Akkuvarastojen monitavoitteinen rooli energiajärjestelmässä

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2 Akkuvarastojen monitavoitteinen rooli energiajärjestelmässä Ville Tikka LUT 2

3 LUT research group and research areas Project management Ville Tikka, Jukka Lassila Tariffs and grid supporting applications Jouni Haapaniemi, Juha Haakana Optimization and market applications Nadezda Belonogova, Arun Narayanan Hardware and storage control, field tests Andrey Lana, Ville Tikka Supervising professors Jarmo Partanen, Samuli Honkapuro 3

4 Partners and funding Partners and funders: Sähkötutkimuspooli, STEK, Helen, Helen Electricity Network, Fingrid, Landis+Gyr, LUT Budget and schedule: 140 k, 12/ /2017 Sähkötutkimuspooli 4

5 Motivation EC currently encourages 1) the increase of efficiency, flexibility, safety, and power quality in distribution grids and 2) to fully exploit potential advantages from RES, DG, DR, and Energy Storage Systems Stationary and mobile BESS play a significant role in modern energy systems Multi-objective operation of distributed BESS could lead to lower socioeconomic costs, but might also cause conflicts of interest 5

6 Battery Cost Forecasts Nykvist, Björn, and Måns Nilsson. "Rapidly falling costs of battery packs for electric vehicles." Nature Climate Change 5.4 (2015):

7 The main research questions I. Implementation of control system? II. How to optimize the stakeholder -specific utilization of an individual BESS for different purposes (e.g. peak-cutting, control of frequency and voltage, optimization of reactive power balance, electricity trade in day-ahead, intraday, and ancillary markets, back-up power for end-user / network, etc.)? III. How to optimize the operation of a system with multiple battery energy storage systems with different sizes, locations, and owners? 7

8 Present project and further questions Implementation 8

9 Test sites, (build on earlier projects) Suvilahti, 2016 (Helen) Green Campus, 2016 LUT LVDC microgrid, 2014 (Suur-Savon Sähko/LUT) V2G hybrid, 2014 LUT 600 kwh, 1.2 MW ~ LTO Li-ion cells 132 kwh, 188 kw 230 pcs LiFePO 4 2x30 kwh, 2x30 kw 2x235 pcs LiFePO 4 1.3kWh, 27 kw (NiMH) kwh, 3 kw (LiFePO 4 ) 9

10 Test sites, (build on earlier projects) Centralized energy storage (primary substation) Helen Suvilahti Centralized energy storage (secondary substation) LUT Green Campus Distributed energy storage (customers) LVDC Suomenniemi Mobile energy storage (customers) LUT Green Campus 10

11 Master unit LVDC Suomenniemi LUT Storage & EV charging Helen Suvilahti 11

12 Suomenniemi LVDC research site 12

13 LUT GreenCampus BESS 13

14 EV smart charging IEC Master ENSTO ECV 100 Ethernet Interface unit Ensto, RS485 IEC62196-X, mode 3 Smart charging based on local logic on interface unit FCR, power band, peak shaving, etc. 14

15 EV smart charging example 15

16 Helen Suvilahti Helsinki Suvilahti Connected to 10 kv distribution grid Commercial storage utilized in research 16

17 f Multi-objective control concept for distributed BESS P, Q Local control algorithms Data resources f, U 1 sec, 15 min P, Q f, U P, Q Local control algorithms Local control algorithms 1 sec, 15 min SOC, E, Ah, Restrictions Central control algorithms Task 1: kwh, kw, Task 2: kwh, kw, f, U P, Q Local control algorithms 1 sec, 15 min P ref, E ref / Parameters of the task P +P max -f db -fmax f, U db +fmax +f Day, 1h, 15 min Task 3: kwh, kw, 1 sec, 15 min -P max 17

18 Control system communication Multi-objective control concept for distributed BESS (generic control architecture) Control system level Control system tasks Input data for control Centralized, data center / cloud computing Decisions for next n hours Data resources Electricity markets Weather forecasts Weather observations Etc. Distributed on site Centralized control plan execution Data resources Local grid status Other locally observed variables Hardware control on site Hardware control, U, I, P, f, etc. Data resources Grid frequency U, I, P 18

19 Simulation tool: basic structure Input Input Input Logics D Logics H Logics S Final results Day-resolution Hour-resolution Second-resolution Year-resolution = techno-economical results Tuning input parameters; Developing optimal bidding and operational strategy Annual revenues Operational cost Profits Benefit-cost ratio Return on investment Strategy to operate 19

20 Relationship between local and system tasks 20

21 Conflict of objectives 21

22 Monitoring interface example (campus) 22

23 Monitoring interface example (LVDC site) 23

24 The project outcomes I. Technical implementation of the control system I. Fully functioning control architecture II. Decision making algorithms based on real-time data III. Visualization platform, etc. II. Simulation tools developed I. Enables simulation of different scenarios II. Based on actual power system and market data III. Algorithm to analyze conflict of objectives 24

25 Thank you! Questions? Contact: Ville Tikka 25

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