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[en] Highlights: • In this study, RDX is dried in the ranges of 60–90 °C under atmospheric pressure and vacuum conditions. • Ten models are used to describe the drying of RDX. • The Midilli–Kucuk model is determined as the most suitable model. • Effective moisture diffusivity and activation energy for drying process are determined. - Abstract: The drying characteristics of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) are investigated in the ranges of 60–90 °C of drying temperature under atmospheric pressure and vacuum conditions in a laboratory scale dryer. The effect of drying temperature and absolute pressure on the drying characteristics is determined. In order to estimate and select the suitable form of RDX drying curves, the curves are fitted to ten different semi-theoretical and/or empirical thin-layer drying models and coefficients are evaluated by non-linear regression analysis. The models are compared based on their coefficient of determination, such as mean bias error, root mean square error, reduced chi-square and modeling efficiency between experimental and predicted moisture ratios. It is deduced that Midilli–Kucuk model has shown a better fit to the experimental drying data as compared to other models. A diffusion model is used to describe the moisture transfer and the effective diffusivity for RDX drying is also determined at each temperature. Beside, the activation energy is also expressed using Arrhenius-type relationship under atmospheric pressure and vacuum conditions
[en] Highlights: • A stochastic day-ahead scheduling model with CCPPs and CDR is proposed. • Operation models of CCPPs and CDR are presented. • Flexible operation mechanism of CCPPs and CDR is analyzed. • The random forecasting errors of hourly wind power output and loads are considered. - Abstract: Global warming caused by excessive CO2 emissions has make it urgent to develop low-carbon economy. The carbon capture system is effective to reduce the carbon footprint of coal-fired power plants. Meanwhile, using more renewable energies such as wind power will require less generations from traditional power plants and hence limit the overall carbon emission. However, the wind power is intermittent by nature and hence may fluctuate and present a stochastic feature. In order to effectively reduce the overall carbon emission, a stochastic day-ahead scheduling optimization model with wind power integration incorporating carbon capture power plant (CCPP) and coupon-based demand response (CDR) is proposed in this work. Firstly, the formulation of CDR and the operating mechanism of CCPPs are clarified. Then the flexible operation mechanism aiming at reducing wind power curtailment and CO2 emissions is analyzed. The random forecasting errors of the day-ahead hourly wind power output and loads are considered. Monte Carlo method is applied to simulate stochastic scenarios and a scenario reduction method is applied to ease the computational burden. Simulation results with on the PJM 5-bus system and the IEEE 118-bus system demonstrate the effectiveness of the proposed method in carbon emission reduction and wind curtailments decrease.
[en] Highlights: • An optimal planning model for DESSs in SOP-based active distribution networks is proposed. • The power flow controllability of SOP is modeled and optimally coordinated with DESS operation. • Inverter-based DG reactive power capability and short-term network reconfiguration at the hourly timescale are incorporated in the planning. • The proposed DESS planning model is formulated as a computationally efficient MISOCP problem. - Abstract: The integration of high-penetration distributed generators (DGs) with smart inverters and the emerging power electronics technology of soft open points provide increased controllability and flexibility to the operation of active distribution networks. Existing works on distributed energy storage planning have not fully considered the coordinated operation of these new power electronic devices with distributed energy storage systems, leading to less economic investment decisions. This paper proposes an optimal planning model of distributed energy storage systems in active distribution networks incorporating soft open points and reactive power capability of DGs. The reactive power capability of DG inverters and on load tap changers are considered in the Volt/VAR control. Moreover, soft open points are modeled to provide flexible active and reactive power control on the associated feeders. Hourly network reconfiguration is conducted to optimize the power flow by changing the network topology. A mixed-integer second-order cone programming model is formulated to optimally determine the locations and energy/power capacities of distributed energy storage systems. Finally, the effectiveness of the proposed model is validated on a modified IEEE 33-node distribution network. Considering soft open points, DG reactive power capability, and network reconfiguration, the results demonstrate the optimal distributed energy storage systems planning obtained by the proposed model achieves better economic solution.