Abstract
Recently, the integration of optimal battery dispatch and demand response has received much attention in improving DC microgrid operation under uncertainties in the grid-connect condition and distributed generations. However, the majority of prior studies on demand response considered the characteristics of global frequency variable instead of the local voltage for adjusting loads, which has led to obstacles in operating DC microgrids in the context of increasingly rising power electronic loads. Moreover, the consideration of voltage-dependent demand response and optimal battery dispatch has posed challenges for the traditional planning methods, such as stochastic programming, because of nonlinear constraints. Considering these facts, this paper proposes a model predictive control-based integrated voltage-based demand response and batteries’ optimal dispatch operation for minimizing the entire DC microgrid’s operating cost. In the proposed model predictive control approach, the binary decisions about voltage-dependent demand response and charging or discharging status of storage batteries are determined using a deep-Q network-based reinforcement learning method to handle uncertainties in various operating conditions (e.g., AC grid-connect faults and DC sources variations). It also helps to improve the DC microgrid operation efficiency in the two aspects: continuously avoiding load shedding or shifting and reducing the batteries’ charge and discharge cycles to prolong their service life. Finally, the proposed method is validated by comparing to the stochastic programming-based model predictive control method. Simulation results show that the proposed method obtains convergence with approximately 41.95% smaller operating cost than the stochastic optimization-based model predictive control method.
Highlights
IntroductionThe increasing intensity of extreme weather events, such as heat waves and large storms, has led to uncertain grid-connect conditions and affected DC microgrids (DCMs)’
It consists of four photovoltaic systems at buses 1, 13, 16, and 19, two wind turbine systems at buses 5 and 28, and two SB systems installed at buses 8 and 25
The proposed model and algorithm are simulated through a typical DC microgrids (DCMs) structure in commercial buildings, where there is a significant increase in DC loads
Summary
The increasing intensity of extreme weather events, such as heat waves and large storms, has led to uncertain grid-connect conditions and affected DCMs’. Texas, USA, faced a historic winter storm in 2021, which made the power system impassable and left millions without access to electricity from hours to days [5]. These facts have promoted the advanced control and planning methods development in DCMs subject to uncertainties and disruptions. DCM refers to power clusters in a distribution network that comprises DC loads, RG units, and SBs, which can operate in either grid-connected or island mode upon loss published maps and institutional affiliations.
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