TY - JOUR
T1 - Risk-Constrained Stochastic Scheduling of a Grid-Connected Hybrid Microgrid with Variable Wind Power Generation
AU - Vahedipour-Dahraie, Mostafa
AU - Rashidizadeh-Kermani, Homa
AU - Anvari-Moghaddam, Amjad
PY - 2019/5
Y1 - 2019/5
N2 - This paper presents a risk-constrained scheduling optimization model for a grid-connected hybrid microgrid including demand response (DR), electric vehicles (EVs), variable wind power generation and dispatchable generation units. The proposed model determines optimal scheduling of dispatchable units, interactions with the main grid as well as adjustable responsive loads and EVs demand to maximize the expected microgrid operator’s profit under different scenarios. The uncertainties of day-ahead (DA) market prices, wind power production and demands of customers and EVs are considered in this study. To address these uncertainties, conditional value-at-risk (CVaR) as a risk measurement tool is added to the optimization model to evaluate the risk of profit loss and to indicate decision attitudes in different conditions. The proposed method is finally applied to a typical hybrid microgrid with flexible demand-side resources and its applicability and effectives are verified over different working conditions with uncertainties.
AB - This paper presents a risk-constrained scheduling optimization model for a grid-connected hybrid microgrid including demand response (DR), electric vehicles (EVs), variable wind power generation and dispatchable generation units. The proposed model determines optimal scheduling of dispatchable units, interactions with the main grid as well as adjustable responsive loads and EVs demand to maximize the expected microgrid operator’s profit under different scenarios. The uncertainties of day-ahead (DA) market prices, wind power production and demands of customers and EVs are considered in this study. To address these uncertainties, conditional value-at-risk (CVaR) as a risk measurement tool is added to the optimization model to evaluate the risk of profit loss and to indicate decision attitudes in different conditions. The proposed method is finally applied to a typical hybrid microgrid with flexible demand-side resources and its applicability and effectives are verified over different working conditions with uncertainties.
KW - Demand response
KW - Conditional value at risk
KW - Hybrid microgrid
KW - Electric vehicle
KW - Wind power generation
KW - Conditional value at risk (CVaR)
KW - Electric vehicle (EV)
KW - Demand response (DR)
UR - http://www.scopus.com/inward/record.url?scp=85069644120&partnerID=8YFLogxK
U2 - 10.3390/electronics8050577
DO - 10.3390/electronics8050577
M3 - Journal article
SN - 2079-9292
VL - 8
JO - Electronics
JF - Electronics
IS - 5
M1 - 577
ER -