TY - JOUR
T1 - A Quadratic Programming-Based Power Dispatch Method for a DC-Microgrid
AU - Yoon, Changwoo
AU - Park, Yongjun
AU - Sim, Min Kyu
AU - Lee, Young Il
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - This Paper deals with the optimum energy management of Microgrid (MG) having Energy-Storage System(ESS)s. Recently, the importance of retaining the profits of MG owners and the needs of providing additional requirements to the electric grid are rising. To accommodate these needs systematically, the Quadratic Programming (QP), one of the simplest and effective optimization method, is gaining attention. The QP has been used for similar cases before, but unlike the known advantages of early QP studies, some of the subsequent papers have been conducted in an inappropriate direction and may be overshadowed. Therefore in this paper, an extended and more practical QP cost function considering the realistic operating conditions is proposed, and the advantages of the original methods are revisited with comparisons. As a result, the proposed method retains the genuine features of QP, such as peak power shaving and assuring the power reserve rate, and can be simply extended to include Electric Vehicle (EV)s into the optimization. Additionally, the practical issues of implementing the QP in real-time have been discussed and resulted in both improved optimization speed by 58% using the cost function reformulation and the robustness with the forecast mismatching.
AB - This Paper deals with the optimum energy management of Microgrid (MG) having Energy-Storage System(ESS)s. Recently, the importance of retaining the profits of MG owners and the needs of providing additional requirements to the electric grid are rising. To accommodate these needs systematically, the Quadratic Programming (QP), one of the simplest and effective optimization method, is gaining attention. The QP has been used for similar cases before, but unlike the known advantages of early QP studies, some of the subsequent papers have been conducted in an inappropriate direction and may be overshadowed. Therefore in this paper, an extended and more practical QP cost function considering the realistic operating conditions is proposed, and the advantages of the original methods are revisited with comparisons. As a result, the proposed method retains the genuine features of QP, such as peak power shaving and assuring the power reserve rate, and can be simply extended to include Electric Vehicle (EV)s into the optimization. Additionally, the practical issues of implementing the QP in real-time have been discussed and resulted in both improved optimization speed by 58% using the cost function reformulation and the robustness with the forecast mismatching.
KW - charging
KW - electric vehicle
KW - energy storage system
KW - microgrid
KW - optimization
KW - photovoltaic
KW - Quadratic programming
KW - real-time simulation
UR - http://www.scopus.com/inward/record.url?scp=85096832522&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3039237
DO - 10.1109/ACCESS.2020.3039237
M3 - Article
AN - SCOPUS:85096832522
SN - 2169-3536
VL - 8
SP - 211924
EP - 211936
JO - IEEE Access
JF - IEEE Access
M1 - 9264160
ER -