Abstract
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.
Highlights
Power reference for ith Electric Vehicle (EV) charging(+)/ discharging(-) at time t Power from the AC grid to DCMG at time t The load connected to the DCMG at time t Battery SoC of ESS at time t EV SoC at time t BSoC at one step before the time t Time step for receding horizon Maximum power limit of PGRID Minimum power limit of PGRID Charging power limit for ESS Discharging power limit for ESS Charging power limit for an EV Discharging power limit for an EV Charging power limit for ith EV Upper bound for BESS battery SoC Lower bound for BESS battery SoC
The inherent intermittence of renewable energy sources such as PV and wind has increased the use of energy storage devices, which has led to the rapid growth of the batteryrelated industry
P, is reached its maximum power but decreased quickly, and it is not charging from 15:00~16:00 even though it’s SoC is not reached 100% and charged long time after. This behavior can be explained that the Quadratic Programming (QP) prioritizes the DCMG optimization goal than the EV #2 charging request
Summary
BSoC(t) BEV(t) BSoC(t − 1) ΔTs PGRID,Max PGRID,Min PESS,Max PESS,Min PEV,Max PEV,Min PEV,Max,i BSoC,Max BSoC,Min. Power reference for ith EV charging(+)/ discharging(-) at time t Power from the AC grid to DCMG at time t The load connected to the DCMG at time t Battery SoC of ESS at time t EV SoC at time t BSoC at one step before the time t Time step for receding horizon Maximum power limit of PGRID (kW) Minimum power limit of PGRID (kW) Charging power limit for ESS (kW) Discharging power limit for ESS (kW) Charging power limit for an EV (kW) Discharging power limit for an EV (kW) Charging power limit for ith EV (kW) Upper bound for BESS battery SoC Lower bound for BESS battery SoC. BS∗oC(t) BSoC(24: 00) BSoC(0) x Aieq bieq Aeq bieq lb ub H f □T N PG(t) PN(t) PL(t) PEV,i(t) PE∗ V,i(t) dN(t) BN(t) BN∗ (t) cG(t)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
