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.

The operational cost of distribution systems is significantly affected by the energy losses of the system. Distribution network losses are known to be higher than transmission system losses, constituting over 70% of all the losses that occur in the power system. For the achievement of loss minimization, operation cost mitigation, and generation expense reduction, the efficient allocation of distributed generation (DG) in distribution networks is considered an effective solution. The penetration of renewable DG units in distribution systems has seen an increasing trend in recent times due to the numerous benefits that come with the use of renewable sources for power generation, especially for the environment. Power demand variations and the variability of load levels play a vital role in the determination of the amount of losses that occur on the network and subsequently the optimal generation of DGs. Meanwhile, taking into consideration the variability of load level in the DG allocation problem worsens the computational stress and time significantly. Therefore, this paper has formulated an effective DG allocation model for energy loss minimization considering power demand variation. The developed model was simulated for different distribution systems using A Mathematical Programming Language (AMPL) to validate the performance of the proposed formulation by comparing the proposed model results with two different models (exact and peak load model). Parameters tested in the study are network losses, cost, and computational time. The evaluation of numerical results indicates the proposed model is very effective for DG allocation in terms of losses, cost, and computational time.

Autonomous grid-forming (GFM) inverter testbeds with scalable platforms have attracted interest recently. In this study, a self-synchronized universal droop controller (SUDC) was adopted, tested, and scaled in a small network and a test feeder using a real-time simulation tool to operate microgrids without synchronous generators. We presented a novel GFM inverter control adoption to better understand the dynamic behavior of the inverters and their scalability, which can impact the distribution system (DS). This paper provides a steady-state and transient analysis of the GFM power inverter controller via simulation to better understand voltage and frequency stabilization and ensure that the critical electric loads are not affected during a prolonged power outage. The controllers of the GFM inverter are simulated in HYPERSIM to examine voltage and frequency fluctuations. This analysis includes assessing the black start capability for photovoltaic microgrids, both grid-connected and islanded, during transient fault conditions. The high photovoltaic PV penetration levels open exciting opportunities and challenges for the DS. The GFM inverter control demonstrated appropriate response times for synchronization, connection, and disconnection to the grid. The DS has become more resilient and independent of fossil fuels by increasing the penetration of inverter-based distributed energy resources (DERs).

Military bases, communities, and university-scale microgrids are being implemented to serve critical loads of high priority. Remote switch scheduling and distributed energy resources (DERs) operation strategies for post-disaster service restoration have been explored in previous literature. Flexible buildings offer the central microgrid management system an opportunity to ensure available energy is directed to critical loads. This work presents a novel bi-level optimal sequence of operations for managing the controllable devices in small-scale microgrids to serve loads based on a priority scheme in campus-scale microgrids. This study introduces a technique for step-by-step restoration of customers’ granular loads. After and during an outage scenario, the facilities’ internal loads are energized in sequence according to their customer and local criticality levels, as well as the amount of energy available from the bulk system and DERs. The proposed methodology is formulated as a mixed-integer linear programming (MILP) model and adapts to various operating conditions. The proposed method is validated by performing controller hardware-in-loop (CHIL) case studies on the Banshee microgrid benchmark model on a real-time simulator.

This article presents a comprehensive data-driven approach on enhancing grid-connected microgrid grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as industries, households, and critical infrastructures. The research combines statistical analysis, machine-learning algorithms, and optimization methods to address this issue to develop a holistic approach for predicting and mitigating power outage events. The proposed methodology involves the use of Monte Carlo simulations in MATLAB for future outage prediction, training a Long Short-Term Memory (LSTM) network for forecasting solar irradiance and load profiles with a dataset spanning from 2009 to 2018, and a hybrid LSTM-Particle Swarm Optimization (PSO) model to improve accuracy. Furthermore, the role of battery state of charge (SoC) in enhancing system resilience is explored. The study also assesses the techno-economic advantages of a grid-tied microgrid integrated with solar panels and batteries over conventional grid systems. The proposed methodology and optimization process demonstrate their versatility and applicability to a wide range of microgrid design scenarios comprising solar PV and battery energy storage systems (BESS), making them a valuable resource for enhancing grid resilience and economic efficiency across diverse settings. The results highlight the potential of the proposed approach in strengthening grid resilience by improving autonomy, reducing downtime by 25%, and fostering sustainable energy utilization by 82%.