[en] Keywords Greenhouse gas emission; Energy efficiency; Photovoltaics; Battery; Edge computing; Linear programming Electricity produced and used in the residential sector is responsible for approximately 30% of the greenhouse gas emissions (GHGE). Insulating houses and integrating renewable energy and storage resources are key for reducing such emissions. However, it is not only a matter of installing renewable energy technologies but also of optimizing the charging/discharging of the storage units. A number of optimization models have been proposed lately to address this problem. However, they are often limited in several respects: (i) they often focus only on electricity bill reduction, placing GHGE reduction on the backburner; (ii) they rarely propose hybrid-energy storage optimization strategies considering thermal and storage heater units; (iii) they are often designed using Linear Programming (LP) or metaheuristic techniques that are computational intensive, hampering their deployment on edge devices; and (iv) they rarely evaluate how the model impacts on the battery lifespan. Given this state-of-affairs, the present article compares two approaches, the first one proposing an innovative sliding grid carbon intensity threshold algorithm developed as part of a European project named RED WoLF, the second one proposing an algorithm designed based on LP. The comparison analysis is carried out based on two distinct real-life scenarios in France and UK. Results show that both algorithms contribute to reduce GHGE compared to a solution without optimization logic (between 10 to 25%), with a slight advantage for the LP algorithm. However, RED WoLF makes it possible to reduce significantly the computational time ([almost equal to]25 min for LP against [almost equal to]1 ms for RED WoLF) and to extend the battery lifespan (4 years for LP against 12 years for RED WoLF). Author Affiliation: (a) Université de Lorraine, CNRS, CRAN, F-54000, France (b) Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, L-1359 Esch-sur-Alzette, Luxembourg (c) Department of Industrial and Systems Engineering, Rochester Institute of Technology, 81 Lomb Memorial Drive, Rochester, NY 14623, United States (d) School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, LS1 3HE, UK * Corresponding author. Article History: Received 12 March 2022; Revised 3 June 2022; Accepted 8 July 2022 (miscellaneous) Handling Editor: Panos Seferlis Byline: Paul Ortiz [paul.ortiz@univ-lorraine.fr] (a,*), Sylvain Kubler [s.kubler@univ-lorraine.fr] (a,b), Éric Rondeau [eric.rondeau@univ-lorraine.fr] (a), Katie McConky [ktmeie@rit.edu] (c), Alexander Alexandrovich Shukhobodskiy [A.Shukhobodskiy@leedsbeckett.ac.uk] (d), Giuseppe Colantuono [G.Colantuono@leedsbeckett.ac.uk] (d), Jean-Philippe Georges [jean-philippe.georges@univ-lorraine.fr] (a)
Disciplines :
Computer science
Author, co-author :
Ortiz, Paul
Kubler, Sylvain ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal
Rondeau, Éric
McConky, Katie
Shukhobodskiy, Alexander Alexandrovich
Colantuono, Giuseppe
Georges, Jean-Philippe
External co-authors :
yes
Language :
English
Title :
Greenhouse gas emission reduction in residential buildings: A lightweight model to be deployed on edge devices
Adefarati, T., Potgieter, S., Bansal, R., Naidoo, R., Rizzo, R., Sanjeevikumar, P., Optimization of PV-wind-battery storage microgrid system utilizing a genetic algorithm. 2019 International Conference on Clean Electrical Power (ICCEP), 2019, IEEE, 633–638.
Ahmed, A., Ge, T., Peng, J., Yan, W.-C., Tee, B.T., You, S., Assessment of the renewable energy generation towards net-zero energy buildings: A review. Energy Build., 2021, 111755.
Al-Shahri, O.A., Ismail, F.B., Hannan, M.A., Lipu, M.S.H., Al-Shetwi, A.Q., Begum, R.A., Al-Muhsen, N.F.O., Soujeri, E., Solar photovoltaic energy optimization methods, challenges and issues: A comprehensive review. J. Cleaner Prod., 284, 2021, 125465.
Arévalo, P., Benavides, D., Lata-García, J., Jurado, F., Energy control and size optimization of a hybrid system (photovoltaic-hidrokinetic) using various storage technologies. Sustainable Cities Soc., 52, 2020, 101773.
Ashraf, M.A., Liu, Z., Alizadeh, A., Nojavan, S., Jermsittiparsert, K., Zhang, D., Designing an optimized configuration for a hybrid PV/Diesel/Battery energy system based on metaheuristics: A case study on Gobi Desert. J. Cleaner Prod., 270, 2020, 122467.
Auñón-Hidalgo, J.A., Sidrach-de Cardona, M., Auñón Rodríguez, F., Performance and CO2 emissions assessment of a novel combined solar photovoltaic and thermal, with a Stirling engine micro-CHP system for domestic environments. Energy Convers. Manage., 230, 2021, 113793.
Awan, A.B., Performance analysis and optimization of a hybrid renewable energy system for sustainable NEOM city in Saudi Arabia. J. Renew. Sustain. Energy, 11(2), 2019, 025905.
Awan, A.B., Zubair, M., Sidhu, G.A.S., Bhatti, A.R., Abo-Khalil, A.G., Performance analysis of various hybrid renewable energy systems using battery, hydrogen, and pumped hydro-based storage units. Int. J. Energy Res. 43:12 (2019), 6296–6321.
Ayse Fidan, A., Muhsin, K., Design and performance evaluation based on economics and environmental impact of a PV-wind-diesel and battery standalone power system for various climates in Turkey. Renew. Energy 157 (2020), 424–443.
Aziz, A.S., Tajuddin, M.F.N., Adzman, M.R., Azmi, A., Ramli, M.A., Optimization and sensitivity analysis of standalone hybrid energy systems for rural electrification: A case study of Iraq. Renew. Energy 138 (2019), 775–792.
Aziz, A.S., Tajuddin, M.F.N., Adzman, M.R., Ramli, M.A., Mekhilef, S., Energy management and optimization of a PV/diesel/battery hybrid energy system using a combined dispatch strategy. Sustainability, 11(3), 2019, 683.
Baek, S., Kim, S., Potential effects of vacuum insulating glazing application for reducing greenhouse gas emission (GHGE) from apartment buildings in the Korean capital region. Energies, 13(11), 2020, 2828.
Bhayo, B.A., Al-Kayiem, H.H., Gilani, S.I., Ismail, F.B., Power management optimization of hybrid solar photovoltaic-battery integrated with pumped-hydro-storage system for standalone electricity generation. Energy Convers. Manage., 215, 2020, 112942.
Bingham, R.D., Agelin-Chaab, M., Rosen, M.A., Whole building optimization of a residential home with PV and battery storage in The Bahamas. Renew. Energy 132 (2019), 1088–1103.
Borra, V.S., Debnath, K., Comparison between the dynamic programming and particle swarm optimization for solving unit commitment problems. 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), 2019, IEEE, 395–400.
Burmester, D., Rayudu, R., Seah, W., Akinyele, D., A review of nanogrid topologies and technologies. Renew. Sustain. Energy Rev. 67 (2017), 760–775.
Das, R., Wang, Y., Putrus, G., Kotter, R., Marzband, M., Herteleer, B., Warmerdam, J., Multi-objective techno-economic-environmental optimisation of electric vehicle for energy services. Appl. Energy, 257, 2020, 113965.
Fodhil, F., Hamidat, A., Nadjemi, O., Potential, optimization and sensitivity analysis of photovoltaic-diesel-battery hybrid energy system for rural electrification in Algeria. Energy 169 (2019), 613–624.
Fonseca, J.D., Commenge, J.-M., Camargo, M., Falk, L., Gil, I.D., Multi-criteria optimization for the design and operation of distributed energy systems considering sustainability dimensions. Energy, 214, 2021, 118989.
García-Triviño, P., Fernández-Ramírez, L.M., Gil-Mena, A.J., Llorens-Iborra, F., García-Vázquez, C.A., Jurado, F., Optimized operation combining costs, efficiency and lifetime of a hybrid renewable energy system with energy storage by battery and hydrogen in grid-connected applications. Int. J. Hydrogen Energy 41:48 (2016), 23132–23144.
García-Vera, Y.E., Dufo-López, R., Bernal-Agustín, J.L., Optimization of isolated hybrid microgrids with renewable energy based on different battery models and technologies. Energies, 13(3), 2020, 581.
Georgiou, G.S., Christodoulides, P., Kalogirou, S.A., Real-time energy convex optimization, via electrical storage, in buildings–a review. Renew. Energy 139 (2019), 1355–1365.
Georgiou, G.S., Christodoulides, P., Kalogirou, S.A., Optimizing the energy storage schedule of a battery in a PV grid-connected nZEB using linear programming. Energy, 208, 2020, 118177.
Georgiou, G.S., Nikolaidis, P., Kalogirou, S.A., Christodoulides, P., A hybrid optimization approach for autonomy enhancement of nearly-zero-energy buildings based on battery performance and artificial neural networks. Energies, 13(14), 2020.
González-Briones, A., Prieto, J., De La Prieta, F., Herrera-Viedma, E., Corchado, J.M., Energy optimization using a case-based reasoning strategy. Sensors, 18(3), 2018, 865.
Haidar, N., Attia, M., Senouci, S.-M., Aglzim, E.-H., Kribeche, A., Asus, Z.B., New consumer-dependent energy management system to reduce cost and carbon impact in smart buildings. Sustainable Cities Soc. 39 (2018), 740–750.
Hannan, M.A., Wali, S.B., Ker, P.J., Abd Rahman, M.S., Mansor, M., Ramachandaramurthy, V.K., Muttaqi, K.M., Mahlia, T.M.I., Dong, Z.Y., Battery energy-storage system: A review of technologies, optimization objectives, constraints, approaches, and outstanding issues. J. Energy Storage, 42, 2021, 103023.
IHEPCDS, M.A., Individual household electric power consumption data set. 2010 URL https://archive.ics.uci.edu/ml/datasets/.
Karamov, D.N., Suslov, K.V., Structural optimization of autonomous photovoltaic systems with storage battery replacements. Energy Rep. 7 (2021), 349–358.
Lazarus, M., van Asselt, H., Fossil fuel supply and climate policy: exploring the road less taken. Clim. Change 150 (2018), 1–13.
Litjens, G., Worrell, E., van Sark, W., Assessment of forecasting methods on performance of photovoltaic-battery systems. Appl. Energy 221 (2018), 358–373.
Liu, J., Chen, X., Yang, H., Li, Y., Energy storage and management system design optimization for a photovoltaic integrated low-energy building. Energy, 190, 2020, 116424.
Luo, L., Abdulkareem, S.S., Rezvani, A., Miveh, M.R., Samad, S., Aljojo, N., Pazhoohesh, M., Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty. J. Energy Storage, 28, 2020, 101306.
Mahmud, K., Hossain, M.J., Town, G.E., Peak-load reduction by coordinated response of photovoltaics, battery storage, and electric vehicles. IEEE Access 6 (2018), 29353–29365.
Marzband, M., Alavi, H., Ghazimirsaeid, S.S., Uppal, H., Fernando, T., Optimal energy management system based on stochastic approach for a home microgrid with integrated responsive load demand and energy storage. Sustainable Cities Soc. 28 (2017), 256–264.
Marzband, M., Yousefnejad, E., Sumper, A., Domínguez-García, J.L., Real time experimental implementation of optimum energy management system in standalone microgrid by using multi-layer ant colony optimization. Int. J. Electr. Power Energy Syst. 75 (2016), 265–274.
Monacchi, A., Egarter, D., Elmenreich, W., D'Alessandro, S., Tonello, A.M., 2014. Greend: An energy consumption dataset of households in italy and austria. In: IEEE International Conference on Smart Grid Communications, pp. 511–516.
Moradi, H., Abtahi, A., Esfahanian, M., Optimal operation of a multi-source microgrid to achieve cost and emission targets. 2016 IEEE Power and Energy Conference At Illinois (PECI), 2016, IEEE, 1–6.
Mulleriyawage, U., Shen, W., Optimally sizing of battery energy storage capacity by operational optimization of residential PV-battery systems: An Australian household case study. Renew. Energy 160 (2020), 852–864.
Munir, M.S., Abedin, S.F., Tran, N.H., Hong, C.S., When edge computing meets microgrid: A deep reinforcement learning approach. IEEE Internet Things J. 6:5 (2019), 7360–7374.
Murty, V.V.V.S.N., Kumar, A., Optimal energy management and techno-economic analysis in microgrid with hybrid renewable energy sources. J. Mod. Power Syst. Clean Energy 8:5 (2020), 929–940.
Nagapurkar, P., Smith, J.D., Techno-economic optimization and environmental Life Cycle Assessment (LCA) of microgrids located in the US using genetic algorithm. Energy Convers. Manage. 181 (2019), 272–291.
Nain, P., Kumar, A., A state-of-art review on end-of-life solar photovoltaics. J. Cleaner Prod., 2022, 130978.
Nguyen, C.-L., Lee, H.-H., Chun, T.-W., Cost-optimized battery capacity and short-term power dispatch control for wind farm. IEEE Trans. Ind. Appl. 51:1 (2014), 595–606.
Nottrott, A., Kleissl, J., Washom, B., Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems. Renew. Energy 55 (2013), 230–240.
NREL, A., Pvwatts calculator. 2020 URL https://pvwatts.nrel.gov/pvwatts.php.
Olivieri, Z.T., McConky, K., Optimization of residential battery energy storage system scheduling for cost and emissions reductions. Energy Build., 210, 2020, 109787.
Olivieri, Z.T., McConky, K., Optimization of residential battery energy storage system scheduling for cost and emissions reductions. Energy Build., 210, 2020, 109787.
Ortiz, P., Kubler, S., Rondeau, E., Georges, J.-P., Colantuono, G., Shukhobodskiy, A.A., Greenhouse gas emission reduction system in photovoltaic nanogrid with battery and thermal storage reservoirs. J. Cleaner Prod., 310, 2021, 127347.
O'Shaughnessy, E., Cutler, D., Ardani, K., Margolis, R., Solar plus: Optimization of distributed solar PV through battery storage and dispatchable load in residential buildings. Appl. Energy 213 (2018), 11–21.
Pandžić, H., Optimal battery energy storage investment in buildings. Energy Build. 175 (2018), 189–198.
Saleem, Y., Crespi, N., Rehmani, M.H., Copeland, R., Internet of things-aided smart grid: Technologies, architectures, applications, prototypes, and future research directions. IEEE Access 7 (2019), 62962–63003, 10.1109/ACCESS.2019.2913984.
Salehi, J., Namvar, A., Gazijahani, F.S., Scenario-based co-optimization of neighboring multi carrier smart buildings under demand response exchange. J. Cleaner Prod. 235 (2019), 1483–1498.
Schram, W.L., AlSkaif, T., Lampropoulos, I., Henein, S., Van Sark, W.G., On the trade-off between environmental and economic objectives in community energy storage operational optimization. IEEE Trans. Sustain. Energy 11:4 (2020), 2653–2661.
Sebestyén, V., Renewable and sustainable energy reviews: Environmental impact networks of renewable energy power plants. Renew. Sustain. Energy Rev., 151, 2021, 111626.
Shukhobodskiy, A.A., Colantuono, G., RED WoLF: Combining a battery and thermal energy reservoirs as a hybrid storage system. Appl. Energy, 274, 2020, 115209.
Shukhobodskiy, A.A., Zaitcev, A., Pogarskaia, T., Colantuono, G., RED WoLF hybrid storage system: Comparison of CO2 and price targets. J. Cleaner Prod., 321, 2021, 128926.
Siami-Namini, S., Tavakoli, N., Siami Namin, A., A comparison of ARIMA and LSTM in forecasting time series. 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, 1394–1401, 10.1109/ICMLA.2018.00227.
Statista, S., Le prix de l’électricité en Europe. 2021 URL https://fr.statista.com/infographie/11825/.
Stepaniuk, V., Pillai, J., Bak-Jensen, B., Battery energy storage management for smart residential buildings. 2018 53rd International Universities Power Engineering Conference (UPEC), 2018, IEEE, 1–6.
Terlouw, T., AlSkaif, T., Bauer, C., van Sark, W., Optimal energy management in all-electric residential energy systems with heat and electricity storage. Appl. Energy, 254, 2019, 113580.
Terlouw, T., AlSkaif, T., Bauer, C., Van Sark, W., Multi-objective optimization of energy arbitrage in community energy storage systems using different battery technologies. Appl. Energy 239 (2019), 356–372.
Tooryan, F., HassanzadehFard, H., Collins, E.R., Jin, S., Ramezani, B., Optimization and energy management of distributed energy resources for a hybrid residential microgrid. J. Energy Storage, 30, 2020, 101556.
Tooryan, F., HassanzadehFard, H., Collins, E.R., Jin, S., Ramezani, B., Smart integration of renewable energy resources, electrical, and thermal energy storage in microgrid applications. Energy, 212, 2020, 118716.
UKDALE, F., The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. 2015, 10.1038/sdata.2015.7.
Wiesheu, M., Rutešić, L., Shukhobodskiy, A.A., Pogarskaia, T., Zaitcev, A., Colantuono, G., RED WoLF hybrid storage system: Adaptation of algorithm and analysis of performance in residential dwellings. Renew. Energy 179 (2021), 1036–1048.
Yadav, M., Jamil, M., Rizwan, M., Accomplishing approximately zero energy buildings with battery storage using FLANN optimization. 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2018, IEEE, 656–661.
Yazan M., A., Alaa M., A., Abo Eleyoun, E., Amged S., E.-W., Almoataz Y., A., Vadim, U., Ali Arshad, U., Optimal configuration and energy management scheme of an isolated micro-grid using Cuckoo search optimization algorithm. J. Franklin Inst. B 356:8 (2019), 4191–4214.
Zhang, H., Davigny, A., Colas, F., Poste, Y., Robyns, B., Fuzzy logic based energy management strategy for commercial buildings integrating photovoltaic and storage systems. Energy Build. 54 (2012), 196–206.
