Load Control in Real Time Price Prediction

Arsi, Irisa
Autor:in (Körperschaft)
Typ der Arbeit
11 - Studentische Arbeit
Herausgeber:in (Körperschaft)
Übergeordnetes Werk
DOI der Originalausgabe
Reihe / Serie
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
Verlag / Herausgebende Institution
Hochschule für Wirtschaft FHNW
Verlagsort / Veranstaltungsort
Switzerland’s electricity consumption in 2014 was 59.3 TWh (Abrell, 2016) and continues to rise every year. As residential needs for electrical energy increase, so does the demand (Abrell, 2016; Filippini, 2011; IEA, 2012; Zhao et al, 2013). As a result, the necessary energy for meeting the demand cannot be provided by the power grid (Abrell, 2016; Filippini, 2011). The Swiss government has tried by applying new methods in price calculation for electricity to help shift the loads to different times (Abrell, 2016). Nevertheless, over- loadings and blackouts occur several times per year creating high maintenance costs (Abrell, 2016; Filippini, 2011), for the production companies which reflects to the users’ payments as well. On the one hand consumers' demand aims at electrical energy of high quality and reliability (Abrell, 2016), but on the other hand producers’ aim in less maintenance costs. A clear solution is needed for the demand and supply of Switzerland’s grid to balance. A new solution, a new methodology based not entirely in technology but also in the correct use of Information Systems. This paper will describe a new proposal, solution for the Swiss energy production and consumption to balance through energy scheduling and flexible pricing. Smart buildings and smart appliances, will provide users, with an ECO efficient use of the energy through the Information. The users can create their own demand schedule, in accordance to the calculated prices by the combination of RTP and IBR and their actual needs. During Real Time Electricity Pricing (RTP) prices can be generated hourly and transmitted to users. A problem that increases with RTP is that users tend to maximize the use of their appliances during the low peak prices and potentially create overloads, which could lead to instability of the grid or even a power blackout. In order, to avoid such problems, and secure except of flexible prices also reliability and stability for the system, RTP needs to be combined with the Inclining Block Rate (IBR) methodology. During IBR pricing prices can be calculated according to the loads. The combination of the two methodologies give the possibility to the users not only to schedule their energy use by time but loads as well. An important fact that rises through this proposal is the possibility, given to the energy production companies and the government, to balance the maintenance costs which will lead in saving thousands of francs every year by simply involving the end-users in the electric grid operation. Simply by giving the possibility to users to control their appliances’ consumption, for different periods, by reducing their consumption or shift their loads to low peak periods.
RTP Real Time Pricing, IBR Inclining Block Rate, DR Demand Response, IS Information Systems, ICT Information Communication Technology, RTEP Real Time Electricity Prices, PAR Power Peak Ratio, TOU Time of Use, TOUR Time of Use Pricing, CPP Critical Peak Pricing, kWh Kilowatt- hour, TWh Terawatt- hour, RES Renewable Energy Sources, WEM Wholesale Energy Market, ES Energy Scheduling, FBE Free Basic Electricity, TSO Transmission system operator, RA Regulatory authority, ElCom Federal Electricity Commission, EMS Energy management system, EMC Energy management controller, AOA Automatically operated appliance, MOA Manually operated appliance, LOT Length operation time, OTI Operation time interval
Fachgebiet (DDC)
004 - Computer Wissenschaften, Internet
003 - Systeme
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
Während FHNW Zugehörigkeit erstellt
Keine Begutachtung
Open Access-Status
ARSI, Irisa, 2017. Load Control in Real Time Price Prediction. Hochschule für Wirtschaft FHNW. Verfügbar unter: https://doi.org/10.26041/fhnw-1150