Grid Outages Research Articles

Economic feasibility of integrating solar energy into water utility operations in Ciudad Juárez, Mexico

We perform an economic feasibility analysis for installing grid-connected solar arrays with batteries to power ten water supply, distribution, and treatment stations in Ciudad Juárez in tandem with the existing local grid. This article covers detailed results from the four stations that represent the topographical and geographical variability of the city and the variable electricity demand across sites. Using data from the city’s water utility and calculations from the HOMER and RETScreen models, we find contrasting results. The HOMER model finds that solar arrays with or without batteries are economically viable while RETScreen reaches the opposite conclusion. One key reason for this is the two models’ differing predictions on the amount of electricity the proposed solar arrays would supply. Further, having reliable battery storage is paramount to overcoming grid outages and the intermittent availability of solar power to ensure the success of solar projects. Global scientific consensus recognizes the need for more renewable energy. With a sunny-climate and its status as a manufacturing hub on the US-Mexico border, Ciudad Juárez has the potential to lead the way forward in promoting solar energy. The comparative results of the HOMER and RETScreen models provide a more complete understanding of the feasibility of solar power for Ciudad Juárez’s water infrastructure as well as vital insight into the factors that shape the success of solar projects.

Adaptation to extreme weather events using pre-conditioning: a model-based testing of novel resilience algorithms on a residential case study

Climate change increases the frequency and intensity of extreme weather events that can be a prominent cause of power outages in North America. These events may cause buildings to experience outages for hours to days, endangering occupant well-being. Although typical adaptive strategies can offer assistance, they often demand substantial initial investments. Thus, due to the need for low-cost solutions, this paper evaluates the efficacy of the proposed pre-heating/cooling algorithm using smart thermostats. The ongoing research employs automated energy modelling through Python scripting to streamline the energy model upgrade process and the EnergyPlus Energy Management System (EMS) algorithm to incorporate pre-conditioning features during grid outages. The results indicated an average 18% improvement in peak intensity and 9% in overall performance during extreme events. Also, it offers the potential for future studies to employ this methodology in assessing the effects of other low-cost strategies for adapting to grid disruptions.

Unified dispatch of grid-connected and islanded microgrids

This work develops microgrid dispatch algorithms with a unified approach to model predictive control (MPC) to (a) operate in grid-connected mode to minimize total operational cost, (b) operate in islanded mode to maximize resilience during a utility outage, and (c) utilize weighting factors in the grid-connected objective function to preserve islanded capability (on-site fuel reserves, battery state of charge) to enhance resilience in the potential event of an unplanned grid outage. Resilience is defined using microgrid survivability (probability to serve 100% of critical load), autonomy (duration of time to serve 100% of critical load), and unserved energy (curtailed critical load) for a target of 7 days during a grid outage. The developed methods are applied to a military microgrid with 2,250 kW of diesel generation, 3,450 kW/13,800 kWh battery storage, and 16,479 kW of solar photovoltaics. Sensitivity analysis is conducted to determine the selection of weighting factors to have the best impact on three developed objectives: grid-connected economics, islanded resilience, and carbon intensity. Optimal weighting factors reduce operating costs by 0.1%, increase survivability by 3.9%, increase autonomy by 16.7%, reduce unserved energy by 94.1%, and increase carbon intensity by 2.5%.

Learning Power Grid Outages With Higher-Order Topological Neural Networks

With the increase in cyber-physical threats and extreme weather events, resilience of the power system has become a problem of utmost societal importance. In this paper, we propose a novel approach for resilience improvement of power distribution networks, based on the notions of persistent homology and simplicial neural networks (SNNs) which are new directions in graph learning. In particular, tools of persistent homology allow us to capture the most essential topological descriptors of the distribution network. In turn, extending the convolutional operation to simplicial complexes on the distribution network, using the Hodge-Laplacian analytics, enables us to describe complex interactions among multi-node higher order graph substructures. Such higher order graph substructures are of particular importance in distribution networks, since a change in power demand at a load bus (or the power supplied from a substation) will produce a corresponding perturbation in nodal variables (such as the bus voltages) and edge variables (such as branch currents). We validate our new Higher-Order Topological Neural Networks (HOT-Nets) model for contingency classification of three test distribution networks, the IEEE 37-bus feeder, IEEE 123-bus feeder, and the 342-bus low voltage network. Our experiment results on two case studies (i.e., (i) with sensors placed at all the buses alone in the networks and (ii) with partial observability in the networks) indicate that HOT-Nets substantially outperforms 9 state-of-the-art methods, yielding relative gains of up to 14.04% in terms of system resilience classification.

E-Bus Based Standby Energy Sharing for EVs in the Event of Large-Scale Grid Outage

E-Bus Based Standby Energy Sharing for EVs in the Event of Large-Scale Grid Outage

Multi-agent-based real-time operation of microgrids employing plug-in electric vehicles and building prosumers

Real-time operation of microgrids holds significant importance due to its ability to enhance energy resilience and efficiency. Real-time control in conjunction with continuous monitoring and energy supply and demand fluctuations adaptation, ensures a stable and reliable power supply for local communities, even during grid disturbances or outages. Moreover, real-time operation supports demand response mechanisms, enabling users to actively manage their energy consumption and costs. Overall, it plays a pivotal role in modernizing and future-proofing contemporary energy infrastructure, making it more sustainable. In this paper, an efficient, easily applied multi-agent-based real-time energy management and control system for microgrids employing Plug-In Electric Vehicles (PEVs) and building prosumers is developed. The primary goal of this proposed approach is to determine the optimal power set-points for all microgrid components with the objective to reduce the overall daily operation expenses under fluctuating electricity price, while simultaneously satisfying a wide range of constraints. Well-defined flexibilities of all microgrid components to adjust their power are harnessed to execute optimal power dispatch. The implementation of the proposed method resulted in a noteworthy cost reduction of 14.3% when contrasted with a typical operation of the microgrid.

Adaptive cold-load pickup considerations in 2-stage microgrid unit commitment for enhancing microgrid resilience

In an extended main grid outage spanning multiple days, load shedding serves as a critical mechanism for islanded microgrids to maintain essential power and energy reserves that are indispensable for fulfilling reliability and resiliency mandates. However, using load shedding for such purposes leads to increasing occurrence of cold load pickup (CLPU) events. This study presents an innovative adaptive CLPU model that introduces a method for determining and incorporating parameters related to CLPU power and energy requirements into a two-stage microgrid unit commitment (MGUC) algorithm. In contrast to the traditional fixed-CLPU-curve approach, this model calculates CLPU duration, power, and energy demands by considering outage durations and ambient temperature variations within the MGUC process. By integrating the adaptive CLPU model into the MGUC problem formulation, it allows for the optimal allocation of energy resources throughout the entire scheduling horizon to fulfill the CLPU requirements when scheduling multiple CLPU events. The performance of the enhanced MGUC algorithm considering CLPU needs is assessed using actual load and photovoltaic (PV) data. Simulation results demonstrate significant improvements in dispatch optimality evaluated by the amount of load served, customer comfort, energy storage operation, and adherence to energy schedules. These enhancements collectively contribute to reliable and resilient microgrid operation.

Techno-enviro-socio-economic design and finite set model predictive current control of a grid-connected large-scale hybrid solar/wind energy system: A case study of Sokhna Industrial Zone, Egypt

This article offers a cohesive design optimization and control framework of a large-scale grid-connected battery and battery-less hybrid solar/wind system. Primarily, a techno-enviro-socio-economic design optimization and feasibility analysis were performed for eight distinct energy alternatives. Secondly, a finite-set model predictive current control (FS-MPCC) was proposed to improve both stability and dynamic behavior of the optimal energy alternative under various climatic circumstances. Moreover, finite-set model predictive control is furtherly employed to exploit the maximum permissible power from both solar and wind energies. The proposed framework is verified over a new case-study in Sokhna Industrial Zone in Suez, Egypt. The studied load demand combines both industrial and residential load sectors with a daily consumption of 40,500 kWh and of 4-MW peak. The design optimization results revealed that the grid-connected photovoltaic/wind turbines/converter system has superior sustainable and economic performances with the least net present cost ($17,371,980) and energy cost (0.0782 $/kWh). These values were less than those of the base-case system (grid-only) by 17 % and 22 %, respectively. Moreover, the reliability analysis found a tiny and tolerable daily capacity shortage of 5835-kWh despite the five annual grid outages. Besides, around 52 % of the total energy production was acquired from both solar and wind generation systems, which interpreted in a maximum saving in carbon dioxide emissions by 45.8 % compared to the base-case system and offered substantial social benefits in terms of creating nearly 10 jobs. The FS-MPCC results proved a reinforced dynamic behavior for the maximum power tracking profiles of solar and wind systems as well as in the DC-bus voltage profile regarding the over- and undershoot and the steady-state response. Compared to PI controller, the proposed FS-MPCC creates dq-axis voltages and currents with less disturbances and ripples which inject current/voltage to the grid with fewer harmonics and thus increases the generator's lifetime. To sum up, the proposed method features an integrative and compelling case to be used as an efficient tool to implement large-scale renewable energy projects. It could act as a blueprint for energy developers, researchers, investors, and authorities to achieve affordable and sustainable energy access.

Battery–integrated combined cooling, heating and power plant (CCHP) through NH3 – H2O absorption system in a hospital facility

Combined heat and power (CHP) systems have been employed in various applications for years. They are gaining increasing attention in the residential and small industrial sectors for their primary energy saving potential and CO 2 emissions reduction. Additionally, in specific applications such as the hospital sector, CHP plants can play a critical role in replacing fossil-fueled electric generators to supply electricity during local grid outages, thus enhancing hospital facilities’ energy efficiency, while also securing reliability and efficiency of operation. Hospitals have unique energy demand profiles, with high and constant demand (particularly heating and cooling demands), making them an ideal use-case for trigeneration systems. Combined, cooling, heating and power plants (CCHP) are capable to provide not only heating and electricity, but also cooling through the efficient exploitation of a single energy source, reducing Hospitals’ reliance on the local grid. In this context, this work aims at evaluating the potential of a battery-integrated CCHP plant through an innovative ammonia-water absorption system, whose energy analysis is based on data acquired from an Italian Hospital facility. The potential of integrating an experimental combined cooling and power production ammonia-water absorption system has been investigated starting from an optimized battery energy storage system (BESS) CHP plant configuration, studied for the same facility. A proper control strategy has been developed to maximize the cooling production when required from the end-user through CHP plant’s exhaust gases waste heat recovery. The energy analysis demonstrated the advantages of the CCHP-BESS plant over the optimal CHP-BESS configuration, with an 11.22% increase in primary energy saving, a 9.85% reduction in CO 2 emissions, and a 5.03% decrease in electric peak power demand.

Evaluation method of composite insulator aging status based on hyperspectral imaging technology

Silicone rubber insulators' long-term operation in complex environments can lead to accelerated aging, causing the risk of flashovers and grid outages. Timely detection of the insulators' aging status is crucial to ensure the stable operation of grid. Therefore, this paper proposes a pixel-level fast, non-contact detection method based on hyperspectral imaging (HSI) technology for insulators' aging state. Firstly, the hyperspectral spectral line of the samples' six aging levels was extracted, and 14 features were extracted using pre-processing methods like segmented principal component analysis (SPCA) to reduce irrelevant information interference. Secondly, the aging state classification model was established, and through comparison using overall accuracy, F1 score, Precision, and Recall, the random forest (RF) model demonstrated optimal performance with a classification accuracy of 96.81%. Finally, this study utilized HSI technology to achieve a pixel-level evaluation of composite insulators' aging status, and the technique's feasibility was verified through specific parameters such as roughness.

Long-term microgrid expansion planning with resilience and environmental benefits using deep reinforcement learning

Microgrid plays an increasingly important role to enhance power resilience and environmental protection regarding greenhouse gas emission reduction through the widespread applications of distributed and renewable energy. Because of the steady growth of load demand, the strict power resilience requirements and the pressing need of carbon emission reduction, microgrid expansion planning considering those factors has become a currently topical topic. In this study, a new framework for long-term microgrid expansion planning, in which a microgrid serves as a backup power system in the event of main grid outages from the perspectives of economy, resilience and greenhouse gas emission, is proposed. Deep reinforcement learning method is used to solve this dynamic and stochastic optimization problem by taking into account various uncertainties and constraints for the long-range planning. Case studies of 20-year microgrid expansion planning using actual data are conducted. The simulation results demonstrate the effectiveness of the proposed framework on reducing greenhouse gas emissions and total cost including economic losses resulting from power grid outages, investment and operating cost of microgrid entities. In addition, the impact of customer load demand and microgrid entities price on optimal planning policies is discussed. The results demonstrate that microgrid expansion planning can be effectively adapted to different levels of load demand and different scenarios of price changes under the proposed framework. This work is helpful for decision makers to implement cost-effective and power resilient microgrid expansion planning with greenhouse gas emission reduction benefits in the long term.

Frequency control by BESS for smooth Island transition of a hydro‐powered microgrid

AbstractThis paper develops a frequency control strategy for a battery energy storage system to facilitate the smooth island transition of a hydro‐powered microgrid during unplanned grid outages. The proposed frequency control strategy uses a PI‐based droop controller, where the tuning of the controller accounts for the limitations in the power response of a hydro generator and the required frequency quality of the microgrid. The effectiveness of the frequency control strategy is verified in Simulink using phasor simulations, and it is further validated in laboratory tests. The results demonstrate that the proposed PI‐based droop and its tuning strategy fulfill the desired frequency quality requirement of the hydro‐powered microgrid without over‐dimensioning the size of the storage capacity as compared to the traditional proportional droop controller.

Emergency management short term response to extreme heat in the 25 most populated U.S. cities

BackgroundExtreme heat events are a leading weather-related killer in the United States and are predicted to increase in frequency, duration, and intensity. Emergency managers lead short-term response to heat events as well as other natural and man-made hazards with discretion to develop criteria to activate heat responses, such as opening cooling centers and increasing surveillance activities. We aim to describe and summarize emergency management plans for short-term response to extreme heat events across the U.S. MethodsInformation on hazard mitigation plans (HMP) and heat response plans (HRP) in the 25 most populated cities in the US were collected from the National Science Foundation's Natural Hazards Engineering Research Infrastructure DesignSafe Data Depot; interviews with emergency managers; and Freedom of Information Act requests. Obtained responses and policy documents were analyzed to answer the following questions developed in consultation with the National Weather Service (NWS), Weather Program Office: 1. How many locations have developed an HRP? 2. What are the emergency management heat response activation thresholds and metrics for emergency response? 3. Do thresholds change with cascading hazards? 4. Are heat vulnerability indexes (HVI) and/or social vulnerability indexes (SVI) included, and 5. Are health outcomes considered in the development of thresholds? Results1. There is no statistical difference in the prevalence of HRPs across geographic regions in the U.S. 2. Initial response thresholds vary greatly across locations from an ambient temperature of 80–85F (26–29 C) to a Heat Index of 108F (42 C). Eighty-eight (22 of 25) percent of locations are using the NWS Heat Index and twelve percent (3 of 25) are using the experimental HeatRisk Product. 3. Sixty-Eight (17 of 25) percent of cities consider cascading hazards, such as a power grid outage, in their HMP or HRP. None of the cities, however, lower response thresholds, as explicitly written in the plans, during these events. 4. Forty percent (10 of 25) of municipalities have an HVI or SVI included or addressed in their plans, & 5. Three of the cities (New York, Phoenix, & Boston) have adapted response thresholds based on local epidemiological studies DiscussionThere is little consistency in the advisory, warning, and watch criteria thresholds across cities which may be a source of confusion to the public. If such criteria are to be tailored to each geographic location, local epidemiological studies and vulnerabilities should help inform the criteria.

Transformer training strategies for forecasting multiple load time series

In the smart grid of the future, accurate load forecasts on the level of individual clients can help to balance supply and demand locally and to prevent grid outages. While the number of monitored clients will increase with the ongoing smart meter rollout, the amount of data per client will always be limited. We evaluate whether a Transformer load forecasting model benefits from a transfer learning strategy, where a global univariate model is trained on the load time series from multiple clients. In experiments with two datasets containing load time series from several hundred clients, we find that the global training strategy is superior to the multivariate and local training strategies used in related work. On average, the global training strategy results in 21.8% and 12.8% lower forecasting errors than the two other strategies, measured across forecasting horizons from one day to one month into the future. A comparison to linear models, multi-layer perceptrons and LSTMs shows that Transformers are effective for load forecasting when they are trained with the global training strategy.

Peer-to-peer trading in smart grid with demand response and grid outage using deep reinforcement learning

With the price of green energy now more reasonable, users can now produce enough electricity to meet their needs and make a profit by selling the surplus on the underground P2P energy market. For households, energy trading and demand management can reduce electricity costs. However, consumers generally obtain market offers based on their expectations and the forecasts of other households. However, the P2P exchange system is not able to quantify the gap between these offers and the best market. The objective of this paper is to apply deep reinforcement learning techniques to optimal energy trading and demand response (DR) methods within a peer-to-peer (P2P) market. The main objective is to maximize cost reductions. The best approach to achieve this objective was investigated as part of this project. The complexity of domestic energy trading and energy recovery is formally characterized as a partially observable Markov decision process (POMDP). Through decentralized training and performance-based learning, the strategy maximizes policy and value functions. In order to identify the most effective proactive solutions, a comparative analysis is carried out between the two parties. Based on the simulation results, it was observed that implementing the recommended reinforcement learning strategy to optimize peer-to-peer (P2P) energy exchange can lead to a significant improvement in the average household reward. Specifically, the average household reward can be increased by 7.6% and 12.08% by employing the aforementioned approach.

Backup Battery Allocation and Workload Migration Against Electrical Load Shedding at Edge

In the 5G era (and the upcoming 6G), Mobile Edge Computing (MEC) has been advocated to serve the massive amount of Internet of Things (IoT) devices by base stations (BSs) and edge data centers (EDCs). Geo-distributed EDCs are generally of much smaller scales as compared to mega DCs and hence of much lower costs, but can have fast response to their users so as to satisfy the demands of real-time applications. As their reliability and availability heavily depend on the electrical power supply, most EDCs are equipped with battery groups as backup power in case of power grid load shedding or outage. In a heterogeneous geo-distributed environment, the QoS of heavily loaded EDCs however can be severely impacted by limited backup power while lightly loaded EDCs may simply waste such precious resources. Moreover, a heavily loaded EDC may suffer from deep discharge of its battery group, which will cause a significant reduction of battery capacity and lifetime. This further aggravates the aforementioned situations should load shedding/outage happen again. In this paper, we carefully analyze the workloads in EDCs and classify them into interactive workloads and batch workloads, respectively. We then develop a novel battery allocation framework with smart workload migration for EDCs, which simultaneously protects interactive workloads from being interrupted and minimizes the waiting time of batch workloads. Our extensive evaluations show that our strategies can optimize all the objectives within a limited overall cost as compared to state-of-the-art practical allocation.

A comprehensive review on Vehicle-to-Home integration based on home and industrial perspective

Exchanging power between vehicles and other electric appliances in bidirectional flow is known as Vehicle-to-Anything (V2X) technology in home and industrial regions. The V2X technology involves Vehicle-to-Home (V2H), Vehicle-to-Vehicle (V2V), and Vehicle-to-Grid (V2G) that enables the bi-directional flow of energy between Electric Vehicles (EVs) and homes or industrial buildings. However, there are challenges associated with V2H integration, including the development of standardized communication protocols, smart grid infrastructure, and ensuring cybersecurity. Regulatory frameworks and interconnection standards also need to be established to ensure the safe and seamless integration of V2H systems into existing energy networks. Conversely, excess energy from renewable sources like solar panels or wind turbines can be stored in the EV battery for later use or to sell back to the grid. The integration of V2H technology offers several advantages such as allowing for increased energy resilience, as EVs can provide backup power during grid outages or natural disasters. Besides, V2H systems can contribute to load balancing by supplying energy to homes or industrial facilities when the demand is high, thereby reducing strain on the electrical grid. This study is considered a reference to the researchers in the field.

Capacity Configuration Method of Flexible Smart Traction Power Supply System Based on Double-Layer Optimization

The flexible smart traction power supply system (FSTPSS) is a new type of traction power supply system including converters, energy storage devices and renewable energies. The capacities of the multiple components in FSTPSS have a significant impact on the economic operation and stable operation. And the capacity configuration optimization requires to consider both the daily operation costs and long-term investment recycle, which has not been fully studied. Based on the characteristics of FSTPSS, this paper proposes a double-layered capacity configuration optimization method by integrating artificial fish swarm algorithm and CPLEX solver. The main goal of this method is to obtain the maximum economic benefits in the whole life cycle. Meanwhile, the operation time of FSTPSS when facing grid outage is also considered. The simulation results show that compared with benchmark, the final converged maximum benefit value has increased by 43.6%, the grid power is averaged cut by 2.26%, the sum of daily cycle number of the energy storage devices increased by 27.3%. In addition, the proposed method can also improve the probability that train can drive out of the current power supply interval in the event of grid outage. By using the proposed method, this probability is increased by 9.27%.

Multi-Stage Mobile BESS Operational Framework to Residential Customers in Planned Outages

Battery Energy Storage Systems (BESSs) play an important role in modern energy systems. At present, stationary BESSs have found wide applicability in real-word applications, while there is a growing trend in exploiting the use of mobile BESSs (MBESSs) for serving energy demands at geographical locations that can vary over time. This paper proposes a comprehensive decision-making framework for a MBESS that dispatches multiple carriers to deliver a number of batteries to serve residential houses in a planned grid outage event. The proposed framework includes model representations capable of addressing the following three scenarios: (1) before the start of the outage -during this scenario, the scheduling of the battery delivery to the houses considers the typical energy operations and aims at exploiting the surplus renewable energy for the charging of the batteries; (2) during the outage period -during this scenario, the battery relocation is optimized to minimize the amount of energy demand affected by the outage by enabling the carriers to dynamically transport the batteries and to serve different houses; and (3) after the outage -in this scenario, an optimal path selection model enables the carriers to recycle the batteries from the houses to the MBESS station. Computational approaches are developed to solve the above three models and extensive numerical simulations are conducted to validate the effectiveness of the proposed framework.

Investigation of a grid-integrated hybrid energy system for residential and electric vehicle (3-wheeler) loads under schedule grid outage

Due to the global energy crisis and the limited availability of conventional fuels and their reserves in certain areas, the utilization of renewable energy resources is significantly paramount for sustainable development. Grid disruptions have been discovered in areas where demand for electricity has surpassed supply. The purpose of this work is to size a grid-integrated hybrid renewable energy system (HRES) during a scheduled power outage. The HRES model was created to meet the requirements for residential load as well as EV charging load at midnight when the residential load is lower. Using the HOMER Pro software tool, the HRES system is developed to satisfy the residentialload demand of 1940 kWh/day and the electric vehicle (3-wheeler) charging load requirement of 645 kWh/day, respectively. This study examined the effects of grid purchasing capacity and grid scheduling outages on techno-economic and environmental performance. The results show that the most optimal configuration among the various configurations is the PV/WT/Batt with grid-integrated system, which has a COE of 0.0714 $/kWh, an NPC of $1,822,653, and emits 164,812 kg/yr of CO2 into the environment. The optimized system consists of a 377 kW PV module, a 2 kW wind turbine, an 836 kWh battery bank, and a 237 kW inverter. In addition, the impacts of sensitivity of the grid purchase and selling capacity, and the different tariffs on the techno-economic indicators of the HRES have been investigated. The daytime grid outage lowers environmental emissions and increases the renewable contribution compared to the grid outage at night. Reducing the grid’s purchase capacity while increasing the share of renewable energy provides significant advantages. The stand-alone PV/Batt has a higher COE (0.26 $/kWh) than the PV/Batt system (0.072 $/kWh) with the net metering option connected to the national grid.