Simplify Bidding on the Day-Ahead Electricity Market Nordpool through Structured Time-Series

In Sweden, electricity is purchased on a so-called day-ahead spot market (Nordpool). The electricity is based on a predicted hourly need for the upcoming day [4, 5]. Production and consumption of electricity need to be balanced since it is hard to store electricity [25]. Today, electricity companies struggle to uphold this balance using currently available tools. A potential solution would be to support bidders by visualizing time-series. Then they could identify time-series lacking data crucial to the prediction phase and resolve them. In this thesis, a prototype was implemented consisting of different views/use-cases, aimed at simplifying the bidding process for balance responsible parties (BRPs). The prototype consisted of structured timeseries and presents predicted data in a way that makes the decision making easier when placing bids. Results from a study using the prototype with BRPs and professionals showed that the use-cases/views are useful in terms of 1) getting a better structure, 2) identifying incomplete timeseries, 3) better quality assurance of the time-series and 4) lowering the time-consumption. Additionally, the bidders suggested that the addition of references, in terms of other prediction methods than the one that was used could improve their decision making.

[1]  Klaus Skytte,et al.  The regulating power market on the Nordic power exchange Nord Pool: an econometric analysis , 1999 .

[2]  J. Contreras,et al.  Forecasting Next-Day Electricity Prices by Time Series Models , 2002, IEEE Power Engineering Review.

[3]  Daniel A. Keim,et al.  Information Visualization and Visual Data Mining , 2002, IEEE Trans. Vis. Comput. Graph..

[4]  Helwig Hauser,et al.  Linking Scientific and Information Visualization with Interactive 3D Scatterplots , 2004, WSCG.

[5]  T. Niimura,et al.  Short-term electricity price modeling and forecasting using wavelets and multivariate time series , 2004, IEEE PES Power Systems Conference and Exposition, 2004..

[6]  D. Kirschen,et al.  Forecasting system imbalance volumes in competitive electricity markets , 2004, IEEE Transactions on Power Systems.

[7]  Luca Chittaro,et al.  Visualizing information on mobile devices , 2006, Computer.

[8]  M. Mahjoob,et al.  Chaotic time series forecasting using locally quadratic fuzzy neural models , 2008 .

[9]  Phoebe Sengers,et al.  Mapping the landscape of sustainable HCI , 2010, CHI.

[10]  Khan Muzammil,et al.  Data and Information Visualization Methods, and Interactive Mechanisms: A Survey , 2011 .

[11]  Svetlozar T. Rachev,et al.  Balancing energy strategies in electricity portfolio management , 2011 .

[12]  Filip Kis,et al.  Enough power to move: dimensions for representing energy availability , 2012, Mobile HCI.

[13]  Daniel M. Frances,et al.  Optimization-Based Bidding in Day-Ahead Electricity Auction Markets: A Review of Models for Power Producers , 2012 .

[14]  Kun Zhu,et al.  A seasonal ARIMA model with exogenous variables for elspot electricity prices in Sweden , 2013, 2013 10th International Conference on the European Energy Market (EEM).

[15]  David Steen,et al.  Challenges of integrating solar and wind into the electricity grid , 2014 .

[16]  Sarvapali D. Ramchurn,et al.  Doing the laundry with agents: a field trial of a future smart energy system in the home , 2014, CHI.

[17]  Lara S. G. Piccolo,et al.  Designing to raise collective awareness and leverage energy savings , 2015, BCS HCI.

[18]  Cheryl Ann Alexander,et al.  Big Data and Visualization: Methods, Challenges and Technology Progress , 2015 .

[19]  Filip Kis,et al.  Linking Data to Action: Designing for Amateur Energy Management , 2016, Conference on Designing Interactive Systems.

[20]  Tom Rodden,et al.  'A bit like British Weather, I suppose': Design and Evaluation of the Temperature Calendar , 2016, CHI.

[21]  R. V. D. Veen,et al.  The electricity balancing market: Exploring the design challenge , 2016 .

[22]  Pierre Pinson,et al.  Towards fully renewable energy systems - Experience and trends in Denmark , 2017 .

[23]  Jacquelien M. A. Scherpen,et al.  Distributed Optimal Control of Smart Electricity Grids With Congestion Management , 2017, IEEE Transactions on Automation Science and Engineering.

[24]  Brendan Walker,et al.  Performing Research: Four Contributions to HCI , 2017, CHI.

[25]  Charles Perin,et al.  MyBrush: Brushing and Linking with Personal Agency , 2018, IEEE Transactions on Visualization and Computer Graphics.

[26]  Ata Kasimoglu The Impact Of Wind Energy Development On Swedish Elspot Day-Ahead Prices , 2018 .

[27]  Matias Negrete-Pincetic,et al.  Portfolio applications in electricity markets review: Private investor and manager perspective trends , 2018 .

[28]  Ying Zhang,et al.  Impact of the Uncertainty of Distributed Renewable Generation on Deregulated Electricity Supply Chain , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[29]  B. Mathiesen,et al.  Beyond sensitivity analysis: A methodology to handle fuel and electricity prices when designing energy scenarios , 2018 .

[30]  Klaus Skytte,et al.  Market Prices in a Power Market with more than 50% Wind Power , 2018 .

[31]  Pasapitch Chujai,et al.  Time Series Analysis of Household Electric Consumption with ARIMA and ARMA Models , 2022 .