Controlling a retailer's short-term financial risk exposure using demand response
2019 (engelsk)Inngår i: IET Generation, Transmission & Distribution, ISSN 1751-8687, E-ISSN 1751-8695, Vol. 13, nr 22, s. 5160-5170Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]
The transition of the electric power system to reach sustainability goals leads to new market conditions with larger uncertainties. This constitutes new challenges and opportunities for new as well as for existing market players such as retailers. In a future more volatile and unpredictable market, financial risk management becomes an important element for such actors in order to achieve viable businesses. Different instruments can be applied for this purpose, where demand response can contribute in the short-term to manage risks related to price variations and imbalance costs. This study contributes to the enhancement of retailer's businesses by presenting a stochastic optimisation model exploring the possibility to apply demand response to control financial risk exposure. The model considers trading and demand response scheduling for different customer clusters, generating optimal trading volumes for day-ahead markets while also considering the possibility to trade intra-day. The optimisation considers uncertainties in prices and loads as well as imbalance settlement costs. Risk management is integrated into the model by applying conditional value-at-risk as risk measures. The developed model has also been applied in a case study with data from the Swedish and Nordic electricity market together with simulated load profiles for different customer clusters.
sted, utgiver, år, opplag, sider
Institution of Engineering and Technology , 2019. Vol. 13, nr 22, s. 5160-5170
Emneord [en]
Commerce, Costs, Electric power systems, Optimization, Risk assessment, Sales, Stochastic models, Stochastic systems, Value engineering, Conditional Value-at-Risk, Customer cluster, Day ahead market, Demand response scheduling, Developed model, Financial risk management, Market condition, Stochastic optimisation, Risk management
HSV kategori
Identifikatorer
URN: urn:nbn:se:ri:diva-42087DOI: 10.1049/iet-gtd.2018.6708Scopus ID: 2-s2.0-85075795542OAI: oai:DiVA.org:ri-42087DiVA, id: diva2:1379191
Merknad
Funding details: Göteborg Energi; Funding text 1: The work presented has been financially supported by Göteborg Energi and ERA-Net Smart Energy Systems. Support from the Nordic power exchange Nord Pool and the Swedish TSO Svenska kraftnät concerning market data is gratefully acknowledged.; Funding text 2: Olsson, ?., Söder, L.: ‘?odeling real-time balancing power market prices using combined SARI?A and ?arkov processes’, IEEE Trans. Power Syst., 2008, 23, (2), pp. 443–450 ? an, C., Xu, Z., ? ang, Y., et al.: ‘A hybrid approach for probabilistic forecasting of electricity price’, IEEE Trans. Smart Grid, 2014, 5, pp. 463– 470 ? eron, R.: ‘Modeling and forecasting electricity loads and prices – a statistical approach’ (John ? iley & Sons Inc., Chichester, UK, 2006) ?cDonald, B., Pudney, P., Rong, J.: ‘Pattern recognition and segmentation of smart meter data’, ANZIAM J., 2014, 54, pp. ?105–?150 D'hulst, R., Labeeuw, ? ., Beusen, B., et al.: ‘Demand response flexibility and flexibility potential of residential smart appliances: experiences from large pilot test in Belgium’, Appl. Energy, 2015, 155, pp. 79–90 ‘Åtgäder för ökad efterfrågeflexibilitet i det svenska elsystemet’. Ei2016:15. (Swedish Energy ?arket Inspectorate (Ei), 2016). in Swedish Luenberger, D.G., Ye, Y.: ‘Linear and nonlinear programming’ (Springer Science & Business edia, New Y ork, NY, USA, 2008, 3rd edn.) ‘Nord Pool’, 2018. Available at http:www .nordpoolgroup.com, accessed April 2018 ‘GUROBI optimization’, 2018. Available at http:www .gurobi.com? , accessed April 2018 ‘Elpriskollen, service provided by the Swedish Energy ?arket Inspectorate (Ei)’, 2018. Available at http:www .ei.seelpriskollen , accessed April 2018 Broberg, T., Brännlund, R., Kazukauskas, A., et al.: ‘An electricity market in transition: is consumer flexibility for sale, or even for real?’ (Swedish Energy ?arket Inspectorate (Ei), Eskilstuna, Sweden, 2014) Heitsch, H., Römisch, ? .: ‘Scenario reduction algorithm in stochastic programming’, Comput. Optim. Appl., 2003, 24, pp. 187–206 Dupačová, J., Gröwe-Kuska, N., Römisch, ? .: ‘Scenario reduction in stochastic programming – an approach using probability metrics’, Math. Program., 2003, 95, pp. 493–511
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