Gestion et planification dans le contexte des smartgrids

Transcription

1 Journée Futuring Cities, «Ville et Énergie Durable», Institut Mines-Télécom, 7 octobre 2014, Paris Gestion et planification dans le contexte des smartgrids Georges Kariniotakis HdR, Responsable groupe Energies Renouvelables et Smartgrids. MINES ParisTech, Centre PERSEE georges.kariniotakis@mines-paristech.fr FUTURING CITIES 1

2 MINES ParisTech > Centre PERSEE PERSEE: Centre for Processes, Renewable Energies and Energy Systems. Renewable Energies & Smartgrids. Sustainable technologies & processes Materials for energy MINES Sophia Antipolis 2

3 MINES ParisTech > Centre PERSEE PERSEE: Centre for Processes, Renewable Energies and Energy Systems. Renewable Energies & Smartgrids. Sustainable technologies & processes Materials for energy Renewable Energies & Smartgrids. Forecasting RES generation, Integration of RES in power systems Management & planning of power systems with renewables, Integration of RES into markets, 3

4 The challenges The future electricity grids will be characterized by a growing integration of distributed generation (DG) and renewable energy sources (RES) and by the necessity to foster demand side management within an open electricity market. Power/Pnominal [p.u.] Time [x 1 hour] Examples of PV and wind farm production targets for EU for wind and solar energy : 230 GW for wind energy. Capable of covering 14-18% of the EU-27 electricity demand (109 GW in 2012) [EWEA]. 150 GWp for PV [EREC] (350 GWp [EPIA]). 4

5 The challenges The future electricity grids will be characterized by a growing integration of distributed generation (DG) and renewable energy sources (RES) and by the necessity to foster demand side management within an open electricity market. In this context, the massive deployment of RES and DG units implies important scientific and technological challenges regarding : The evolution towards new configurations or even new concepts for the grid, The security of the grid, The management of the supplied energy, The definition of new planning tools for such infrastructures. 5

6 Management and planning of power systems Management refers mainly to decisions made in times scales ranging from real-time to few days up to months ahead. Scheduling of a power system (economic dispatch, unit commitment) Congestions management Reserves setting Renewables/storage coordination Demand side management (active demand) Maintenance planning. Trading in electricity markets. Planning refers to decisions made considering longer multi-annual time scales Investments on renewable power plants Repowering of wind farms Storage deployment Electricity grid expansion Evaluation of demand evolution. 6

7 Research challenges The development of new management and planning tools for the future power systems involves several challenges: Modelling of a more and more complex power system: Emergence of multiple actors (deregulation) with different business models Deployment of distributed generation and storage at different levels including at the distribution level (was not designed for that) Need to model down to the «last mile» the clients behavior when active demand is present. Multiplication of information sources (i.e. smart meters) Decision making under increased level of uncertainties: Fluctuating renewable energy generation (weather dependence) Variability of demand at distribution level (feeder, client) Active demand alters regular consumption patterns Evolution of cost of new technologies (i.e. storage) Evolution of electricity prices Climatic change and extremes affect risk of investments.... Necessity for probabilistic methods and tools for decision making 7

8 Research highlights The Centre PERSEE develops research to address the above challenges related to the integration of RES and DG into power systems and markets: Since late '80s +60 R&D projects including some pioneer European ones (Dispower, Microgrids, More-Microgrids, Anemos, Grid4EU ) Approach: Modeling, simulation, optimisation. Experimentations: software prototypes démonstration, test beds Selected higlights: RES forecasting DLR forecasting Microgrid management Network Batteries Managemement Bottom-up demand simulation Power system Resource Electricity markets 8

9 Highlights: RES Forecasting Short-term forecasting at different temporal (0h-10 days ahead) and spatial scales is of primary importance for optimising power system management. Forecasts are required for : Electricity demand (from client to regional/national scale). Renewable generation (PV, solar thermal, wind, waves, hydro..) Electricity prices Dynamic line rating PERSEE has developped a leadership in the field of wind power forecasting: Research since 1990 Several PhDs Coordination of the 3 major EU projects in the period : Anemos (FP5), Anemos.plus (FP6), SafeWind (FP7) The following timeline points out the contributions to the state of the art: 9

10 Highlights: RES Forecasting "Deterministic" (spot) approaches Statistical/time-series approaches Artificial intelligence Physical modelling Empiric/hybrid implementations into operational forecast tools Source: Ref [1] 10

11 Highlights: RES Forecasting 1990 Mapping of state of the art 1st benchmarking (Anemos competition) Physical modelling Statistical models, AI, Data mining, Combination of models First probabilistic approaches/ensembles Upscaling Evaluation standardisation/protocol International collaboration 2002 Anemos Probabilistic view Source: Ref [1] 11

12 Highlights: RES Forecasting THE STATE OF THE ART "Deterministic" (spot) approaches New generation of tools Diversified predicted information Portfolio of products Anemos 2008 Anemos.plus, SafeWind Anemos/SafeWind Consortia: 15Mio, 250 publications, commercial products Probabilistic view Source: Ref [1] 12

13 Highlights: RES Forecasting Variability/texture predictions Predictability maps THE STATE OF THE ART Massive data big data Alarming tools for large errors Weather patterns analysis PV forercasting Probabilistic Forecasting Scenarios, Ensembles Risk indices Ramp forecasting Spatio temp Source: Ref [1] 13

14 Highlights: RES Forecasting THE STATE OF THE ART "Deterministic" (spot) approaches New generation of tools Anemos 2008 SafeWind 2014 On-going R&D Probabilistic view Source: Ref [1] 14

15 Highlights: Forecasting of Dynamic Line Rating (DLR) Exemple of weather-based DLR 15

16 Highlights: Management of Microgrids Development of scheduling approaches taking into account Generation mix with renewable units and storage Presence of controllable loads (active demand for load shaving and shifting) Presence of electricity prices signal (market context) Uncertainties of renewable production, load and electricity prices forecasts. Scheduling based on stochastic optimization Methods based on stochastic dynamic programming when storage is present (temporal dependence on the decisions). Coupled with algorithm that optimizes short-term decisions about operation of DG units and storage. New book [2] Ref: PhD thesis Luis Costa, MINES ParisTech 16

17 Highlights: Management of Virtual Power Plants Increase controlability of a generation mix including renewables Strategic bidding in electricity markets (day ahead, intraday) o Reduce penalties due to RES variability when participating in markets Approach Consider risk associated to power forecast uncertainty and market prices. Stochastic optimization approaches. + Storage - Advanced forecasts & uncertainty estimation of RES Coupling with storage Coupling with dispatchable generation Aggregation of RES Participation in intraday markets Physical hedging Financial hedging Ref: PhD thesis Franck Bourry, MINES ParisTech 17

18 Highlights: Management of Virtual Power Plants Increase controlability of a generation mix including renewables Strategic bidding in electricity markets (day ahead, intraday) o Reduce penalties due to RES variability when participating in markets Increase RES controllability 18

19 Highlights: Management of Network Batteries Development of a network batteries aggregator - NBA to provide flexibilities to a DSO An internal market-based approach implemented to manage a microgrid The NBA provides offers for the storage flexibilities to the Network Energy Manager. As a function of the retained offers, the NBA manages the batteries in real time. Application/demo at the Nice Grid demonstrator for the management of grid batteries of 30 kw to 1 MW. Power (kw) Output Power CL Min CL Max Batt Min Batt Max Batt Target Flexibility Offer 33% Flexibility Offer 66% Flexibility Offer 100% SOC (%) Output SOC 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% SOC Min SOC Max SOC Target Batt soc 33% Batt soc 66% Batt soc 100% 19

20 Highlights: Assessment of large-scale DG & storage integration Methodology to assess benefits through coordinated economic dispatch & distribution optimal power flow Horizon 1-5 years. Separation of short-term (hourly step) and long term (weekly step) Long term: hydro mgmt, nuclear maintenance, seasonal storage Short-term: power variations, start/stop process of thermal plants, storage mgmt. Coordination of distribution and transmission grid. Ref: PhD thesis Seddik Abdelouadoud, MINES ParisTech 20

21 Highlights: Assessment of large-scale DG & storage integration Objective: evaluate where and how much DG & storage to install in the distribution network * in order to maintain quality of supply at distribution grid. * optimal coordination with transmission grid. Acceptable voltage limits Example: control of active/reactive power injection to maintain voltage limits, min losses etc. 21

22 Highlights: Bottom-up simulation of demand Objective: Simulate the evolution of the demand for inclusion in the planning of power systems. Challenging due to the new context of smartgrids Evolution of consumption profiles (incl; active demand) Exploitation of massive available data: Température (MERRA, NASA) Irradiation solaire (SoDa) Buildings (BDTOPO, IGN) Households (IRIS, INSEE) Consumption and clients data Exemple de tracé de mesure de départ de la ville d Antibes (06), ramené par client Exemple de carte d irradiation faite par le centre OIE avec les données SoDa Exemple graphique de données bâtiments BDTOPO Exemple de découpage de ville à la maille IRIS 22

23 Highlights: Bottom-up simulation of demand Simulation of the operation of consumption devices. Global load curve, per appliance, per customers, at different time steps Example of a load curve simulated of a futurist district (2030): 500 apartments, 6000m² tertiary, 1 charging station of 25 terminals Example of a load curve simulated for a residential customer Example: Results produced with ERDF for the EU project TRANSFORM (case: Lyon). 23

24 Conclusions - perspectives Evolution to a more and more weather-dependent power system R&D efforts at the Centre PERSEE focus among others on : improving RES predictability; development of efficient probabilistic methods for power system management & planning; exploiting the potential offered by the increasing amounts of available information (big data); It is recognised today the necessity to develop stronger synergies between research in the energy and the ICT layers that compose smartgrids. Beyond the smartgrid paradigm, it will be necessary to evolve towards an integrated approach for managing the different energy systems (electrical grid, gas network, transportation networks, etc). 24

25 References: [1] George Kariniotakis. The present and the future of wind power forecasting, Proceedings of the EWEA 2014, European Wind Energy Association annual event, March 2014, Barcelona, Spain. [2] Merci de votre attention For using material from this presentation please contact the author 25