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Network reconfiguration is an effective technique to reduce lost power in distribution systems, where losses are significantly greater than the ones in transmission. The distribution power loss produces a costly operation and a degraded voltage profile over the electric power systems. In traditional reconfiguration, the topology of the distribution network is efficiently modified to achieve minimum power losses based on the electric energy requested by end-users. Various types of energy consumers are connected to load points of actual distribution networks that cause different power losses compared to mono-type loads. However, the majority of models propose distribution reconfiguration problems disregarding consumer diversity. The few studies that have included load type in their proposed reconfiguration strategies present non-linear formulations for the problem and do not consider the impact of ambient temperature on network topology. However, non-linear models can be solved by metaheuristic algorithms without guaranteed optimal solutions or can be computed by non-linear solvers in classic optimization tools but require intensive and time-consuming computations that restrict their real-world applications. Moreover, environmental temperature affects electricity consumption, and therefore, the selection of appropriate switching combinations. Therefore, the current paper introduces an efficient model for the reconfiguration of distribution systems considering various load types with their dependencies on environment temperature rise that can be easily implemented by linear solvers in commercial optimization software. The results indicate that the proposed model is precise enough to find accurate solutions for desired reconfiguration problems and is adequately fast for online reconfigurations, showing significant effect of environment temperature on the configuration of distribution systems through load and power losses changes.

Electricity generation from biomass energy resources is compared with wind and solar power considering an uncertain electric load and a variable generation in the present study. The comparison results can give a good perspective to readers for judgment about energy utilization method in places which all of these renewable energy sources are available and the electricity consumption as well as wind and solar energy are not certain and constant. The hybrid operation of biomass, wind, and solar is unavoidable due to limited availability to energy sources in each region under load uncertainty and generation variability. For examples, some areas have a great potential for wind power generation, while they are cloudy most of the times as solar irradiation efficiency is low in such places. Whereas in some sunny areas, often wind is absent and in some other places like seashores both wind blow and sunshine is good. Important question is that if these three energy generation sources are available enough in the same place, which of them is better and more efficient to satisfy electricity demand of consumers with uncertain load patterns? To address this concern, a village in Morocco with great potentials of wind, solar, and biomass energy generation is economically studied to find out better renewable electricity generation method in uncertain environments.

The distribution losses significantly affect the system's operational costs and voltage profile that can be reduced by network reconfiguration. However, load power is not constant and can be changed by voltage fluctuation of network buses, in which reconfiguring the topology of distribution systems impacts the magnitude of bus voltages. Accordingly, alteration of nodal voltages affects load amount and subsequent power losses. Still, most of the research works associated with distribution systems reconfiguration have not included load power dependency to nodal voltages in their proposed formulations. Only a few articles have considered voltage-dependent loads in their reconfiguration models but introduced nonlinear formulations or highly approximated linear approaches. While nonlinear models can be solved by metaheuristic algorithms without guaranteeing the optimality of solutions or may be implemented by commercial nonlinear solvers, they require intensive and time-consuming computations. Moreover, linearized models are highly approximated by piecewise linear functions with many unknown parameters. Thus, this paper presents the effective design of the reconfiguration problems, including voltage dependency of loads, which can be solved using commercial linear solvers. The proposed models are precise enough to find accurate results for the reconfiguration problem and are adequately fast to converge to optimal solutions. The results show that the proposed solutions not only reduce active power losses significantly but increase the minimum voltage of the system effectively. Nevertheless, the second strategy presents a better efficiency over smaller networks and provides higher voltage levels.

Distribution networks are susceptible to de-energizing many customers during an outage event. Through distribution network reconfiguration (DNR) techniques, outage events can be partially or entirely restored by rerouting power to customers in a new configuration that also isolates the faulted string. To improve reliability indices, utilities have been deploying many reclosers per feeder, increasing the chances for protection miscoordination. Based on that, it is crucial that the restoration solution not only finds an optimal switching configuration to re-energize customers but also identifies miscoordinations in the system to narrow the faulted region to the smallest string possible. This paper presents a two-stage distribution systems outage restoration solution. Its novelty integrates a set of logic to properly localize the faulted area while identifying potential miscoordinations between reclosers to then improve the optimal outage restoration benefit. The restoration is computed as a Multi-Objective Optimization (MOO) based on a Mixed-Integer Convex Programming (MICP) model that minimizes unserved customers and switching maneuvers while maintaining de-energized the faulted area, providing the appropriated switching control sequence (SCS) to achieve the optimal set of open and closed switches. The validation is made in a realistic distribution systems model where different outage locations and miscoordination conditions are considered.

Distribution system operators seriously restrict bus voltage deviations within a small range from the nominal voltage to ensure security aspects. The distribution network reconfiguration technique is often performed to improve these voltage levels, if not to reduce power losses. However, distribution systems feed various kinds of consumers, and their power demand dependency on bus voltages is different based on their load type. Modifying the network configuration changes the system's nodal voltage levels, and then the load power is also impacted after the reconfiguration. Only a few research works in the field of distribution system reconfiguration have included load power dependency to nodal voltages in their proposed models. However, these studies have not discussed whether adding voltage-depend loads to the reconfiguration problem is beneficial or only increases the computational efforts without substantial gains on the optimal solution. With the intention to fill this gap, this article develops a comprehensive analysis of how the change of load according to the voltage affects reconfiguration solutions. This investigation aims to help distribution system researchers and readers understand this phenomenon and highlight the benefits and drawbacks of taking it into account. Also, a robust reconfiguration model for considering load types and nodal voltage is proposed. The model is validated in different network models and sizes, where the obtained results are compared with other models presented in the literature. The results analysis indicates that voltage fluctuations impact the distribution network configuration through voltage effect on power losses and the system nodal loads. Also, load voltage-dependent models are more flexible than constant demand based-formulations in finding feasible solutions in more restricted voltage tolerances.