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The growing demand for electricity and the reconstruction of poor areas in Africa require an effective and reliable energy supply system. The construction of reliable, clean, and inexpensive microgrids, whether isolated or connected to the main grid, has great importance in solving energy supply problems in remote desert areas. It is a complex interaction between the level of reliability, economical operation, and reduced emissions. This paper investigates the establishment of an efficient and cost-effective microgrid in a remote area located in the Djado Plateau, which lies in the Sahara Ténéré desert in northeastern Niger. Three cases are presented and compared to find the best one in terms of low costs. In case 1, the residential area is supplied by PVs and a battery energy storage system (BESS), while in the second case, PVs, a BESS, and a diesel generator (DG) are utilized to supply the load. In the third case, the grid will take on load-feeding responsibilities alongside PVs, a BESS, and a DG (used only in scenario 1 during the 2 h grid outage). The central objective is to lower the cost of the proposed microgrid. Among the three cases, case 3, scenario 2 has the lowest LCC, but implementing it is difficult because of the nature of the site. The results show that case 2 is the best in terms of total life cycle cost (LCC) and no grid dependency, as the annual total LCC reaches about $2,362,997. In this second case, the LCC is 11.19% lower compared to the first case and 5.664% lower compared to the third case, scenario 1.

This research proposes a day-ahead scheduling utilizing both demand side management (DSM), and Energy Management (EM) in a grid-tied nanogrid comprises of photovoltaic, battery, and diesel generator for optimizing the generation cost and the energy not supplied (at grid-outage). Wider terminology is introduced to combine both load controllability (considered in traditional DSM), and interval capability to accommodate additional loads defined as flexible, non-flexible, and semi-flexible intervals. Moreover, the user selection for EM or combined operation of EM with DSM at different degrees of interval flexibility is defined as user preference. In addition, three utility’s operations are considered denoted as fixed rate pricing (FRP), time-of-use (ToU) pricing, and FRP with grid-outage. Hence, the suggested framework utilizes the opportunities of generation diversity, the electricity pricing strategy, and the load flexibility. The obtained result show that, DSM with flexible intervals reduces the cost by 21.02%, 25.23%, and 18.15% for FRP, ToU, and FRP with grid-outage scenarios respectively. And cost reduction by 20.41%, 22.42%, and 17.81% for DSM with semi-flexible intervals and 16.24%, 21.15%, and 13.8% for DSM with non-flexible intervals. This cost reduction is associated with full utilization of renewable energy generation and reduction of the energy from/to battery which enhances its lifetime or reduces the required battery size during design stage for cost and provisions saving in flexible and semi-flexible intervals. A hybrid optimization technique of Moth-flame optimization algorithm, and Lagrange’s multiplier is proposed and confirms its effectiveness with detailed comparison with other techniques.

Telecommunication Base Transceiver Stations (BTSs) require a resilient and sustainable power supply to ensure uninterrupted operation, particularly during grid outages. Thus, this paper proposes an Adaptive Model Predictive Control (AMPC)-based Energy Management System (EMS) designed to optimize energy dispatch and demand response for a BTS powered by a renewable-based microgrid. The EMS operates under two distinct scenarios: (a) non-grid outages, where the objective is to minimize grid consumption, and (b) outage management, aiming to maximize BTS operational time during grid failures. The system incorporates a dynamic weighting mechanism in the objective function, which adjusts based on real-time power production, consumption, battery state of charge, grid availability, and load satisfaction. Additionally, a demand response strategy is implemented, allowing the BTS to adapt its power consumption according to energy availability. The proposed EMS is evaluated based on BTS loss of transmitted data under different renewable energy profiles. Under normal operation, the EMS is assessed regarding grid energy consumption. Simulation results demonstrate that the proposed AMPC-based EMS enhances BTS resilience.

Intermittent power interruptions and blackouts with long outage durations are very common, especially on weak distribution grids such as in developing countries. This paper proposes a hybrid photovoltaic (PV)-battery-system sizing optimization through a genetic algorithm to address the reliability in fragile grids measured by the loss of power supply probability (LPSP) index. Recorded historical outage data from a real stochastic grid in Ethiopia and measured customer load is used. The resulting hybrid-system Pareto solutions give the flexibility for customers/power utilities to choose appropriate sizes based on the required reliability level. To evaluate the sizing solutions’ robustness, this work considers and compares grid outage modeling through two different approaches. The first is a Markov model, developed to be minimally implemented with limited outage data available. The second is a Weibull model, commonly used to describe extreme phenomena and failure analysis. It is more faithful in reproducing the dispersion of outage events. Using these models, the effectiveness and performance of the PV-battery system is verified on a large number of simulated outage scenarios, to estimate the real performance of the optimized design. It leads to a more accurate evaluation of the behavior of a renewable power system to a weak and unreliable electrical grid.

The ever‐increasing energy demand and extreme weather days lead to more frequent power outage events. This paper proposes a hierarchical energy management scheme for residential communities, aiming to facilitate energy sharing among houses and minimize the impact on the grid outage on the whole community. In the proposed model, the complexity of scheduling residential energy resources of multiple houses is decomposed into a bi‐level structure, in which the Home Energy Management System (HEMS) of each house iteratively interacts with the Community Energy Management System (CEMS). In the upper layer, the CEMS receives the information of the planned outage; it then solves a social warfare optimization model to determine: (1) charging/discharging power of a community battery energy storage system, and (2) load re‐shaping instructions of each house. In the lower layer, the HEMS of each house performs appliance‐level autonomous scheduling to try to satisfy the load re‐shaping instructions received from the CEMS. The autonomous scheduling result of individual HEMSs are sent back to the CEMS, and the latter updates the upper‐layer result based on the received result. This process continues until the convergence criteria is achieved. Extensive simulations are conducted to validate the proposed solution

To enhance online dispatch decision support capabilities for power grid outage planning, this study proposes a Universal Information Extraction (UIE)-based method for enhanced named entity recognition and event extraction from outage documents. First, a Structured Extraction Language (SEL) framework is developed that unifies the semantic modeling of outage information to generate standardized representations for dual-task parsing of events and entities. Subsequently, a trigger-centric event extraction model is developed through feature learning of outage plan triggers and syntactic pattern entities. Finally, the event extraction model is employed to identify operational objects and action triggers, while the entity recognition model detects seven critical equipment entities within these operational objects. Validated on real-world outage plans from a provincial-level power dispatch center, the methodology demonstrates reliable detection capabilities for both named entity recognition and event extraction. Relative to conventional techniques, the F1 score increases by 1.08% for event extraction and 2.48% for named entity recognition.

An attempt has been made to evaluate the financial feasibility of hybrid power supply option during real-time grid power unavailability (continuous and intermittent) conditions and determine the optimal hybrid power supply configurations for outdoor telecom towers in India. As grid power availability is highly dependent on locations, a review of real-time hourly grid power supply availability for telecom towers at 36 locations in different parts of India has also been presented. Ten different locations across different climatic zones were analyzed, and it was found that duration and continuity (continuous and intermittent) of power outages considerably affect the LCOE of different configurations. So it is imperative that these factors be taken into account when determining the optimal hybrid power system. Solar PV-based hybrid power supply systems were found to have lower LCOE for all power outage conditions both in continuous as well as intermittent with their values in the range of Indian rupees (INR) 6.76–INR 26.32 (US $0.095–US $0.371) per kWh for the optimal cases (1 US$ =  INR 76.28 (As on April 27, 2020)). While solar PV with battery is found to be the least cost hybrid power supply options for the telecom towers located in areas with continuous grid power unavailability up to 4 h, a diesel generator also needs to be included for larger hours of continuous grid unavailability.

With the increased climatic change and modern grid complexity, extreme grid power outage events caused by natural calamity and human interruptions have led to an urgency to enhance the grid resiliency. Microgrids (MGs) have proved to be a concrete solution to these situations. However, these events are quite uncertain, leading to the unintentional island of MGs that has adverse effects. Thus, as a first step toward increasing grid resiliency with MG, informing the distributed generations about the unintentional island is a critical task. Hence, there is a need to develop a quick and reliable unintentional island detection scheme. Micro phasor measurement units (μPMUs) are becoming popular in MG. Given this, this study proposes an inadvertent island detection scheme in an MG using an intelligent μPMU. With the μPMU, the voltage at solar generator bus is measured, three features are extracted through spectral kurtosis and random forest classifier is employed for island detection. After island detection, a control methodology is proposed to circumvent the post‐effects. The method has zero non‐detection zone, 99.83% accuracy and a detection time of 20 ms. The reliability of the algorithm is ascertained using the analytical hierarchical approach and software fault‐tree analysis.

As demand for electricity increases on an already strained electrical supply due to urbanization, population growth, and climate change, the likelihood of power outages will also increase. While researchers understand that the number of electrical grid disturbances is increasing, we do not adequately understand how increased power outages will affect a society that has become increasingly dependent on a reliable electric supply. This systematic review aims to understand how power outages have affected society, primarily through health impacts, and identify populations most vulnerable to power outages based on the conclusions from prior studies. Based on search parameters, 762 articles were initially identified, of which only 50 discussed the social impacts of power outages in North America. According to this literature, racial and ethnic minorities, especially Blacks or African Americans, those of lower socioeconomic status, children, older adults, and those living in rural areas experienced more significant impacts from previous power outages. Additionally, criminal activity increased during prolonged power outages with both pro-social and anti-social behaviors observed. Providing financial assistance or resources to replace spoiled goods can reduce crime. Future research on this topic must consider the financial effects of power outages, how power outage impacts seasonally vary, and the different durations of power outage impacts.

Automating the process of restoring service to customers after a large‐scale outage event have significant impacts on the agility and speed of recovery in distribution systems. This study develops a set of probabilistic metrics to assess the impact of automation in enhancing the resilience of power distribution systems. The proposed metrics capture the features and detailed process of automatically locating and isolating faults and restoring the service to customers in distribution systems. In addition, this study develops a model to evaluate the spatio–temporal impacts of hurricane on power distribution systems, which is used to generate hurricane‐induced outage scenarios to calculate the resilience metrics given different automation schemes. The proposed model is utilised to evaluate the resilience of bus number four of Roy Billinton Test System in the face of a passing hurricane. The metrics are calculated to evaluate the impact of different levels of automation throughout the network, the intensity of the hurricane, and line hardening on the resilience of the system.