A new non-isolated, high step-up DC-DC converter is proposed based on the current-fed Cockcroft-Walton Voltage Multiplier (CW-VM) that can be used as the boosting power stage of converter for low-voltage low power renewable energy sources such as photovoltaic and fuel cells. The proposed converter employs two low-voltage common-emitter switches. Utilizing the current-fed CW-VM eliminates some problems associated with the conventional voltage-fed type such as voltage ripple and load-dependent voltage droop. Other advantages of the proposed converter are continuous input current, high voltage gain, and high efficiency. Steady state operation principles, specifications, and design considerations of the proposed converter are illustrated. Furthermore, efficiency analysis of the converter is performed and discussed. Experimental tests for a 200 W prototype are carried out which confirm the theoretical analysis.

Growing water scarcity, due to growing populations and varying natural conditions, puts pressure on irrigation systems, which often are the main consumptive water users. Therefore, water resources management to improve the allocation of limited water supplies is essential. In this study, a non-linear programming optimization model with an integrated soil/water balance is developed to determine the optimal reservoir release policies and the optimal cropping pattern around Doroudzan Dam in the South-West of Iran. The proposed model was solved using a genetic algorithm (GA). Four weather conditions were identified by combining the probability levels of rainfall, evapotranspiration and inflow. Moreover, two irrigation strategies, full irrigation and deficit irrigation were modeled under each weather condition. The results indicate that for all weather conditions the total farm income and the total cropped area under deficit irrigation were larger than those under full irrigation. In addition, our results show that when the weather conditions and the availability of water changes the optimal area under corn and sugar beet decreases sharply. In contrast, the change in area cropped with wheat is small. It is concluded that the optimization approach has been successfully applied to Doroudzan Dam region. Thus, decision makers and water authorities can use it as an effective tool for such large and complex irrigation planning problems.

In recent years, there has been an increasing interest in using AC power flow equations for the transmission expansion planning (TEP) studies. The AC power flow equations are quadratic and hence the TEP problem can be formulated as a mixed‐integer quadratically constrained programme. Therefore, the complexity of the TEP problem lies in the non‐convexity of AC power flow equations in which the global optimal solution is not guaranteed to be found. This study aims at proposing a tight convex relaxation for the TEP problem. In this context, first, the TEP problem is formulated as a mixed‐integer bilinear problem by representing the complex bus voltage in its rectangular coordinates. Second, the multiparametric disaggregation technique (MDT) and piecewise McCormick relaxation are employed to generate a mixed‐integer linear relaxation. MDT is based on the discretisation of the domain of one of the variables in every bilinear term. The method presented is much more precise compared with the DC or other linearisation approaches, while the optimal solution is of high quality. The results of the case studies show the tractability and exactness of the proposed model as well as its superiority over the state‐of‐the‐art schemes.

This study presents a novel scheme for transmission expansion planning (TEP) based on piecewise McCormick relaxation. The model presented considers investment and operation cost and identifies the transmission lines to be built. Since the AC power flow equations are inherently non‐convex and non‐linear, the resulting TEP model will be a highly complex optimisation problem, in which the optimal global solution is not guaranteed to be found by the existing techniques. This study aims to transform the non‐linear model of the power network into a novel linear one. The model proposed is much more precise compared with the DC approach, while the global solution is guaranteed to be found by the off‐the‐shelf solvers. This accurate transformation from a non‐convex and non‐linear AC‐TEP formulation into a linear‐TEP model enables the planner to get more insight into the power flow of the power system. The TEP problem is formulated as a mixed‐integer linear programming problem and is solved using the efficient commercial solvers. The results of the case studies show the tractability and exactness of the proposed model as well as its superiority over the state‐of‐the‐art schemes.

The fuel consumption and pollutant emissions of vehicles can significantly be reduced by adding an electric drivetrain to the internal combustion engine (ICE) without deteriorating the vehicle performance. One of the best suggestions to achieve such a goal is Plug-in Hybrid Electric Vehicles (PHEVs). In PHEVs, the design of an efficient energy management strategy (EMS) plays a significant role. In this paper, a new control strategy is proposed for parallel PHEV to keep the ICE operation points close to the high efficiency region while minimizing the fuel consumption over the standardized drive cycles. First, a new high efficiency ICE on/off control strategy is designed. Second, the teaching-learning based optimization (TLBO) algorithm is used to optimize the proposed control strategy and find the optimal ICE operation region. In the rule-based controllers, the ICE operates within predefined constant efficiency region without incorporating the driving pattern. However, the proposed controller can optimally tune the ICE operation points based on each driving scenario to achieve a better fuel economy. Since the control strategy of PHEVs highly depends on the driving patterns, three different drive cycles with three different trip distances and one recharging opportunity during the trip are considered to show the effectiveness of the proposed technique in terms of the fuel economy and the ICE efficiency. The results illustrate that the proposed control strategy could greatly decrease the fuel consumption of the PHEV under all driving scenarios.