Model to Calculate the Customer Base-Line for a Demand Response Program in the Colombian Power Market

Transcription

1 Model to Calculate the Customer Base-Line for a Demand Response Program in the Colombian Power Market Escobar, Edgar. Cadena, Ángela. Correal, Maria Elsa. Duran, Hernando. Universidad de los Andes 32nd Annual IAEE conference, "Energy, Economy, Environment: The Global View." June

2 Agenda 1. Motivation and objective 2. Problem description 3. Solution 4. Performance Analysis 5. Conclusions and Future Work 6. References

3 Agenda 1. Motivation and objective 2. Problem description 3. Solution 4. Performance Analysis 5. Conclusions and Future Work 6. References

4 Motivation 1. Motivation and objective Importance of the Demand Response in the Power Market. The Colombian Reliability Charge Mechanism, considers a Voluntary Load Reduction Program. Importance of active participation as a support for the Smart Grids.

5 Objective 1. Motivation and objective This research presents a method to produce a Baseline electricity consumption model to be used by the first Demand Response Program in Colombia. The method is a respond to the needs of the new regulatory scheme to guarantee the reliability in the supply of the electric energy in Colombia called the Reliability Charge, which includes a Voluntary Load Reduction (VLR) program in the form of a contingency mechanism. The research includes: Analysis of the international experience for both demand response (DR) and Baseline Consumption methods (BLC). A Baseline Consumption method specific to the Colombian requirements: Analysis and comparison of different BLC methods. Evaluation of the best chosen BLC method for the Colombian scenario.

6 Agenda 1. Motivation and objective 2. Problem description 3. Solution 4. Performance Analysis 5. Conclusions and Future Work 6. References

7 International Experience There are 5 reasons to have a Demand Response program: 1. An efficient market requires active demand. 2. Power Scarcity: United States, Nordic Countries, France, and United Kingdom (countries under seasonal weather). 3. Energy Scarcity: Nordic Countries, and Colombia (countries with a high dependence of intermittent sources of primary energy (hydro)). 4. Environmental Interests: Australia, and Greece. 5. System Stability: United Kingdom, and Nordic Countries. 2. Problem description

8 Colombia s Situation 2. Problem description Colombia s energy sources are mainly obtained from hydro resources (65% and increasing). Energy source MW % Hydroelectric 8, Thermal 4, Gas 2,757.0 Carbon Minors Hydro & Thermal Wind 18.4 Co-Generation Total National Generation Source: XM, , Source:UPME, 2009.

9 Colombia s Situation 2. Problem description The reliability charge scheme was created as a mechanism to assure the electricity service during scarcity periods. Spot Price vis-à-vis Contract Price Source: CREG, An essential features of this new scheme is the existence of the Firm Energy Obligation (OEF), which is a commitment from the generator (backed by a physical resource) to produce energy at a pre-established price during scarcity periods.

10 Colombia s Situation 2. Problem description Scenario 1: Spot Price < Scarcity Price Scenario 2: Spot Price > Scarcity Price P Expected demand Scarcity price P Spot Price Expected demand Scarcity price Spot Price G1 G2 G3 Q G1 G2 G3 Q The generator who acquires an OEF will receive: i)a fixed remuneration during the commitment period of the OEF, ii)the price of the contract for each KWH of the OEF in which the generator sold its firm energy during scarcity periods. Response to Scenario 2: OEF Activation P OEF G1 OEF G2 Expected demand OEF G3 Scarcity price Contract Price Q

11 Colombia s Situation 2. Problem description The Contingency Mechanisms are market instruments, which aim to facilitate the coverage of demand in scarcity periods and the compliance of the OEF of the generators. Source: CREG, 2009.

12 Colombia s Situation 2. Problem description What is expected from the Demand Response Program To take advantage of the opportunity that some end-users have to back-up generation or to modify their consumption, the Voluntary Disconnectable Demand will allow the generator to approach these users. The demand reduction done by the users will be discounted from the generator s obligation and in exchange the generator will remunerate or compensate the user. What is expected from the Baseline of Consumption (BLC) In order to obtain an efficient result from the program, a Baseline of consumption is essential to: Efficiently understand, estimate and forecast the consumption and savings of a user. Determine values of consumption that may be consider as a saving. Given that users may take advantage of the program, The ability to reduce risk.

13 Agenda 1. Motivation and objective 2. Problem description 3. Solution 4. Performance Analysis 5. Conclusions and Future Work 6. References

14 Method Objective and Characteristics Colombia is an unique scenario, Energy scarcity with recorded historical data (Periods under Niño) Objective: Energy savings in the short term to assure service during scarcity periods. The program will be applied by the System Regulator CREG (Comisión de Regulación de Energía y Gas) to be used as a contingency mechanism to support the generator and their Obligations of Firm Energy. Characteristics: The program must use a Baseline of consumption in order to understand and estimate the users consumption and its ability to save. The Demand Response program must respond to a hybrid program that includes Peak Clipping and Price Response. The program will be available to the industry and some commercial users. Efficiency to estimate consumptions and forecast savings. 3. Solution

15 Baseline of Consumption Characteristics 3. Solution 1. Easy to implement and upgradeable (Using a basic spreadsheet). 2. Flexible and as general as possible. This, to allow the method to be used by any user regardless of its individual characteristics. Some of this characteristics may include: Seasonality, Stability around an average, decreasing or increasing slopes and/or level changes. 3. The method must be appropriate for seasonal time series. The data showed a behaviour that repeats itself weekly and depends on daily basis. The method should be able to estimate the effect of each day on the consumption behaviour of the user. 4. The Baseline of Consumption must generates a value that indicates accuracy to estimate the forecasted values. Consumer with a historical homogeneous behaviours must be rewarded with more reliable forecasts and therefore better chances to obtain incentives.

16 Studied Baseline Consumption Methods 3. Solution 1. Linear Regression 2. Linear Regression with a scalar adjustment 3. Holt Winter`s 4. Modified Decomposition

17 1. Lineal Regression (RL) 3. Solution Builds 7 estimations, one for each day of the week. Highly reliable for fragments scenarios and long term periods. Based in a typical lineal regression model, it uses 13 weeks of data and a typical error to establish savings. D k D ( k) a : k 1,..., 0 N where, a= slope, D= average in the first period The parameters are calculated in order to minimize the following equation: 2 N k 0 ( D k D k ) 2 Error

18 3. Solution 2. Linear Regression with a scalar adjustment (SR) Based in the lineal regression (Durán, 2006) and scalar adjustment (Encinas and Redondo, 2006). Includes a new parameter that values the forecast Z(k) in terms of the previous measured consumption Y(k-1) : Y k Y k ( Z Y 1 ) k k

19 3. Holt Winter`s (HW) 3. Solution Econometric method of exponential smoothing (multiplicative) used for seasonal series and a lineal type slope. Source: Carcedo, Julián. 2004

20 4. Decomposition Method (MD) 3. Solution Based on the representation of the daily consumption of a final user C t, using parameters of slope T t, seasonality Et and error u t. C t T t E t u t

21 4. Decomposition Method Methodology 3. Solution First Stage: Data acquisition and Data transformation Capture: The Baseline of consumption will be made in a daily frequency. The method uses a 105 days interval that corresponds to 15 weeks; this value was chosen so the method can capture the behaviour of the user. Transforming values equal to zero: Identify the zero values in the historical data and its proper i group. The value will be transformed with the average of the last 5 days that own the same i group. Anomalies: Data referring to public holydays, periods of scarcity and data under 2 standard deviations will be replaces using the same process as the zero transformation. Second Stage: Seasonal parameter PM t C t 3 C t Third Stage: Slope estimation Ct Dt E 2 C t 1 C t 7 C t t 1 C t 2 C t 3, t 4,5,... T t C PM t E a bt t u t i ~ E i 1

22 3. Solution 4. Decomposition Method Methodology Fourth Stage: Forecast Cˆ N k T N k E k, k 1,2,..., 7. Confidence Interval and Saving Equation Trust Percentage C T k ± 1.28 MSE 80 It can be accepted as a saving only those values in which the real consumption it s under the low limit of the forecast. However, final savings should be accounted as the difference between the Forecast and the real consumption.

23 Agenda 1. Motivation and objective 2. Problem description 3. Solution 4. Performance Analysis 5. Conclusions and Future Work 6. References

24 Method Comparison 4. Performance Analysis The studied methods were compared using an Average Square Root Error method. All methods were studied under the same circumstances. User Type Percentage User Type Percentage User 1 RL 1,98% User 5 RL 3,02% SR 2,09% SR 3,20% HW 0,38% HW 5,53% MD 0,25% MD 5,37% User 2 RL 19,68% User 6 RL 4391,65% SR 18,49% SR 2363,64% HW 0,20% HW 5,59% MD 0,07% MD 7,97% User 3 RL 29784,03% User 7 RL 909,90% SR 28192,33% SR 910,15% HW 1103,16% HW 0,45% MD 1,96% MD 273,11% User 4 RL 2,47% User 8 RL 4,58% SR 2,20% SR 5,03% HW 0,99% HW 2,21% MD 0,24% MD 1,96% ASRE i n 0 BCL i MR n MR 2 i Average Square Root Error: This method of verification helps to state the accuracy of the method to forecast a series of consumption. i 2 RL: Lineal Regression, SR: Scalar Regression, HW: Holt Winters, MD: Decomposition Method

25 Decomposition Method 4. Performance Analysis 16 weeks of data were used, were the first 15 were used to perform the method and the last one to validate the obtained forecast. In order to evaluate the method parameter of precision and viability were calculated in order to establish the method efficiency. Valid days /7 MSE % MSE Valid days /7 MSE % MSE User 1 7 0,15% 2,22 User 9 4 3,01% 1174,66 User 2 6 0,14% 2,52 User ,80% 3356,41 User 3 5 3,00% 102,82 User ,47% 830,10 User 4 7 0,06% 1,60 User ,31% 38,36 User ,54% 88,60 User ,66% 201,39 User ,12% 99,52 User ,24% 4586,80 User ,95% 89,97 User ,34% 1516,72 User 8 5 2,12% 2,60 User ,43% 1055,76 Valid days /7 MSE % MSE Valid days /7 MSE % MSE User ,35% 1297,88 User ,37% 363,86 User ,02% 86,89 User ,45% 924,60 User ,68% 2066,29 User ,84% 1764,61 User ,15% 175,57 User ,26% 213,56 User ,05% 218,28 User ,55% 774,35 User ,05% 9401,75 User ,70% 53,76 User ,60% 139,66 User ,99% 702,20 User ,33% 1924,99 User ,79% 605,93

26 4. Performance Analysis Consumption and Forecast

27 4. Performance Analysis Consumption and Forecast

28 Agenda 1. Motivation and objective 2. Problem description 3. Solution 4. Performance Analysis 5. Conclusions and Future Work 6. References

29 Conclusions 5. Conclusions and Future Work The proposed decomposition method shows the ability to estimate electricity consumption and to measure electricity savings. The decomposition method fulfilled the established criteria (uniqueness, simplicity, ability to be reproduced) to be used and applied in the Voluntary Load Reduction program. Even though the heterogeneity of final consumers, the method manages to follow consumption series that exhibit seasonal behavior and moderate variations along the time analysis period.

30 Conclusions 5. Conclusions and Future Work The needs of Colombia are particular. Colombia s Demand Response is idealized to support the generator and assure electricity service during shortage periods. Even though, final user trickery is expected, the need of a reliability program is necessary. The parameters for the time interval (105 days) and the confidence interval (1.28 : 80%) can be modified. However, short periods may affect the precision, and long ones may generate mistaken trends. At the same time, higher confidence intervals may exclude participants, and lower ones may accept savings that probably are not.

31 5. Conclusions and Future Work Future work This program should be just the beginning towards a Demand Response (DR) culture, it is important to include residents as well as other mechanisms of DR such as Real time prices and curtailable circuits. This programs are more reliable and assure a higher precision and terms for evolution in the Power Market. Implementation: In order to assure active participation from the final user it is essential to provide the rules and tools to participate in the process. This, may include regulation, laws and technological projects, such as automatic meters implementation and programs available during non-critical periods.

32 Agenda 1. Motivation and objective 2. Problem description 3. Solution 4. Performance Analysis 5. Conclusions and Future Work 6. References

33 References 6. References *1+ UNIVERSIDAD DE LOS ANDES, Asesoría para el diseño de un mecanismo de mercado para la participación y remuneración de la demanda eléctrica desconectable. May, [2] Encinas Redondo; A.Domijan ; Alvares-Bel; Rodriguez-Garcia; Masiel Lo, Settlement computation demand response programs: Comparing Baseline Methods, December 2004 *3+ Montgomery, Douglas C; Jhonson, Lyinwood A; Gardiner, John S, Forecasting and Time Series Analysis, McGraw-Hill Companies; 2nd edition (July 1990) *4+ Carcedo Julián, Universidad Autónoma de Madrid Informática Aplicada al Análisis Económico; Medias Móviles y Alisados, 2004 *5+ Edison Electric Institute, Economic principles of demand response in electricity October, *6+ FERC, Federal Energy Regulatory Commission. Recommended FERC actions to facilitate demand response resource programs. February *7+ Prada Arboleda. Carlos Camilo, Propuesta de implementacion del mecanismo de demanda desconectable voluntaria en el mercado eléctrico colombiano *8+ Edison Electric Institute, The role of demand response in electric power market design October, *9+ IEA DSM Energy Efficiency. Task VIII - Demand-Side Bidding in a Competitive Electricity Market 2003 *10+ Energy and Environments Economic, A survey of Time-of-use (TOU) Pricing and Demand-Response (DR) Programs, July [11] Department of Prime Minister and Cabinet, Government of Australia, *12+ Murphy, Helen, Department of Infrastructure, Victoria Valuing Demand Response, November 2006 *13+ European Transmission System Operators, Demand Response as a resource for the adequacy and operational Reliability of the power systems, January 2007 *14+ RISO National Laboratory, Analyses of Demand Response in Denmark, October [15] Demand Response Resources, May 2007 *16+ Stasko Robert, Energy Conservation, The New Paradigm, Presentation to HRAI Annual Meeting, September [17] IESO, Power to Ontario, On Demand, May *18+ Oland Gunn; Head of Electricity market Sector, Demand Response Resources (IEA), Nordic Regulatory Aspects, April 2005 *20+ IESO, Emergency Load Reduction Program (ELRP), July 2006 [21] Crossley Dr. David, IEADSM TaskXV, Workshop on the future of demand response, Melbourne, 11 November *22+ Comision de Regulación de Energia y Gas (CREG), Reliability Charge, Regulatory Scheme to Guarantee the Reliability in the Supply of Electric Energy in Colombia, A Long-Term Vision. May [23] Lawrence Berkeley National Laboratory (University of California, University of California), Estimating Demand Response Load Impacts: Evaluation of Baseline Load Models for Non-Residential Buildings in California [24] California Energy Commission Consultant, Report Protocol Development for demand response calculation-findings and Recommendations, February 2003

34 Thank you The authors would like to present a special thank to CREG.