ORIGINAL PAPER
The assessment of residential demand-side management in Hungary
1
Institute of Operations and Decision Sciences, Corvinus University of Budapest, Hungary
2
Cambridge Econometrics, Hungary
3
Corvinus University of Budapest, Hungary
4
REKK - Regional Centre for Energy Policy Research, Hungary
Submission date: 2024-03-14
Acceptance date: 2024-05-27
Publication date: 2024-09-24
Corresponding author
Áron Dénes Hartvig
Institute of Operations and Decision Sciences, Corvinus University of Budapest, Hungary
Institute of Operations and Decision Sciences, Corvinus University of Budapest, Hungary
Polityka Energetyczna – Energy Policy Journal 2024;27(3):5-30
KEYWORDS
TOPICS
ABSTRACT
This paper assesses the electricity bill savings potential of nationwide demand-side management programs in the residential sector. The analysis provides br oad insights into how time-of-use optimization could bring economic benefits while accelerating the deployment of renewable energy sources. We have built an electricity model with detailed household electricity consumption. Using survey data, we have created a baseline scenario that represents the current appliance usage habits of households in Hungary providing useful information on their shiftable electricity demand. We have then used time-of-use optimization of household appliances that do not affect thermal comfort in order to minimize electricity bills. Assuming different levels of participation in the demand-side management program, we reschedule the use of washing machines, dishwashers and dryers. Load optimization has a peak shaving impact on the total load, ranging from 2.2 to 3.6%. During winter, the potential for peak shaving is around –205 MW, whereas in summer, it is approximately –166 MW. Although solar energy is abundant and cheap during the day in summer, motivating households to shift their load, there is less shiftable load in the late evening hours. Therefore, the peak shaving potential is higher during winter due to the earlier peak. Modelling results from the Hungarian electricity market illustrate that smartening the grid has a bill saving potential of 6.1%, or EUR 700 million in Hungary, assuming that all households are equipped with smart meters. However, half of this reduction can be achieved with only a 25% participation rate.
METADATA IN OTHER LANGUAGES:
Polish
Ocena zarządzania popytem mieszkaniowym na Węgrzech
zarządzanie popytem, przenoszenie obciążeń, elastyczność, modelowanie w całym systemie
W artykule dokonano oceny potencjału oszczędności w rachunkach za energię elektryczną wynikających z ogólnokrajowych programów zarządzania popytem w sektorze mieszkaniowym. Analiza dostarcza szerokich informacji na temat tego, w jaki sposób optymalizacja czasu użytkowania może przynieść korzyści ekonomiczne, przyspieszając jednocześnie wdrażanie odnawialnych źródeł energii. Zbudowaliśmy model energetyczny ze szczegółowym zużyciem energii elektrycznej w gospodarstwach domowych. Korzystając z danych ankietowych, stworzyliśmy scenariusz bazowy, który przedstawia obecne nawyki użytkowania urządzeń w gospodarstwach domowych na Węgrzech, dostarczając przydatnych informacji na temat ich zmiennego zapotrzebowania na energię elektryczną. Następnie zastosowaliśmy optymalizację czasu użytkowania urządzeń gospodarstwa domowego, które nie wpływają na komfort cieplny, w celu zminimalizowania rachunków za energię elektryczną. Zakładając różny poziom uczestnictwa w programie zarządzania popytem, zmieniono harmonogram wykorzystania pralek, zmywarek i suszarek.
Optymalizacja obciążenia ma wpływ na szczytowe golenie obciążenia całkowitego, w zakresie od 2,2 do 3,6%. Zimą potencjał szczytowego golenia wynosi około –205 MW, podczas gdy latem wynosi około –166 MW. Chociaż energia słoneczna jest dostępna w dużych ilościach i tania w ciągu dnia latem, co motywuje gospodarstwa domowe do zmiany obciążenia, w późnych godzinach wieczornych obciążenie jest mniejsze. Dlatego potencjał redukcji szczytu jest wyższy zimą ze względu na wcześniejszy szczyt. Wyniki modelowania węgierskiego rynku energii elektrycznej pokazują, że inteligentniejsza sieć ma potencjał oszczędności rachunków wynoszący 6,1%, czyli 700 mln EUR na Węgrzech, zakładając, że wszystkie gospodarstwa domowe są wyposażone w inteligentne liczniki. Jednak połowę tej redukcji można osiągnąć przy zaledwie 25% wskaźniku uczestnictwa.
REFERENCES (39)
1.
Awais et al. 2015 – Awais, M., Javaid, N., Shaheen, N., Iqbal, Z., Rehman, G., Muhammad, K. and Ahmad, I. 2015. An efficient genetic algorithm based demand-side management scheme for smart grid. 2015 18th International Conference on Network-based Information Systems, IEEE, pp. 351–356, DOI: 10.1109/NBiS.2015.54.
2.
Babatunde et al. 2020 – Babatunde, O.M., Munda, J.L. and Hamam, Y. 2020. Power system flexibility: A review. Energy Reports 6, pp. 101–106, DOI: 10.1016/j.egyr.2019.11.048.
3.
BBC 2023. Energy saving scheme: National Grid pays people to cut power again due to cold weather. [Online] https://www.bbc.com/news/busin... [Accessed: 2023-09-10].
4.
Bharathi et al. 2017 – Bharathi, C., Rekha, D. and Vijayakumar, V. 2017. Genetic algorithm based demand-side management for smart grid. Wireless personal communications 93, pp. 481–502, DOI: 10.1007/s11277-017-3959-z.
5.
Bloomberg 2023. European Power Prices Go Below Zero Again as Solar Output Surges. [Online] https://www.bloomberg.com/news... [Accessed: 2023-09-20].
6.
Directive 2009/72/EC of the European Parliament and of the Council of 13 July 2009 concerning common rules for the internal market in electricity and repealing Directive 2003/54/EC (2009).
7.
ESO 2023. Demand Flexibility Service (DFS). [Online] https://www.nationalgrideso.co... [Accessed: 2023-09-20].
8.
Freire-Barcelo et al. 2022 – Freire-Barcelo, T., Martin-Martinez, F., Sanchez-Miralles, A., Rivier, M., San Roman, T.G., Huclin, S., Chaves Ávila, J.P. and Ramos, A. 2022. Storage and demand response contribution to firm capacity: Analysis of the Spanish electricity system. Energy Reports 8, pp. 10546–10560, DOI: 10.1016/j.egyr.2022.08.014.
9.
Hayn et al. 2014 – Hayn, M., Bertsch, V. and Fichtner, W. 2014. Electricity load profiles in Europe: The importance of household segmentation. Energy Research & Social Science 3, pp. 30–45, DOI: 10.1016/j.erss.2014.07.002.
10.
Jiang, X. and Xiao, C. 2019. Household energy demand management strategy based on operating power by genetic algorithm. IEEE Access 7, pp. 96414–96423, DOI: 10.1109/ACCESS.2019.2928374.
11.
Khatoon, S. and Singh, A.K. 2014. Effects of various factors on electric load forecasting: An overview. 2014 6th IEEE Power India International Conference (PIICON), pp. 1–5), DOI: 10.1109/POWERI.2014.7117763.
12.
Kirkerud et al. 2021 – Kirkerud, J.G., Nagel, N.O. and Bolkesjø, T.F. 2021. The role of demand response in the future renewable northern European energy system. Energy 235, DOI: 10.1016/j.energy.2021.121336.
13.
Kökény, L. and Hortay, O. 2020. A survey of popular attitudes to deferment of electricity consumption in Hungary (A villamosenergia-fogyasztás elhalasztásával kapcsolatos lakossági attitűd felmérése magyarországon). Közgazdasági Szemle 67(7–8), pp. 657–687, DOI: 10.18414/KSZ.2020.7-8.657.
14.
Laicane et al. 2015 – Laicane, I., Blumberga, D., Blumberga, A. and Rosa, M. 2015. Reducing household electricity consumption through demand-side management: the role of home appliance scheduling and peak load reduction. Energy procedia 72, pp. 222–229, DOI: 10.1016/j.egypro.2015.06.032.
15.
Linssen et al. 2017 – Linssen, J., Stenzel, P. and Fleer, J. 2017. Techno-economic analysis of photovoltaic battery systems and the influence of different consumer load profiles. Applied Energy 185, pp. 2019–2025, DOI: 10.1016/j.apenergy.2015.11.088.
16.
Lutzenhiser, L. 1993. Social and behavioral aspects of energy use. Annual review of Energy and the Environment 18(1), pp. 247–289, DOI: 10.1146/annurev.eg.18.110193.001335.
17.
Lüth et al. 2018 – Lüth, A., Zepter, J.M., del Granado, P.C. and Egging, R. 2018. Local electricity market designs for peer-to-peer trading: The role of battery flexibility. Applied energy 229, pp. 1233–1243, DOI: 10.1016/j.apenergy.2018.08.004.
18.
Markets Insider 2023. European electricity prices tumble into negative territory amid glut of green energy. [Online] https://markets.businessinside... [Accessed: 2023-09-20].
19.
MEKH 2024. Nem engedélyköteles kiserőművek és háztartási méretű kiserőművek adatai. [Online] https://mekh.hu/nem-engedelyko... [Accessed: 2024-03-15] (in Hungarian).
20.
Ministry of Energy 2023. 2023 első felében több, mint 1 gigawattal nőtt a hazai napelemes kapacitás. [Online] https://kormany.hu/hirek/2023-... [Accessed: 2023-09-25].
21.
Misconel et al. 2021 – Misconel, S., Zöphel, C. and Möst, D. 2021. Assessing the value of demand response in a decarbonized energy system–A large-scale model application. Applied Energy 299, DOI: 10.1016/j.apenergy.2021.117326.
22.
Mondal et al. 2017 – Mondal, A., Misra, S. and Obaidat, M.S. 2017. Distributed home energy management system with storage in smart grid using game theory. IEEE Systems Journal 11(3), pp. 1857–1866, DOI: 10.1109/JSYST.2015.2421941.
23.
Mota et al. 2022 – Mota, B., Faria, P. and Vale, Z. 2022. Residential load shifting in demand response events for bill reduction using a genetic algorithm. Energy 260, DOI: 10.1016/j.energy.2022.124978.
24.
Nguyen et al. 2012 – Nguyen, H.K., Song, J.B. and Han, Z. 2012. Demand-side management to reduce peak-to-average ratio using game theory in smart grid. [In:] 2012 Proceedings IEEE INFOCOM Workshops, pp. 91–96, DOI: 10.1109/INFCOMW.2012.6193526.
25.
Nicolson et al. 2018 – Nicolson, M.L., Fell, M.J. and Huebner, G.M. 2018. Consumer demand for time of use electricity tariffs: A systematized review of the empirical evidence. Renewable and Sustainable Energy Reviews 97, pp. 276–289, DOI: 10.1016/j.rser.2018.08.040.
26.
O’Shaughnessy et al. 2018 – O’Shaughnessy, E., Cutler, D., Ardani, K. and Margolis, R. 2018. Solar plus: A review of the end-user economics of solar PV integration with storage and load control in residential buildings. Applied energy 228, pp. 2165–2175, DOI: 10.1016/j.apenergy.2018.07.048.
27.
Palensky, P. and Dietrich, D. 2011. Demand-side management: Demand response, intelligent energy systems, and smart loads. IEEE transactions on industrial informatics 7(3), pp. 381–388, DOI: 10.1109/TII.2011.2158841.
28.
Pratt, B.W. and Erickson, J.D. 2020. Defeat the Peak: Behavioral insights for electricity demand response program design. Energy Research & Social Science 61, DOI: 10.1016/j.erss.2019.101352.
29.
Rajamand, S. 2020. Effect of demand response program of loads in cost optimisation of microgrid considering uncertain parameters in PV/WT, market price and load demand. Energy 194, DOI: 10.1016/j.energy.2020.116917.
30.
REFLEX data repository 2019. Database of the REFLEX Project – Analysis of the European Energy System. [Online] https://data.esa2.duckdns.org/... [Accessed: 2023-12-20].
31.
Sanquist et al. 2012 – Sanquist, T.F., Orr, H., Shui, B. and Bittner, A.C. 2012. Lifestyle factors in US residential electricity consumption. Energy Policy 42, pp. 354–364, DOI: 10.1016/j.enpol.2011.11.092.
32.
Schipper et al. 1989 – Schipper, L., Bartlett, S., Hawk, D. and Vine, E. 1989. Linking life-styles and energy use: a matter of time? Annual review of energy 14(1), pp. 273–320, DOI: 10.1146/annurev.eg.14.110189.001421.
33.
Siano, P. (2014). Demand response and smart grids — A survey. Renewable and sustainable energy reviews 30, pp. 461–478, DOI: 10.1016/j.rser.2013.10.022.
34.
Szőke et al. 2021 – Szőke, T., Hortay, O. and Farkas, R. 2021. Price regulation and supplier margins in the Hungarian electricity markets. Energy Economics 94, DOI: 10.1016/j.eneco.2021.105098.
35.
Taik, S. and Kiss, B. 2019. Smart household electricity usage optimisation using MPC and MILP. [In:] 2019 22nd International Conference on Process Control (PC19), pp. 31–36, IEEE, DOI: 10.1109/PC.2019.8815043.
36.
Tounquet, F. and Alaton, C. 2019. Benchmarking smart metering deployment in the EU-28. Tractebel Impact.
37.
van der Veen et al. 2018 – van der Veen, A., van der Laan, M., de Heer, H., Klaasen, E. and van der Reek, W. 2018. Flexibility Value Chain. USEF. [Online] https://www.usef.energy/app/up... [Accessed: 2023-12-19].
38.
Vitiello et al. 2022 – Vitiello, S., Andreadou, N., Ardelean, M. and Fulli, G. 2022. Smart metering roll-out in europe: Where do we stand? cost benefit analyses in the clean energy package and research trends in the green deal. Energies 15(7), DOI: 10.3390/en15072340.
39.
Yang et al. 2012 – Yang, P., Tang, G. and Nehorai, A. 2012. A game-theoretic approach for optimal time-of-use electricity pricing. IEEE Transactions on Power Systems 28(2), pp. 884–892, DOI: 10.1109/TPWRS.2012.2207134.
| eISSN: | 2720-569X |
| ISSN: | 1429-6675 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
