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Brolin, M. & Sandels, C. (2019). Controlling a retailer's short-term financial risk exposure using demand response. IET Generation, Transmission & Distribution, 13(22), 5160-5170
Open this publication in new window or tab >>Controlling a retailer's short-term financial risk exposure using demand response
2019 (English)In: IET Generation, Transmission & Distribution, ISSN 1751-8687, E-ISSN 1751-8695, Vol. 13, no 22, p. 5160-5170Article in journal (Refereed) 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. 

Place, publisher, year, edition, pages
Institution of Engineering and Technology, 2019
Keywords
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
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-42087 (URN)10.1049/iet-gtd.2018.6708 (DOI)2-s2.0-85075795542 (Scopus ID)
Note

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

Available from: 2019-12-16 Created: 2019-12-16 Last updated: 2020-01-29Bibliographically approved
Persson, M., Sandels, C. & Nilsson, A. (2018). Dataanalys och avancerade algoritmer : Möjligheter med utökad mätinfrastruktur.
Open this publication in new window or tab >>Dataanalys och avancerade algoritmer : Möjligheter med utökad mätinfrastruktur
2018 (Swedish)Report (Other academic)
Abstract [sv]

Incitament för att nätbolagen skall effektivisera sin drift föreslås av Energimarknadsinspektionen.

Sett till effektivitet har 54 av 159 nätbolag högre nätförluster än 4% på årsbasis. Om dessa 54 hade gjort förbättringar i sina elnät och nått ned till en nätförlust på 4%, så skulle det innebära en minskning på totalt 143 GWh/år. Antas en kostnad på 50 öre/kWh motsvarar det 72 miljoner kr/år. Detta kan vara dels onödig förlust att behöva betala i anslutningspunkten men också en saknad inkomst för distributionselnätsägaren.

Enkätsvar från elnätsbolag visar vilka framtida utmaningar som man ser är allt från ökade energimätarkrav, förstärkningar och att värdesäkra nätet. Enkätsvaren visar att det finns stor spridning hur nätbolagen hanterar sina nätförluster, hur man i nuläget hanterar icke-tekniska förluster (ITF) och dess lokalisering och identifiering. Jämfört med kostnaden för nya generationens mätare kommer lagringskostnader för extra mätvärden och ökad mätupplösning inte vara den stora kostnadsdrivaren.

I denna rapport utvärderas korrelation, effektflödesanalys och maskininlärning för att detektera och lokaliserar olika typer av ITF. Med hjälp av maskininlärning kan förluster lokaliseras utan perfekt kunskap om elnätets uppbyggnad så länge dessa förluster följer förbrukningsmönstret och perioder utan ITF finns för upplärning av algoritmen. För slumpmässiga förluster har 3 olika metoder utvecklats och utvärderats (SiM, K:SE och K:V) som visar lovande resultat när det gäller lokalisering och detektering med hjälp av spänningsvärden hos energimätare. Upplösningen hos energimätvärdena och dess påverkan på lokaliseringen har även studerats.

Abstract [en]

An incentive for distribution companies to improve the efficiency of their operation is being suggested by Energimarknadsinspektionen.

In terms of efficiency, 54 out of 159 network companies have higher distribution losses than 4% on an annual basis. If these 54 had made improvements in their power grids and reached an energy loss of 4%, this would mean a reduction of 143 GWh / year overall. Assuming a cost of 50 öre / kWh this would equivalent to 72 million kr / year. This may be unnecessary loss of having to pay at the connection point, but also a missing income for the distribution network owner.

Surveys from electricity companies show what future challenges are ranging from increased energy metering requirements and reinforcements. The questionnaire shows that there is a large spread of how network companies manage their network losses, how to handle non-technical losses (ITF) and how they go about to locate and identify them. Compared to the cost of the new generation meters, storage costs for additional metrics and increased measurement resolution will not be the major cost driver.

In this report, correlation, power flow analysis and machine learning are evaluated in order to detect and locate different types of ITF. With the help of machine learning, losses can be located without perfect knowledge of the power grid structure as long as these losses follow the usage pattern, and periods without ITF are available for the learning of the machine learning algorithm. For random losses, 3 different methods have been evaluated (SiM, K: SE and K: V) that show promising results with regard to localizing and detecting ITF using voltage values of energy meters. The resolution of the energy meters and its influence on the possibility to localize a ITF has also been studied.

Publisher
p. 66
Series
energiforsk
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-37559 (URN)978-91-7673-537-4 (ISBN)
Available from: 2019-01-24 Created: 2019-01-24 Last updated: 2020-01-29Bibliographically approved
Paridari, K., Nordstrom, L. & Sandels, C. (2017). Aggregator strategy for planning demand response resources under uncertainty based on load flexibility modeling. In: 2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017: . Paper presented at 2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017, 23 October 2017 through 26 October 2017 (pp. 338-343).
Open this publication in new window or tab >>Aggregator strategy for planning demand response resources under uncertainty based on load flexibility modeling
2017 (English)In: 2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017, 2017, p. 338-343Conference paper, Published paper (Refereed)
Abstract [en]

Nowadays, end-users can participate in demand response (DR) programs, and even slight load reductions from many houses can add up to major load shifts in the power system. Aggregators, which act as mediators between end-users and distribution system operator (DSO), play an important role here. The aggregator contracts the end-users for DR programs, plans ahead for times when customers should shift their load, and controls the load shifts in the running time. In this paper, our main focus is on planning the end-users for load shifting. Here, we first define and formulate the flexibilities (e.g., Stamina, repetition, and capacity) related to the dynamic loads such as space heating systems (SHSs) in detached houses. Assuming some end-users being contracted for DR program, based on estimation of their house characteristics and load flexibilities, an algorithm is then proposed to plan the SHSs for load shifting. In this algorithm the states in which a flexible load can be planned, kept in backup, or unavailable are considered by the aggregator. Another algorithm has been proposed here to deal with the different sources of uncertainties (which cause some of the planned SHSs to become unavailable). Numerical results are presented at the end, which discuss performance of the proposed strategy in terms of load flexibilities, load shifts in response to DR signals, and sensitivity analysis. Here, how to estimate the houses characteristics is a difficult issue, and we approximate them based on available models in the literature.

Keywords
Dynamic loads, Electric power transmission networks, Heating, Houses, Sensitivity analysis, Smart power grids, Space heating, Demand response programs, Demand response resources, Detached house, Distribution systems, Flexibility modeling, Heating system, Numerical results, Sources of uncertainty, Uncertainty analysis
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-35805 (URN)10.1109/SmartGridComm.2017.8340694 (DOI)2-s2.0-85051029519 (Scopus ID)9781538640555 (ISBN)
Conference
2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017, 23 October 2017 through 26 October 2017
Available from: 2018-11-07 Created: 2018-11-07 Last updated: 2020-01-29Bibliographically approved
Sandels, C., Brodén, D., Widén, J., Nordström, L. & Andersson, E. (2016). Modeling office building consumer load with a combined physical and behavioral approach: Simulation and validation. Applied Energy, 162, 472-485
Open this publication in new window or tab >>Modeling office building consumer load with a combined physical and behavioral approach: Simulation and validation
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2016 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 162, p. 472-485Article in journal (Refereed) Published
Abstract [en]

Summary: Due to an expanding integration of renewable energy resources in the power systems, mismatches between electricity supply and demand will increase. A promising solution to deal with these issues is Demand Response (DR), which incentives end-users to be flexible in their electricity consumption. This paper presents a bottom up simulation model that generates office building electricity load profiles representative for Northern Europe. The model connects behavioral aspects of office workers with electricity usage from appliances, and physical representation of the building to describe the energy use of the Heating Ventilation and Air Conditioning systems. To validate the model, simulations are performed with respect to two data sets, and compared with real load measurements. The validation shows that the model can reproduce load profiles with reasonable accuracy for both data sets. With the presented model approach, it is possible to define simple portfolio office building models which subsequently can be used for simulation and analysis of DR in the power systems.

Place, publisher, year, edition, pages
Elsevier, 2016
Keywords
Demand response, Holistic, HVAC system, Markov-chain models, Office building design and architecture, Office electricity demand
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-388 (URN)10.1016/j.apenergy.2015.10.141 (DOI)000367631000043 ()2-s2.0-84945571135 (Scopus ID)
Available from: 2016-06-22 Created: 2016-06-22 Last updated: 2020-01-29Bibliographically approved
Sandels, C., Widén, J., Nordström, L. & Andersson, E. (2015). Day-Ahead Predictions of Electricity Consumption in a Swedish Office Building from Weather, Occupancy, and Temporal data (ed.). Energy and Buildings, 108, 279-290
Open this publication in new window or tab >>Day-Ahead Predictions of Electricity Consumption in a Swedish Office Building from Weather, Occupancy, and Temporal data
2015 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 108, p. 279-290Article in journal (Refereed) Published
Abstract [en]

An important aspect of demand response (DR) is to make accurate predictions for the consumption in the short term, in order to have a benchmark load profile which can be compared with the load profile influenced by DR signals. In this paper, a data analysis approach to predict electricity consumption on load level in office buildings on a day-ahead basis is presented. The methodology is: (i) exploratory data analysis, (ii) produce linear models between the predictors (weather and occupancies) and the outcomes (appliance, ventilation, and cooling loads) in a step wise function, and (iii) use the models from (ii) to predict the consumption levels with day-ahead prognosis data on the predictors. The data has been collected from a Swedish office building floor. The results from (ii) show that occupancy is correlated with appliance load, and outdoor temperature and a temporal variable defining work hours are connected with ventilation and cooling load. It is concluded from the results in (iii) that the error rate decreases if fewer predictors are included in the predictions. This is because of the inherent forecast errors in the day-ahead prognosis data. The achieved error rates are comparable with similar prediction studies in related work

Keywords
Office building electricity consumption, Load level, Building energy management system, HVAC, Exploratory data analysis, Prediction, Regression, Demand response
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-6865 (URN)10.1016/j.enbuild.2015.08.052 (DOI)2-s2.0-84943265845 (Scopus ID)28275 (Local ID)28275 (Archive number)28275 (OAI)
Available from: 2016-09-08 Created: 2016-09-08 Last updated: 2020-02-03Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9860-4472

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