Evaluation of the performance of Aggregated Demand Response by the use of Load and Communication Technologies Models

Transcription

1 Institute for Energy Engineering (IIE-UPV) & Research Network REDYD-2050 Curso de formación en Mercados Eléctricos Evaluation of the performance of Aggregated Demand Response by the use of Load and Communication Technologies Models Session 26. Demand Response Paper #85

2 Authors A. Gabaldón (5) ;C. Álvarez (1) ; J.I. Moreno (2) ;G. López (2) ; J. Matanza (3) ; S.Valero (4) and M.C. Ruiz (5) Universities (REDYD2050 team) (1): IIE-UPV; (2) U. Carlos III; (3) IIT-ICAI; (4) UMH; (5) UPCT; (SPAIN) ETSII-UPCT

3 A brief overview of the problem: DR in Ancillary Services (AS) markets

4 DR, but focussed on small/medium segments Why small DR is so important for Markets, Power Systems, and consumers? Five arguments should be considered in the near future: 1) Small segments (residential/commercial) are an important part of total energy consumption ( 40-50%). 2) Residential electricity demand will grow (according to forecasts for the period ) 3) Small end-uses explain a high percent of system's peak-loads. 4) Small DR remains unexplored and/or is considered uncontrollable (it is much more complex than large customers, e.g. aggregation). 5) DR is gaining interest at the political and economic level in EU ( and in other developed countries) European Energy (EU) policy: the thorough inclusion of DR in the European Commission s legislative proposals on Electricity Market Design within the Clean Energy Package, from November 2016 Forecasts for DR savings: 100,000 M/year (EU-28) (speech by Mr. M. Arias Cañete, Climate & Energy Commissioner, EC).

5 But in which market «small DR» is technically more complex? And it is less matture DR participation in AS is still reduced (e.g. Synchronous Reserve) Figure source: PJM, DR activity report, Nov Participation vs. requirements Mainly, for large customers DR Registrations

6 A technical challenge for a multidisciplinary research team, isn t it? Important: to develop new tools yo help increasing and facilitating the DR in markets and, especially, in Ancillary Services (AS) RESPONSE TIME: CONVENTIONAL MODELS (30min-hr) REVENUE:LOAD/ICT PERFORMANCE

7 The proposed methodology Objective: to evaluate the flexibility and performance of small/medium customers demand in AS markets to develop & integrate a simulation tool

8 Making easier the tasks of aggregators while improving customers benefit Properties of the proposed Methodology: Blackbox models The model is universal, i.e. a similar philosophy (base) for all the possibilities of DR (implicit or explicit): Price, Events, Capacity and AS! The same database for model s parameters A similar architecture in load: elemental models & aggregation Holistic approach: Integrates different models (in this ppt) Statistic models: forecasting, price volatility, aggregation Load models: elec. appliances and their environment (e.g. thermal) ICT modeling: performance, architecture, Robustness of the specific model for AS The parameters are «easy» to be evaluated or explained and do not change during different time periods (day, week, season,..) In other approaches (e.g. G parameter ) C dt dt + G( T T ) + P = w* P a disturbance

9 STEP 1: Physical base. Understandable by other models (higher order, e.g. EnergyPlus) A) Elemental models (dwelling): 3 rd - 4 th order, state-space Dwelling characteristics: storage and heat transfer Weather: Solar radiation, external temperature Load/appliance: energy conversion and storage

10 STEP 2: THE STRUCTURE CAN INCLUDE RELEVANT FACTORS FOR EACH MARKET B) Elemental model (appliance): mechanical latency (I) To consider the «lock-out» or mechanical delay in HVAC (a mechanism that prevents a rapid recycling of a compresor, i.e. load damages) It is not relevant for DR in energy markets, but for AS services the «scoring» (i.e. revenue) depends on latency Several changes in thermostat settings (up&down) were recorded in test facilities (residential Heat Pumps) Enabling technology for testing the latency (mechanical) Smart Thermostat Control & Energy meter Cheap, easy and available

11 TESTING RESULTS: Heat Pump (Winter season) B) Elemental model (appliance): latency (II) Latency has been reported, in some papers, for commercial customers (e.g. IEEE Proceedings, Beil, 2016, and applied to AS policies) 1-3kW Heat Pump (3-7kW thermal capacity) have been considered and tested (demand and thermostat consigns monitored every sec by ICT). ON time delay: seconds (it depends on thermostat changes) OFF time delay: seconds (ºC) 4ºC 3ºC 2ºC =1ºC 1ºC

12 STEP 3: Aggregation to reach a minimum load level to participate in AS (>100kW) C) Load Aggregation Drawing together a resource of interest for AS needs Two options (models by REDYD2050) OPTION A: Monte Carlo Solve ~ elemental state-space models (STEP 1 by eq(1)) and then obtain the demand and indoor temperature for each load: i.e. X i (t) and its probability density distribution (pdf) dxi ( t) = Ai Xi ( t) + Bi ( t) + Hch( t) mi ( t) u( t); i = 1, 2, eq( 1) dt A detailed calculus for each load (great precision with a hard simulation process). After simulation, we need to find statistical values of the ensemble OPTION B: Stochastic Differential Partial Equations (SDPE) Solve one (several) SDPE (s) that is (are) representative of the whole aggregation: less accuracy in results but higher computational speed It is a working area for REDYD2050 (to improve the speed of response)

13 STEP 4: Introducing ICT models in individual & aggregate responses C) Integration of ICT modeling OpenADR is assumed as application protocol Two types of nodes: VTN & VEN (Virtual Top and End nodes) Define the architecture of ICT: several possibilities from ISO/TSO to aggregator and from aggregator to consumer and vice-versa. E.g. NB-PLC (Narrowband PLC, close to end-users): availability and latency are key communication parameters SIMULATION TOOLS

14 STEP 4: ICT MODELING Evaluation of ICT performance Performance of OpenADR over NB-PLC infrastructure Metric: the latency under three different types of noise Results Synchronous and backgound noise Asynchronous noise Median from 6.5 to 7.5 s Median around 12s ( ) d X t dt Integration into elemental models: u(t) control policy changes, and load behaviour (through m(t), energy conversion) = A X ( t) + B ( t) + H ( t) m ( t) u( t) ch ch Here you are

15 THE PROCESS: SEARCHING FOR THE APPROPRIATE MIX Results and applications: AS markets Evaluation of response against a test signal Mr. Aggregator AS CUSTOMERS LOADS AGGREGATION ICT (PAPER: SMALL) (STEPS 1&2) (STEP 3) (STEP 4)

16 Aggregation: to reach a minimum load level (>100kW) to overcome market requirements Results: Load Aggregation & Model integration Aggregated Demand (average) (%) Indoor Temperature w/o control (pdf) Controlled Customer databasese Hch; N(m, σ)

17 IMPLEMENTATION OF THERMOSTAT CHANGES THROUGH THE MODEL Follow me, please (I) Integrated model give probability density functions (pdf) f k (X,t) for indoor temperature X(t), i.e. customer comfort f k (X,t) for k=1 (ON) and k=0 (OFF) load states [ ] f k ( x, t ) dx = Pr x X ( t ) x + dx m ( t ) = k ; k = 0, 1 Also, they allow to evaluate cumulative prob. functions F k (X,t) [ ] F ( x, t) = Pr X ( t) x m( t) = k = f ( λ, t) dλ; k = 0, 1 k f k (X,t) x k F k (X,t) Where is the load? E.g. Rated power 1MW How many loads under a temperature?

18 THERMOSTAT CHANGES ACCORDING TO PDF (AIR CONDITIONING LOAD) Follow me, please (II)» 300 kw offered in AS (50% of available HVAC load) LOAD DROP (e.g. 30%) (look at f1, F1) 1,500 1,000 «AS» TEST SIGNAL 0,500 0,000 Series1-0,500 00:00 02:10 04:20 06:30 08:40 10:50 13:00 15:10 17:20 19:30 21:40 23:50 26:00 28:10 30:20 32:30 34:40 36:50 39:00-1,000-1,500

19 THERMOSTAT CHANGES ACCORDING TO PDF (AIR CONDITIONING LOAD) Follow me, please (III) ORIGINAL STATE: IDLE (e.g. 60%) (look at f0, F0) 1,500 1,000 0,500 0,000 Series1-0,500 00:00 02:10 04:20 06:30 08:40 10:50 13:00 15:10 17:20 19:30 21:40 23:50 26:00 28:10 30:20 32:30 34:40 36:50 39:00-1,000-1,500 «AS» TEST SIGNAL Warning: in thermostat does not imply to get the original setpoint (the load is dynamic) Pdf are changing, driven by state-space equations (i.e. models)

20 THERMOSTAT CHANGES ACCORDING TO PDF (AIR CONDITIONING LOAD) Follow me, please (IV) LOAD GROWTH (ej: +20%) (look at f0, F0) 1,500 1,000 «AS» TEST SIGNAL 0,500 0,000 Series1-0,500 00:00 02:10 04:20 06:30 08:40 10:50 13:00 15:10 17:20 19:30 21:40 23:50 26:00 28:10 30:20 32:30 34:40 36:50 39:00-1,000-1,500

21 RESULTS Evaluation of aggregated response Evaluation of «performance scoring» (applied to PJM scoring items) Accuracy: response compared to signal over a five-minute period Performance: delay between control signal and point of highest correlation Precision: the instantaneous error between the control signal and the regulating unit s response In our case revenue drops around 20% (from 100 to 80%)

22 Conclusions The interest for AS markets is continuously growing (i.e. renewables) DR is almost unexplored for a lot of AS Services We propose a methodology to allow DR participation To use the same architecture that can be used for energy markets To improve the speed of response and integrate new models Tuned elemental models Aggregation are revisited and improved for higher order systems Results easy to understand: we use them in other markets ( pdf, demand) Some additional tools are still needed Assisted response to the aggregator : artificial neuronal networks to fit thermostat change, feedback from loads through ICT (NILM) Integration of new models (ICT, appliance, ) To develop procedures to find Average/Representative Loads in the aggregation (monitoring, feedback, ) Gaining momentum in modeling performance (aggregation by SPDE)

23 Thanks so much! REDYD2050 hopes meeting you soon! Questions? (grant ENE REDT) (grants ENE C3-1-P&2-P) Acknowledgment