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Frankfurt (Germany), 6-9 June 2011 Author Name-Country-SessionX-BlockY- Paper ID 1 DEVELOPMENT OF A METHODOLOGY FOR FORECASTING ELECTRICITY-PRICE SERIES.

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Presentation on theme: "Frankfurt (Germany), 6-9 June 2011 Author Name-Country-SessionX-BlockY- Paper ID 1 DEVELOPMENT OF A METHODOLOGY FOR FORECASTING ELECTRICITY-PRICE SERIES."— Presentation transcript:

1 Frankfurt (Germany), 6-9 June 2011 Author Name-Country-SessionX-BlockY- Paper ID 1 DEVELOPMENT OF A METHODOLOGY FOR FORECASTING ELECTRICITY-PRICE SERIES TO IMPROVE DEMAND RESPONSE INITIATIVES  Authors: Gabaldón, A. (1), Hernández, M. (1), Guillamón, A. (1) ; Ruiz, MªC. (1), Valero S. (2), Ortiz, M. (2), Alvarez C. (3),  Affiliation:  (1): Universidad Politécnica de Cartagena (Cartagena, Spain)  (2): Universidad Miguel Hernández (Elche, Spain)  (3): Instituto de Ingeniería Energética (IIE-UPV, Valencia, Spain) GABALDON-SP-S6-0697

2 Frankfurt (Germany), 6-9 June Objectives  To increase of the participation of small and medium customers in electricity markets.  How? With … Physical load models ► knowledge of load response. Price forecasting ► more time for load rescheduling. Economic model ► evaluation of cost-effectiveness  Where? DR (Demand Response) in Energy Markets Capacity Markets: these are in expansion (NE and PJM, USA)

3 Frankfurt (Germany), 6-9 June 2011 Characteristics of the customers  Residential segment (rated power: 4 to 10kW)  Temperature levels: -5 to 10ºC (winter)  End-Uses (suitables for DR) and shares for UE Heating: 18.7% Cold Appl.: 15.2% Lighting: 10.5% Water Heater: 8.6% AC: 4.4% Heat Storage: yes Aggregated Load Curve

4 Frankfurt (Germany), 6-9 June 2011 Model. 1) Determine E ik elasticities  Definition: the changes in customer demand D according to prices P Own price elasticities (E<0): at the same time i Elasticity of substitution (E>0): peak (i) to off-peak (k) Data sources: pilots in USA, Australia, France, Canada  More info in

5 Frankfurt (Germany), 6-9 June 2011 Model. 2) Peak-Price Forecasting (I)  We consider 48h Real-Time Energy Prices: (NE-ISO, 2006). ► Pattern Extraction and Clustering.  We analyze Real-Time prices “on line” (in our example: 2007 prices) Identify 2007 prices (0-24h) with ‘06 patterns Forecast of next 24h RT-LMP through ‘06 patterns.  An accurate estimation is not necessary We need trends of prices (high-price periods)

6 Frankfurt (Germany), 6-9 June 2011 Model. 2) Peak-Price Forecasting (II)  High price periods forecast: cluster 1 (max. price) Identification (0-24hr) Forecast (25-48hr) ClusterSize 143 ( ↑ €) ( ↓ €)

7 Frankfurt (Germany), 6-9 June 2011 Model. 3) Economic model  Optimization procedures to maximize customer benefit Benefit function (B): (Schweppe et al., 1988) New Demand (D) with Price DR Programs (PRP)?  ± Demand increments due to E ik,∆P & Load Recovery (%)  Hints: Load Recovery (LR) often is not considered (i.e energy payback, task rescheduling)

8 Frankfurt (Germany), 6-9 June 2011 Model. 4) Load classification for DR Criteria: response possibility (t=i..i+n) & payback (t= i+n+1 to k). Notice: Response (kWh) ≠ En. Recovery (kWh) LoadFCMElasticity E ik Energy Recovery Own (t=i►i)Cross (t=i►k)t=i+1t=k HVACYes No LightingYes No Response HVAC En. Recovery i+1 k

9 Frankfurt (Germany), 6-9 June 2011 Model. 5) Load Response evaluation Minimum threshold aggregation: ~ 100kW of net response Analyze the response of loads: Eg. 2kW HVAC units  External temp: 6ºC, setpoint 20ºC, comfort level >15ºC  Average unforced duty cycle m(t)=72%. m(t) = ton/(ton+toff)  DR strategies u(t) (ton/(ton+toff)) are simulated. Hint! u(t) < m(t) Indeed, available reductions for FCM auctions are evaluated (eu: end- use):

10 Frankfurt (Germany), 6-9 June 2011 Simulation Results (I)  Residential users ► Demand Aggregator: Manages 10 Transformation Centers CT (400kVA each one) Needs a minimum reduction (100kW) ► Premise: 20% of customers should have DR ability (conservative) Surveys RT high prices (price forecast tool) and plans response Storage Energy recovery Peak clipping

11 Frankfurt (Germany), 6-9 June 2011 Simulation Results (II)  Load portfolio: HVAC (heat), WH (water h), DF (dual fuel), HS (h. storage) Loads are divided in control groups DR policies: cycling control (HVAC), demand shifting (WH, HS), change of supply to gas (DS) HVAC, DF WH, HS Recovery, Storage

12 Frankfurt (Germany), 6-9 June 2011 Simulation Results (III)  Response ability in CTs (20% of CT customers) Own and cross responses (due to E ii & E ik )

13 Frankfurt (Germany), 6-9 June Simulation Results (IV)  Seasonal economic evaluation (Price-Response) Peak clipping: 4,5%. Cost savings: 9% Item (units)CT Max. Demand reduction (kW)18 Energy reduction/event (kWh)180 Event days (days)) ► see cluster nº 140 Guaranteed energy payment ($/kWh)0.100 Average RT-LMP during event ($/kWh)0.120 Average Electricity price (Day-Ahead) ($/kWh)0.06 RESPONSE BENEFIT (Winter) ($) $1425

14 Frankfurt (Germany), 6-9 June 2011 Conclusions (I)  Through DR customer obtains interesting benefits. Capacity and Demand Response are another possibilities  Through these tools aggregators identify better: The load response. Energy Price alternatives. Barriers: enabling technology is needed.

15 Frankfurt (Germany), 6-9 June 2011 Conclusions (II)  And Capacity Markets benefits $  Customer response increases demand elasticity ►A lot of benefits for the market and the society!!! Figure source: CIGRE working group C6-09, Demand-Side Integration Societal Benefit

16 Frankfurt (Germany), 6-9 June 2011 Questions? Thanks for your attention! ETS de Ingeniería Industrial, Spain


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