A Two-stage Operating Strategy of Microgrids With Consideration of Uncertainty For Participating in Power Market

Thumbnail Image
Files
CIRED 2018 Ljubljana WS - 0263 - 20897.pdf (426.53 KB)
Paper number
0263
Working Group Number
Conference name
CIRED 2018 Ljubljana Workshop
Conference date
7 - 8 June 2018
Conference location
Ljubljana, Slovenia
Peer-reviewed
Yes
Short title
Convener
Authors
GAN, Lin, China Southern Power Grid, Guangzhou Power Supply CO., Ltd, China
Mo, Wenxiong, China Southern Power Grid, Guangzhou Power Supply CO., Ltd, China
Zhang, Zichong, China Southern Power Grid, Guangzhou Power Supply CO., Ltd, China
WU, GuoPei, China Southern Power Grid, Guangzhou Power Supply CO., Ltd, China
LUO, LinHuan, China Southern Power Grid, Guangzhou Power Supply CO., Ltd, China
Zhang, Hang, China Southern Power Grid, Guangzhou Power Supply CO., Ltd, China
Chen, Dapeng, South China University of Technology, China
He, Hao, China Southern Power Grid, Guangzhou Power Supply CO., Ltd, China
Abstract
In this paper, a two-stage strategy is proposed for themicrogrid to optimize the energy bids for the day-ahead(DA) and real-time (RT) balancing market, respectively.The uncertainty of the energy prices in DA and RTmarket, the output power of renewable energy resourcesand the load demand are considered and modeled inthis paper. In the first stage, the microgridsimultaneously optimizes the energy bids in both the DAand RT market. After the DA market clearing, thecleared energy quantity and energy price are known.The microgrid re-optimizes the bids for RT marketduring the DA rebidding period. Comprehensive casestudies based on the codes and data of PJM powermarket show that the proposed strategy can minimizethe total cost of the microgrid.NOMENCLATUREp The probability of scenario p The probability of scenario ,,, ptiC The operating cost of unit i in scenariop during period t,,, ptiP The output power of unit i in scenario during period t,,tisBinary variable. 1 if unit i is scheduled onin scenario p  during period t and 0otherwise.upptiC ,,,The startup cost of unit i in scenario pduring period tbesptC ,,The charging/discharging cost of thebattery energy storage in scenario p during period tbesptP ,,The charging/discharging power of thebattery energy storage in scenario p during period tLptC ,,The cost of load curtailment in scenariop during period tlcptP ,,The load curtailed in scenario p duringperiod tdaptP , The bid power in the DA energy market inscenario prtptP , The bid power in the RT energy market inscenario pt Time intervaldaptp , The energy price in the DA market duringperiod t in scenario prtptp , The energy price in the RT market duringperiod t in scenario ploadptP ,,The forecast load demand in scenario pduring period twptP ,,The forecast output power of wind turbinein scenario p during period tpptP ,,The forecast output power of photovoltaicarrays in scenario p during period tmaxmin / ii PPThe minimum/maximum output power ofof unit idiui rr /The ramping up and ramping down limitof unit i during period tmaxmax / dcch rrThe maximum/minimum charging/discharging power of the battery energystoragebesptE ,,The energy content of the battery energystorage in scenario p during period tbesbes EE minmax / The maximum/minimum energy contentof the battery energy storagedcch  / The charging/discharging efficiency of thebattery energy storagemaxL The maximum load curtailmentmaxgridP The maximum exchanging power betweenmicrogrid and the upstream grid1.
Table of content
Keywords
Publisher
AIM
Date
2018-06-07
Published in
CIRED 2018 Ljubljana Workshop on Microgrids and Local Energy Communities
Permanent link to this record
https://www.cired-repository.org/handle/20.500.12455/1261
http://dx.doi.org/10.34890/439
ISSN
2032-9628
ISBN
978-2-9602415-1-8