predictive control and ANN-based forecasters for jointly acting renewable self-consumers: An environmental and economical evaluation

Author

Listed:
  • Negri, Simone
  • Giani, Federico
  • Blasuttigh, Nicola
  • Massi Pavan, Alessandro
  • Mellit, Adel
  • Tironi, Enrico

Abstract

Recent European Community directives introduce Renewable Energy Communities (REC) and Jointly Acting Renewable Self-Consumers (JARSC). Both entities are constituted by communities of residential and/or non-residential prosumers, located in proximity of renewable generators and Electrical Storage Systems (ESS) owned and managed by the REC/JARSCs. These aggregations of prosumers are aimed at providing environmental and economic benefits by maximizing their global self-consumption. In this frame, it is relevant to introduce a control strategy which considers the whole system represented by the REC/JARSCs and performs optimal management of energy production, storage and consumption. The present paper proposes a Model Predictive Control (MPC) based control design, targeted at the minimization of electricity cost and equivalent CO2 emissions, considering the whole ensemble of loads included in the REC/JARSCs over a 24-h prediction horizon. To exploit the MPC ability of including forecasts in the optimization problem, predictors including Artificial Neural Networks (ANN) are developed for solar irradiance, air temperature, electricity price and carbon intensity. The proposed control performance is evaluated considering a case study located in Milan, Italy, and its advantages with respect to traditional control algorithms are highlighted by comprehensive numerical simulations. Lastly, an economic evaluation of the considered system is presented.

Suggested Citation

  • Negri, Simone & Giani, Federico & Blasuttigh, Nicola & Massi Pavan, Alessandro & Mellit, Adel & Tironi, Enrico, 2022. "Combined model predictive control and ANN-based forecasters for jointly acting renewable self-consumers: An environmental and economical evaluation," Renewable Energy, Elsevier, vol. 198(C), pages 440-454.
    • Handle: RePEc:eee:renene:v:198:y:2022:i:c:p:440-454
    DOI: 10.1016/j.renene.2022.07.065
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148122010606
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2022.07.065?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bartolini, Andrea & Carducci, Francesco & Muñoz, Carlos Boigues & Comodi, Gabriele, 2020. "Energy storage and multi energy systems in local energy communities with high renewable energy penetration," Renewable Energy, Elsevier, vol. 159(C), pages 595-609.
    2. Rodríguez, Fermín & Fleetwood, Alice & Galarza, Ainhoa & Fontán, Luis, 2018. "Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control," Renewable Energy, Elsevier, vol. 126(C), pages 855-864.
    3. Bokde, Neeraj Dhanraj & Tranberg, Bo & Andresen, Gorm Bruun, 2021. "Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling," Applied Energy, Elsevier, vol. 281(C).
    4. Luz, G. Pontes & Brito, M.C. & Sousa, J.M.C. & Vieira, S.M., 2021. "Coordinating shiftable loads for collective photovoltaic self-consumption: A multi-agent approach," Energy, Elsevier, vol. 229(C).
    5. Di Lorenzo, Gianfranco & Rotondo, Sara & Araneo, Rodolfo & Petrone, Giovanni & Martirano, Luigi, 2021. "Innovative power-sharing model for buildings and energy communities," Renewable Energy, Elsevier, vol. 172(C), pages 1087-1102.
    6. Herenčić, Lin & Kirac, Mislav & Keko, Hrvoje & Kuzle, Igor & Rajšl, Ivan, 2022. "Automated energy sharing in MV and LV distribution grids within an energy community: A case for Croatian city of Križevci with a hybrid renewable system," Renewable Energy, Elsevier, vol. 191(C), pages 176-194.
    7. Giuseppe Barone & Giovanni Brusco & Daniele Menniti & Anna Pinnarelli & Gaetano Polizzi & Nicola Sorrentino & Pasquale Vizza & Alessandro Burgio, 2020. "How Smart Metering and Smart Charging may Help a Local Energy Community in Collective Self-Consumption in Presence of Electric Vehicles," Energies, MDPI, vol. 13(16), pages 1-18, August.
    8. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
    9. Morato, Marcelo M. & Vergara-Dietrich, José & Esparcia, Eugene A. & Ocon, Joey D. & Normey-Rico, Julio E., 2021. "Assessing demand compliance and reliability in the Philippine off-grid islands with Model Predictive Control microgrid coordination," Renewable Energy, Elsevier, vol. 179(C), pages 1271-1290.
    10. Korkmaz, Deniz, 2021. "SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 300(C).
    11. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Zhang, Jun & Shi, Junsheng & Gao, Bixuan & Liu, Wuming, 2021. "Hybrid deep neural model for hourly solar irradiance forecasting," Renewable Energy, Elsevier, vol. 171(C), pages 1041-1060.
    12. Tschora, Léonard & Pierre, Erwan & Plantevit, Marc & Robardet, Céline, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Applied Energy, Elsevier, vol. 313(C).
    13. Yang, Wendong & Sun, Shaolong & Hao, Yan & Wang, Shouyang, 2022. "A novel machine learning-based electricity price forecasting model based on optimal model selection strategy," Energy, Elsevier, vol. 238(PC).
    14. Mazzeo, Domenico, 2019. "Nocturnal electric vehicle charging interacting with a residential photovoltaic-battery system: a 3E (energy, economic and environmental) analysis," Energy, Elsevier, vol. 168(C), pages 310-331.
    15. Lee, Donghun & Kim, Kwanho, 2021. "PV power prediction in a peak zone using recurrent neural networks in the absence of future meteorological information," Renewable Energy, Elsevier, vol. 173(C), pages 1098-1110.
    16. Hu, Jiefeng & Shan, Yinghao & Guerrero, Josep M. & Ioinovici, Adrian & Chan, Ka Wing & Rodriguez, Jose, 2021. "Model predictive control of microgrids – An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    17. Tovar Rosas, Mario A. & Pérez, Miguel Robles & Martínez Pérez, E. Rafael, 2022. "Itineraries for charging and discharging a BESS using energy predictions based on a CNN-LSTM neural network model in BCS, Mexico," Renewable Energy, Elsevier, vol. 188(C), pages 1141-1165.
    18. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
    19. Sima, Catalina Alexandra & Popescu, Claudia Laurenta & Popescu, Mihai Octavian & Roscia, Mariacristina & Seritan, George & Panait, Cornel, 2022. "Techno-economic assessment of university energy communities with on/off microgrid," Renewable Energy, Elsevier, vol. 193(C), pages 538-553.
    20. Mellit, A. & Pavan, A. Massi & Lughi, V., 2021. "Deep learning neural networks for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 172(C), pages 276-288.
    21. Terlouw, Tom & AlSkaif, Tarek & Bauer, Christian & van Sark, Wilfried, 2019. "Multi-objective optimization of energy arbitrage in community energy storage systems using different battery technologies," Applied Energy, Elsevier, vol. 239(C), pages 356-372.
    22. Manzano, J.M. & Salvador, J.R. & Romaine, J.B. & Alvarado-Barrios, L., 2022. "Economic predictive control for isolated microgrids based on real world demand/renewable energy data and forecast errors," Renewable Energy, Elsevier, vol. 194(C), pages 647-658.
    23. Gairaa, Kacem & Voyant, Cyril & Notton, Gilles & Benkaciali, Saïd & Guermoui, Mawloud, 2022. "Contribution of ordinal variables to short-term global solar irradiation forecasting for sites with low variabilities," Renewable Energy, Elsevier, vol. 183(C), pages 890-902.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hu, Jiaxiang & Hu, Weihao & Cao, Di & Sun, Xinwu & Chen, Jianjun & Huang, Yuehui & Chen, Zhe & Blaabjerg, Frede, 2024. "Probabilistic net load forecasting based on transformer network and Gaussian process-enabled residual modeling learning method," Renewable Energy, Elsevier, vol. 225(C).
    2. Nicola Blasuttigh & Simone Negri & Alessandro Massi Pavan & Enrico Tironi, 2023. "Optimal Sizing and Environ-Economic Analysis of PV-BESS Systems for Jointly Acting Renewable Self-Consumers," Energies, MDPI, vol. 16(3), pages 1-25, January.
    3. Calise, Francesco & Cappiello, Francesco Liberato & Cimmino, Luca & Dentice d’Accadia, Massimo & Vicidomini, Maria, 2023. "Renewable smart energy network: A thermoeconomic comparison between conventional lithium-ion batteries and reversible solid oxide fuel cells," Renewable Energy, Elsevier, vol. 214(C), pages 74-95.
    4. Tanja M. Kneiske, 2023. "Reducing CO 2 Emissions for PV-CHP Hybrid Systems by Using a Hierarchical Control Algorithm," Energies, MDPI, vol. 16(17), pages 1-24, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    2. Sabadus, Andreea & Blaga, Robert & Hategan, Sergiu-Mihai & Calinoiu, Delia & Paulescu, Eugenia & Mares, Oana & Boata, Remus & Stefu, Nicoleta & Paulescu, Marius & Badescu, Viorel, 2024. "A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches," Renewable Energy, Elsevier, vol. 226(C).
    3. Simoiu, Mircea Stefan & Fagarasan, Ioana & Ploix, Stéphane & Calofir, Vasile, 2022. "Modeling the energy community members’ willingness to change their behaviour with multi-agent systems: A stochastic approach," Renewable Energy, Elsevier, vol. 194(C), pages 1233-1246.
    4. Xu, Shaozhen & Liu, Jun & Huang, Xiaoqiao & Li, Chengli & Chen, Zaiqing & Tai, Yonghang, 2024. "Minutely multi-step irradiance forecasting based on all-sky images using LSTM-InformerStack hybrid model with dual feature enhancement," Renewable Energy, Elsevier, vol. 224(C).
    5. Sarmas, Elissaios & Spiliotis, Evangelos & Stamatopoulos, Efstathios & Marinakis, Vangelis & Doukas, Haris, 2023. "Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models," Renewable Energy, Elsevier, vol. 216(C).
    6. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    7. Yang, Mao & Zhao, Meng & Huang, Dawei & Su, Xin, 2022. "A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder," Renewable Energy, Elsevier, vol. 194(C), pages 659-673.
    8. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2022. "Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    9. Shabbir, Noman & Kütt, Lauri & Raja, Hadi A. & Jawad, Muhammad & Allik, Alo & Husev, Oleksandr, 2022. "Techno-economic analysis and energy forecasting study of domestic and commercial photovoltaic system installations in Estonia," Energy, Elsevier, vol. 253(C).
    10. Cao, Yisheng & Liu, Gang & Luo, Donghua & Bavirisetti, Durga Prasad & Xiao, Gang, 2023. "Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM-Informer model," Energy, Elsevier, vol. 283(C).
    11. Liao, Zhouyi & Coimbra, Carlos F.M., 2024. "Hybrid solar irradiance nowcasting and forecasting with the SCOPE method and convolutional neural networks," Renewable Energy, Elsevier, vol. 232(C).
    12. Sima, Catalina Alexandra & Popescu, Claudia Laurenta & Popescu, Mihai Octavian & Roscia, Mariacristina & Seritan, George & Panait, Cornel, 2022. "Techno-economic assessment of university energy communities with on/off microgrid," Renewable Energy, Elsevier, vol. 193(C), pages 538-553.
    13. Khan, Zulfiqar Ahmad & Khan, Shabbir Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2024. "DSPM: Dual sequence prediction model for efficient energy management in micro-grid," Applied Energy, Elsevier, vol. 356(C).
    14. Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
    15. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique," Energies, MDPI, vol. 16(18), pages 1-23, September.
    16. Zhang, Yijie & Ma, Tao & Yang, Hongxing, 2022. "Grid-connected photovoltaic battery systems: A comprehensive review and perspectives," Applied Energy, Elsevier, vol. 328(C).
    17. Liu, Jiarui & Fu, Yuchen, 2023. "Decomposition spectral graph convolutional network based on multi-channel adaptive adjacency matrix for renewable energy prediction," Energy, Elsevier, vol. 284(C).
    18. Bo Gu & Xi Li & Fengliang Xu & Xiaopeng Yang & Fayi Wang & Pengzhan Wang, 2023. "Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
    19. Wang, Yuqing & Fu, Wenjie & Wang, Junlong & Zhen, Zhao & Wang, Fei, 2024. "Ultra-short-term distributed PV power forecasting for virtual power plant considering data-scarce scenarios," Applied Energy, Elsevier, vol. 373(C).
    20. Martins, Guilherme Santos & Giesbrecht, Mateus, 2023. "Hybrid approaches based on Singular Spectrum Analysis and k- Nearest Neighbors for clearness index forecasting," Renewable Energy, Elsevier, vol. 219(P1).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:198:y:2022:i:c:p:440-454. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.