DER Integration and Market issues: The MIRABEL Project Dr. Evangelos Rikos

Transcription

1 Conference: Experimental research and DER integration in the EU Energy System Milano, October 10 th 2013 c/o RSE DER Integration and Market issues: The MIRABEL Project Dr. Evangelos Rikos CRES-dept. of PVs and Distributed Generation tel.: , fax:

2 The MIRABEL Project Project full title: Micro-Request-Based Aggregation, Forecasting and Scheduling of Energy Demand, Supply and Distribution Funding Scheme: FP7 ICT Grant Agreement number: Period covered: from to Involved Partners: SAP AG(coordinator), EnBW, TUD (Germany), AAU (Denmark), CRES (Greece), INEA, JSI (Slovenia), TNO (the Netherlands), AFNOR (France) Project website address: 2

3 Project Objectives 1. Role and process model 2. Flex-offers (FO) concept 3. Distributed, decentralised and scalable infrastructure 4. Real-world trials 5. Contribution to standardization 3

4 MIRABEL: Role and process model The Energy Data Management System has to be applicable to the future deregulated energy sector Suitable for largest possible future energy market The basis is the Harmonized model of the Electricity market in Europe, jointly proposed by ENTSO-E The electrical energy market system can be decomposed into smaller nested primary subsystems Active components are named as roles : carry out processes, constitute domains with boundary conditions Definition and analyses of the use cases 4

5 MIRABEL: Role and process model The most important processes that have been implemented include: flex-offer generation, acceptance, rejection and assignment energy forecasting (consumption, production, imbalances) aggregation, scheduling, disaggregation (flexibilities in) energy trading The following primary subsystems were analysed: balance group subsystem, as use case market balance area subsystem, as use case market area subsystem; only to the extent that these roles participate in the other two primary subsystems. 5

6 MIRABEL: Role and process model Balance Group Use Case: Household community and LDE trial Market Area Use case TSO trial BRP: assigned to any real electricity market and grid structures, by electricity traders (brokers), electricity suppliers or even (LDEs) in order to balance their electricity grid area of responsibility 6

7 The Flex-offer concept FO is used to make flexibility explicit and allow for the trade of it Involved roles: Prosumers (providers) BRP (Acquirer) pool flexibility in their portfolio and use it to compensate for the intermittent operation of supply/demand Business interaction: providing FOs in terms of power and time acceptance by the BRP of attractive offers assignment, specification of how flexibility is to be exercised (e.g. operation schedules), resulting in a better balance between supply and demand 7

8 The Flex-offer concept FO life-cycle phases A framework allowing prosumers and BRPs to negotiate the price of demand and supply flexibility automatically FO parameters 8

9 Mirabel Components-Aggregation Technically not feasible to schedule millions of micro flex-offers Aggregation component performing also disaggregation f1 f 2 f 3 f 4 f 5 9

10 Objectives: Mirabel Components-Forecasting Transparent forecast model creation and usage and Transparent forecast model update and maintenance EGRV-Model: multi-equation energy demand forecast model that uses an individual model for each intra-day period and HWT-Model: energy specific adaptation of the general purpose Holt-Winters exponential smoothing forecast model 10

11 Mirabel Components-Scheduling Invoked each time there is a significant change in the forecasts or in the pool of aggregated flexoffers Best schedule for the given aggregated flex-offers by taking into account the forecast energy production and consumption and the possibility of selling energy to (and buying energy from) the market (other BRPs) Randomized greedy search and evolutionary algorithm 11

12 Mirabel Solution overview Flex-Offers: allow prosumers to automatically indicate their flexibility to their BRP. Aggregation: groups similar Flex- Offers to manage the large amount of data in a distributed hierarchical EDMS. Forecasting: predicts demand and supply of RES energies for the upcoming hours and days. Scheduling: computes optimal plan to dispatch flexible energy demand and supply to minimize costs for the BRP. Negotiation: finds the optimal price in real-time that maximizes the financial benefit for the customer and the profit for the BRP. 12

13 Real-world trials Trial case: Community of 4 prosumers Used components: Flex-offer generator Forecasting of PVs and non-flexible consumption Scheduling of flexible loads and production Use of storage units: Normal storage units with high flexibilities Additional PV units controlled by a signal equal to the real PV power 13

14 Real-world trials: Input data Historical consumption data coming from real residential consumers measured in Meregio project. The data are appropriately scaled down to the size of the CRES microgrid Capacity price data Capacity [kw] Duration ratio Energy produced One year cost Production price 2,00 0, , ,75 0,13 2,00 0, ,00 661,95 0,25 1,00 0, ,00 243,98 0,56 1,00 0, ,00 217,88 1,25 1,00 0, ,50 204,83 4,71 1,00 0, ,40 202,22 11,62 1,00 0,0010 8,70 201,35 23,14 1,00 0,0005 4,35 200,91 46,19 10, , ,85 14

15 Real-world trials: Test results Scenario: flex-ratio=10%, RES=3kWp Imbalance Achievements: -Average imbalances are eliminated leading to significant cost reduction -Good forecasting performance Short-term imbalances in the scale of 15min are not eliminated completely due to the: -Granularity of flexibility does not allow very short-term balancing -Volatility of Solar production which is prone to fast solar irradiance changes Forecasted Solar 15

16 Real-world trials: Test results Average load duration curve for three different flexibility ratios: 6, 10 and 15% Achievement: Reduction of peak loads by 50% load Increase of base load Flattening of overall consumption profile 16

17 Real-world trials: Test results Scenario: flex-ratio=6%, RES=3kWp, Storage Test goal: To show that MIRABEL can be used in order to exploit effectively the storage unit capabilities in order to provide balances quarterly Achievements: -By maintaining priority in using consumption and production flexibility, MIRABEL achieves to eliminate imbalances in short-term by using storage flexibility -Scheduling of production is cost effective: base(low-cost) production is mostly used. Consumption flexibility is scheduled mostly at the appropriate intervals (with excess of production) Imbalance Scheduling 17

18 Real-world trials: Test results CO2 emissions of the diesel genset for Community of consumers/lde scenario No Storage Storage Cases 1-6 refer to different flex-ratio (6, 10, 15%) and RES penetration (3 or 6kWp) Achievement: The emissions of the diesel genset are reduced to about 50% due to the good exploitation of the storage capabilities by MIRABEL kgrco2/kwh 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,

19 MIRABEL achievements overview Flexible demand and supply can lead to 7-13% cost reduction for BRPs. Peak-load reduction by 13-50% and an increase of the base-load in general improve the overall efficiency of the system. Flexible demand and supply improves the integration of renewable energy sources significantly: 70% of the negative impact caused by fluctuating energy supply of renewables like wind can be neutralized given that 15% of the energy consumption was intelligently controlled by the BRP. Households can reduce their energy bill by 10-20%. In combination with energy storages flexible demand and supply can lead to up to 50% reduction of CO2 emissions. 19

20 Thank you for your attention! Questions?