Open AccessStrategies for combined operation of PV/storage systems integrated into electricity markets
Author(s) -
Carriere Thomas,
Vernay Christophe,
Pitaval Sebastien,
Neirac FrancoisPascal,
Kariniotakis George
Publication year - 2020
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2019.0375
Subject(s) - photovoltaic system , reliability engineering , sizing , probabilistic logic , intermittency , computer science , electricity , production (economics) , electricity market , investment (military) , operations research , engineering , economics , electrical engineering , art , turbulence , physics , macroeconomics , artificial intelligence , visual arts , thermodynamics , politics , law , political science
The increasing share of photovoltaic (PV) power in the global energy mix presents a great challenge to power grid operators. In particular, PV power's intermittency can lead to mismatches between energy production and expectation. Battery energy storage systems (BESSs) are often put forward as a good technological solution to these problems, as they can mitigate forecast errors. However, the investment cost of such systems is high, which questions the benefits of using these systems in operational contexts. In this work, we compare several strategies to manage a PV power plant coupled with a BESS in a market environment. They are obtained by stochastic optimisation using a model predictive control approach. This study proposes an approach that takes into account the aging of the BESS, both at the day‐ahead level and in the real‐time control of the BESS, by modelling the cost associated with BESS usage. As a result, the BESS arbitrates between compensating forecast errors and preserving its own life expectancy, based on both PV production and price scenarios derived from probabilistic forecasts. A sensitivity analysis is also carried out to provide guidelines on the optimal sizing of the BESS capacity, depending on market characteristics and BESS prospective costs.