Intermittent Power Generation Research Articles

Preface

IOP Conference Series: Materials Science and Engineering International Nuclear Science, Technology and Engineering Conference 2023 (iNuSTEC2023) “Nuclear Science and Technology For Socio-economic Development “ Universiti Tenaga Nasional Kajang, Selangor, Malaysia 10-12 October 2023 MESSAGE FROM: VICE-CHANCELLOR, UNIVERSITI TENAGA NASIONAL (UNITEN)UNITEN is honored to host the International Nuclear Science, Technology, and Engineering Conference 2023 (iNUSTEC2023) and the Nuclear Youth Competition (NYC). With the theme “Nuclear Science and Technology for Sustainable Development Goals”, iNuSTEC2023 is the thirteenth in a series of conferences hosted by the Malaysian Nuclear Society (MNS) since 2010 through collaboration with research institutes and local universities.Don’t we feel like time has passed by so quickly? And with it, came along significant and some were unthinkable changes to our lives. According to Our World in Data statistics, in 2022, each of us consumed on average 3.5 times more energy compared to 40 years ago. In the year 2050, Malaysia is expected to consume almost twice the amount of electricity in 2022 (320 TWh vs. 180 TWh). This means that on a daily basis in 2050, Malaysia is expected to consume close to 0.9 TWh of electricity per day.About 50 million electric vehicles are expected to be on the road in 2050. Every day, about 0.6 TWh of electricity is needed to charge these cars. That is an additional two-thirds of the expected daily electricity consumption per person in 2050. Malaysia has identified the pathways for Responsible Transition 2050 in the National Energy Transition Roadmap (NETR) to tackle the energy trilemma of affordability, security, and sustainability. These pathways include having 0% coal share and 70% renewable energy share of installed capacity in 2050.70% of renewable energy share is about 57 GW of solar PV, which will take a total land size that is slightly smaller than Melaka state. The same amount of power can be generated by nuclear power on a total land size that is 10 times smaller. That is not even considering the intermittency of power generation with solar PV.At UNITEN, we believe that research and knowledge in nuclear power can play a huge role in supporting the Low Carbon Nation Aspiration and the Responsible Transition, especially in providing affordable, clean and safe energy supply to the Rakyat. Institute of Nuclear Energy (INE) at UNITEN was established in May this year to further empower and accelerate the existing research and educational efforts done at UNITEN since 2010.At the university level, UNITEN has established the National Energy Centre (NEC) to support the national energy transition efforts. NEC will serve as a hub for research institutes, industrial and international linkages, and government agencies to form collaborations with experts from local and foreign universities, energy agencies, and stakeholders.UNITEN envisions that NEC will play a significant role in providing technological solutions for Malaysia’s energy transition which will be beneficial to Malaysia, as well as the surrounding region. NEC will also play the role of capacity building for the country’s future workforce that will lead Malaysia towards net-zero in 2050. I extend my warmest invitation to every participant attending this conference, to be a part of this concerted nationwide effort in championing sustainability through involvement with the NEC.I would like to remind participants, that you are among the very small percentage of Malaysia’s population, who are experts in the nuclear field. Thank you so much for your contributions to keeping the knowledge related to nuclear science and engineering alive, despite how hard or negative it can get in the industry.Congratulations to the organizing committee for hosting this iNuSTEC2023 together with the Nuclear Youth Competition (NYC2023). My deepest appreciation goes to our keynote speakers, technical co-sponsors, and all supporters for their contribution to ensuring the success of this conference. To the presenters and participants, thank you for your participation and I wish the conference all success. PROFESSOR IR. DR. NOOR AZUAN ABU OSMAN Vice-Chancellor Universiti Tenaga Nasional List of Editors, Contents, Papers, iNuSTEC2023 Organising Committee, Nuclear Youth Competition 2023 Secretariat, Nuclear Youth Competition 2023, Acknowledgement, Mns Council Members 2023/2025, are available in the pdf.

Urban-scale energy matching optimization with smart EV charging and V2G in a net-zero energy city powered by wind and solar energy

Renewable energy and electric vehicles (EVs) are crucial technologies for achieving sustainable cities. However, intermittent power generation from renewable energy sources and increased peak load due to EV charging can pose technical challenges for the power systems. Improved load matching through energy system optimization can minimize these challenges. This paper assesses the optimal urban-scale energy matching potentials in a net-zero energy city powered by wind and solar energy, considering three EV charging scenarios: opportunistic charging, smart charging, and vehicle-to-grid (V2G). A city on the west coast of Sweden is used as a case study. The smart charging and V2G schemes aim to minimize the mismatch between generation and load, and are formulated as quadratic programming problems. The simulation results show that the optimal load matching performance is achieved in a net-zero energy city with the V2G scheme and a wind-PV electricity production share of 70:30. The load matching performance in the optimal net-zero energy city is increased from 68% with opportunistic charging to 73% with smart charging and further to 84% with V2G. It is also shown that a 2.4 GWh EV battery participating in the V2G scheme equals 1.4 GWh stationary energy storage in improving urban-scale load matching performance. The findings indicate that EVs have a high potential to provide flexibility to urban energy systems.

Probabilistic Planning for an Energy Storage System Considering the Uncertainties in Smart Distribution Networks

Today, many countries are focused on smart grids due to their positive effects on all sectors of a power system, including those of operators, utilities, and consumers. Furthermore, the usage of renewable energy sources for power production is quickly expanding due to the depletion of fossil fuels and the emissions caused by their use. Additionally, intermittent power generation from renewable energy sources, such as wind and solar, necessitates the use of energy storage devices with which to ensure a continuous power supply to meet demand. This can be accomplished by employing an appropriate storage device with a sufficient storage capacity, thus enabling a grid-connected solar PV and wind system to have enhanced performance and to reduce adverse effects on the power quality of the grid. In this study, a probabilistic planning model that takes the intermittent natures of solar irradiances, wind speeds, and system demands into account is introduced. A novel criterion is also adopted to map the three-dimensional spaces of intermittency with the proposed model for optimizing BESS charging/discharging decisions. This planning model is intended to minimize the economic costs of investment and operation of a battery energy storage system (BESS) for a planning period. Moreover, the substation and feeder upgrade costs, as well as the overall system loss costs, are included in the proposed model. Particle swarm optimization (PSO) is utilized to find the optimal sizing, location, and operation of energy storage systems. The proposed methodology was validated using a 69-bus distribution system.

Efficiency-Driven Iterative Model for Underwater Compressed Air Energy Storage (UW-CAES)

The competitiveness of large-scale offshore wind parks is influenced by the intermittent power generation of wind turbines, which impacts network service costs such as reserve requirements, capacity credit, and system inertia. Buffer power plants smooth the peaks in power generation, distribute electric power when the wind is absent or insufficient, and improve the capacity factor of wind parks and their profitability. By substituting the variable pressure storage with an underwater variable volume air reservoir and reducing the wastage of compression heat using liquid Thermal Energy Storage (TES), which eliminates the combustor, the plant design allows overcoming the most common drawbacks of CAES plants. Underwater Compressed Air Energy Storage (UW-CAES) plants are investigated with a thermodynamic model to drive the power plant design toward efficiency maximization. Functional maps, constrained on the plant pressure ratio and the number of compressor/turbine phases with inter-refrigerated/inter-heating phases, are drawn by solving the model iteratively for the heat exchangers’ effectiveness to meet the target turbine discharge temperature, selected in advance to avoid unfeasible mathematical solutions.

Fuzzy Logic Energy Management Based on Wind Turbine for Standalone Application

Wind turbines (WT) offer valuable environmentally friendly energy solutions, particularly for electricity generation, owing to their renewable nature. These renewable energy systems (RESs) present an indispensable solution for sites that are not associated with the electrical grid. However, their intermittent power generation, which can be more or less than that required by the load, due to variable wind patterns, poses a challenge. To overcome these weaknesses, the integration of robustly controlled storage systems is a good proposal as it enhances the stability of standalone applications in terms of voltages and powers. In this paper, an intelligent supervisory power control system is developed to manage the different components of the renewable energy generator (REG). A fuzzy logic proportional–integral PI controller is also introduced, in the permanent magnet synchronous generator (PMSG) and the batteries, to better regulate energy, compared to a conventional PI controller. The main objective of this study is to introduce a new control strategy to satisfy the energy requirement of the load. The configuration of the suggested control methodology consists of a fuzzy logic supervisor (FLS) elaborated to manage energy flows, ensuring equilibrium between production and consumption amidst weather and load changes. The presented control method was applied to an autonomous wind battery station, showcasing its ability to maintain energy supply, minimize fluctuations, and enhance stability. Simulation results, executed in MATLAB/Simulink, confirm the effectiveness of the proposed system under various scenarios of wind speed variability and storage system state of charge.

Smoothing Intermittent Output Power in Grid-Connected Doubly Fed Induction Generator Wind Turbines with Li-Ion Batteries

Wind energy is an increasingly important renewable resource in today’s global energy landscape. However, it faces challenges due to the unpredictable nature of wind speeds, resulting in intermittent power generation. This intermittency can disrupt power grid stability when integrating doubly fed induction generators (DFIGs). To address this challenge, we propose integrating a Li-ion battery energy storage system (BESS) with the direct current (DC) link of grid-connected DFIGs to mitigate power fluctuations caused by variable wind speed conditions. Our approach entails meticulous battery modeling, sizing, and control methods, all tailored to match the required output power of DFIG wind turbines. To demonstrate how well our Li-ion battery solution works, we have developed a MATLAB/Simulink R2022a version model. This model enables us to compare situations with and without the Li-ion battery in various operating conditions, including steady-state and dynamic transient scenarios. We also designed a buck–boost bidirectional DC-DC converter controlled by a proportional integral controller for battery charging and discharging. The battery actively monitors the DC-link voltage of the DFIG wind turbine and dynamically adjusts its stored energy in response to the voltage level. Thus, DFIG wind turbines consistently generate 1.5 MW of active power, operating with a highly efficient power factor of 1.0, indicating there is no reactive power produced. Our simulation results confirm that Li-ion batteries effectively mitigate power fluctuations in grid-connected DFIG wind turbines. As a result, Li-ion batteries enhance grid power stability and quality by absorbing or releasing power to compensate for variations in wind energy production.

Changes in solar resource intermittency and reliability under Australia’s future warmer climate

The dependency of photovoltaic (PV) power generation on meteorological parameters can impact power production due to weather-induced variability. During the day, fluctuations in radiation introduce intermittency in power generated, raising reliability and grid stability issues at higher penetration levels. Long-term future resource assessment provides an effective tool for estimating resource reliability and future intermittency essential for pre-feasibility site assessments around the world. Australia has high solar power capacity, with several solar farms in operational and developmental stage. Using Australia as a case study, this research aims to understand Australia's solar resource distribution and variability using regional climate model projections under a high emission scenario. Results indicate an abundance of solar resource power density in Australia, especially in the North (450-500Wm−2). The solar resource will be more reliable in Eastern Australia in the future with ∼ 5% increase in resource density. Results suggest reduction in intermittency (∼20-minute lull periods) in the East with increase in clear-sky days/year in the future (∼20 days/year). Resource assessment of Sun Cable and New England solar farm located in Australia, revealed the future scope of increase in clear-sky days at the sites. This long-term future solar variability analysis can help identify regions in Australia where PV systems will be least susceptible to losses due to intermittency. Furthermore, this study will help in critical decision-making processes like planning storage systems, site selection, opportunities to create hybrid solar farms with the co-existence of solar and wind technology, etc., to mitigate the risks associated with future intermittent PV power generation.

Artificial Neural Network with 3-Port Dc-Dc Converter Based Energy Management Scheme in Sustainable Energy Sources

In micro grids, energy management is referred to as an information and control system that offers the essential functionality to ensure that the energy supply from the generation and distribution systems occurs at the lowest possible operational cost. Energy management systems (EMS) support distributed energy resource utilization in micro grids, especially when variable generation and pricing are present. In this paper, an Artificial Neural Network (ANN)-based energy management approach for a hybrid wind, solar and Battery Storage System (BSS) is presented. To sustain the DC voltage, a 3 Port DC-DC Converter is also proposed. While renewable energy systems have numerous advantages, one of the challenges they face is the intermittency of power generation, leading to fluctuations in the power supply to the grid. Therefore, EMS aims to reduce these variations. Another goal is to maintain the battery state of charge (SOC) within the allowed ranges to extend the battery life. The implementation is carried out in Simulink/Matlab platform. To demonstrate the efficacy of the suggested approach, we compare the Total Harmonic Distortion (THD) of the proposed controller (1.52%) with that of conventional controllers, including the ZSI-based PID controller (3.05%), PI controller (4.02%), and FO-PI (3.32%) controller.

Estimating the value of energy storage: The role of pumped hydropower in the electricity supply network

This study explores the role of storage systems in reducing the variability of renewable power, focusing on pumped hydropower storage (PHS) systems. We regress the hourly storage by PHS on the hourly solar power generation to estimate the average amount of solar power generation stored by PHS systems. We adopt an instrumental variable approach to mitigate any endogeneity concerns, in which a weighted sunshine duration serves as the instrument for solar power generation. Our estimation results show that PHS systems alleviate the intermittency of solar power generation: an additional 1 MWh of solar power generation corresponds to an additional 0.249 MWh of storage by PHS systems. The relationship between PHS and solar power is more pronounced when the demand is relatively low and solar power generation is large. Inter-regional transmission grids also respond to an increase in solar power generation but to a lesser extent than PHS systems. Based on the estimated coefficients in the empirical analysis, we quantified the value of the existing PHS systems as storage systems that mitigated intermittent solar power generation. The estimated social benefit of avoiding curtailment is 180–280 million yen for a 10 MW scale plant, accounting for 7.7–11.7% of the cost of constructing a new PHS system. The result highlights the importance of effectively utilizing the current PHS capacity.

Multi-level coordinated control of islanded DC microgrid integrated with electric vehicle charging stations with fault ride-through capability

This paper presents an integrated control framework for islanded DC microgrid (MG) with electric vehicle (EV) charging stations, energy storage unit, and AC/DC loads. The proposed DC MG consists of two DC buses with different voltages, photovoltaic (PV) arrays as intermittent power generation sources, energy storage, three EV charging stations, and a three-phase inverter to power time-varying three-phase loads. Control strategies are proposed to select the charge-discharge mode of EVs that does not negatively affect the MG, as well as coordinate the production and demand management. A central control system enables the operation of MG under different working conditions by managing generation, consumption, and energy storage and also monitors the performance of the charging/discharging unit and the issues relevant to commands of different charging/discharging programs. A specific control strategy has also been implemented to provide fault ride-through (FRT). As a result, the MG feeds its loads in any operating condition. Nonlinear simulations are carried out in MATLAB/SIMULINK software to show that the proposed control methods easily and effectively achieve all objectives, including supplying DC/AC loads, managing battery/resources/charging stations, and detecting/dealing with AC faults.

Power-Weighted Prediction of Photovoltaic Power Generation in the Context of Structural Equation Modeling

With the popularization of solar energy development and utilization, photovoltaic power generation is widely used in countries around the world and is increasingly becoming an important part of new energy generation. However, it cannot be ignored that changes in solar radiation and meteorological conditions can cause volatility and intermittency in power generation, which, in turn, affects the stability and security of the power grid. Therefore, many studies aim to solve this problem by constructing accurate power prediction models for PV plants. However, most studies focus on adjusting the photovoltaic power station prediction model structure and parameters to achieve a high prediction accuracy. Few studies have examined how the various parameters affect the output of photovoltaic power plants, as well as how significantly and effectively these elements influence the forecast accuracy. In this study, we evaluate the correlations between solar irradiance intensity (GHI), atmospheric density (ρ), cloudiness (CC), wind speed (WS), relative humidity (RH), and ambient temperature (T) and a photovoltaic power station using a Pearson correlation analysis and remove the factors that have little correlation. The direct and indirect effects of the five factors other than wind speed (CC) on the photovoltaic power station are then estimated based on structural equation modeling; the indirect effects are generated by the interaction between the variables and ultimately have an impact on the power of the photovoltaic power station. Particle swarm optimization-based support vector regression (PSO-SVR) and variable weights utilizing the Mahalanobis distance were used to estimate the power of the photovoltaic power station over a short period of time, based on the contribution of the various solar radiation and climatic elements. Experiments were conducted on the basis of the measured data from a distributed photovoltaic power station in Changzhou, Jiangsu province, China. The results demonstrate that the short-term power of a photovoltaic power station is significantly influenced by the global horizontal irradiance (GHI), ambient temperature (T), and atmospheric density (ρ). Furthermore, the results also demonstrate how calculating the relative importance of the various contributing factors can help to improve the accuracy when estimating how powerful a photovoltaic power station will be. The multiple weighted regression model described in this study is demonstrated to be superior to the standard multiple regression model (PSO-SVR). The multiple weighted regression model resulted in a 7.2% increase in R2, a 10.7% decrease in the sum of squared error (SSE), a 2.2% decrease in the root mean square error (RMSE), and a 2.06% decrease in the continuous ranked probability score (CRPS).

Energy storage capability of seawater batteries for intermittent power generation systems: Conceptualization and modeling

The use of renewable energy for power generation is increasing rapidly. However, residual electricity supplied in excess of demand is a global concern. To effectively utilize excess power, storing surplus renewable energy in energy storage systems (ESSs) is important. In this study, a seawater battery (SWB) is proposed as an ESS for intermittent power resources, and its energy storage capability is evaluated. Four charging scenarios that imitate different forms of renewable energy (constant current, solar, tidal, and wind) reveal that SWB is an efficient ESS for intermittent renewable energy sources. Scenario-dependent energy efficiency follows the order: ideal constant current (83.6%) > solar power (80.4%) > tidal power (79.6%) > wind power (79.4%). The ability of two artificial intelligence models is also tested to estimate the potential of SWBs. A novel long short-term memory model outperforms an artificial neural network model, predicting the potential of SWB with a high precision (R2 > 0.99) and an extremely low error rate (<0.18%). Therefore, the conceptualization and modeling of an SWB as an ESS may pave the way for energy storage from and management of intermittent energy sources.

Bio‐Physicochemical Dual Energy Harvesting Fabrics for Self‐Sustainable Smart Electronic Suits

AbstractDespite the rapid development of various wearable generators to harvest energy from human activity, they are limited to single or intermittent power generation. Here, stretchable and washable energy harvesting fabric is reported, which enables biomechanical and biochemical generation through co‐weaving triboelectric generator (TEG) and perspiration electric generator (PEG)‐based fibers. The two energy harvesting approaches can work individually or simultaneously, thereby combating humidity and increasing the electrical output. The maximum output power density of TEG‐ and PEG‐based fibers can achieve 166 and 5.4 µW cm−2, respectively. The woven‐structured patch‐type energy harvester is conformable with clothing. It can harvest energy from human movements and sweat to drive the wearable Internet of Things (IoT) without charging systems. This synergistic and complementary energy harvester can provide a viable standalone power source relying solely on human activity for wearable electronics.

Industrial processes and the smart grid: overcoming the variability of renewables by using built-in process storage and intelligent control strategies

Manufacturers are facing pressure to reduce electricity costs. Onsite renewable energy generation may be a solution, but its high capital cost and intermittent power generation limit its use. Grid-responsive smart manufacturing could effectively incorporate renewables in industrial processes. This study integrates grid-responsive smart manufacturing with renewables on an industrial plant scale and demonstrates both a favourable economic and environmental outcome. A user-friendly decision-aid model for energy management is provided to manufacturers. A case study shows how solar panels, industrial batteries, smart pumping strategies, and various combinations of those elements can save on electricity costs. Dynamic simulation results demonstrate that grid-responsive smart manufacturing can effectively lower peak demand. The economic results show that grid-responsive smart manufacturing and renewables synergistically optimise cost reductions. The solar coupled with smart pumping scenario shows annual cost savings of $755,200, accounting for 4.6% of the total electricity cost. Smart pumping alone saves $371,900 annually with a 0.7-year payback period, demonstrating how the manufacturing sector can utilise its own processes in load shifting. This study supports that incorporating grid-responsive smart manufacturing with renewables can effectively reduce electricity costs and emissions for industry. Abbreviations: e: Equivalent; GHG: Greenhouse gas; PBP: Payback period; PV: Photovoltaics; SP: Setpoint; VFD: Variable speed drives

Research on Coordinated Control Strategy of Thermal Heating and Melting Depth of Steam Heating and Melting Salt Reservoir

The implementation of the upgrading of the national coal electric power unit has provided a clear proposal to promote the clean and low-carbon transformation of the power industry. With the power of large-scale intermittent renewable energy and power generation, the electric crew should be flexible enough to adjust resources to achieve a depth of 35% THA. This article aims to propose a heat extracting and heat storage system for fire power plants, to realize the coordinated control strategy of the deep peak, and to explore the coordinated control strategy of the steam–molten salt heat exchanger, molten salt and water exchanger, and the turbine’s main control. The simulation results reveal that the coordinated control of the steam–molten salt heat exchanger, molten salt and water heat exchanger, and steam turbine control could reduce the depth of the fire power unit by 10% THA. The output power response speed of the thermal power unit is enhanced by utilizing the heat turbine, which could effectively enhance the output power response speed of the thermal power unit and increase the output power response speed pertinent to 302.55 s by 75.60%.

Development of a reliable simulation framework for techno-economic analyses on green hydrogen production from wind farms using alkaline electrolyzers

The present study investigates the feasibility of coupling the intermittent electric power generation from a wind farm with alkaline electrolyzers to produce green hydrogen. A physically accurate model of commercial electrolytic modules has been first developed, accounting for conversion efficiency drop due to modules’ cool down, effects of shutdowns due to the intermittence of wind power, and voltage degradation over the working time frame. The model has been calibrated on real modules, for which industrial data were available. Three commercial module sizes have been considered, i.e., 1, 2 and 4 MW. As a second step, the model has been coupled with historical power datasets coming from a real wind farm, characterized by a nominal installed power of 13.8 MW. Finally, the model was implemented within a sizing algorithm to find the best combination between the actual wind farm power output and the electrolyzer capacity to reach the lowest Levelized Cost Of Hydrogen (LCOH) possible. To this end, realistic data for the capital cost of the whole system (wind farm and electrolyzers) have been considered, based on industrial data and market reports, as well as maintenance costs including both periodic replacements of degraded components and periodic maintenance. Simulations showed that, if the right sizing of the two systems is made, competitive hydrogen production costs can be achieved even with current technologies. Bigger modules are less flexible but, by now, considerably cheaper than smaller ones. A future economy of scale in alkaline electrolyzers is then needed to foster the diffusion of the technology.

Perovskite quantum dot-based tandem triboelectric-solar cell for boosting the efficiency and rain energy harvesting

Silicon (Si) solar cell efficiently convert solar energy into electricity under the sun illumination, but its inherent intermittent power generation due to diurnal and seasonal cycle has usually allowed for the requirement for alternative power sources. Owing to the abundance, diversity and availability, rain drop energy is one of the most promising green energy sources. Hence, hybrid energy harvesting could be a promising strategy of achieving preferential hybrid performances without sacrificing the advantages in individual devices. Herein, we proposed a hybrid solar and rain energy harvesting device featuring the integration of triboelectric nanogenerator (TENG) top-cell and a crystalline Si solar cell bottom-cell by laminating perovskite quantum dots-embedded polydimethylsiloxane composite film (PQDP) onto the Si solar cell, which endows enhanced electrical output performances under rainy days. To overcome the limitation of low external quantum efficiency (EQE) in the ultraviolet region of the Si solar cell, the PCE of the hybrid solar cell is significantly enhanced from 18.47% of bare Si solar cell to 20.16% owing to the anti-reflection effect of PQDP film and the down-conversion property of CsPbBr3 QDs doped with Ce3+ ions. Meanwhile, this hybrid solar cell presents excellent durability after continuous measurement over 7 days. This tandem hybrid solar cell provides a promising strategy to simultaneously scavenging the solar and rain energy and boosting the power conversion efficiency by introducing the perovskite QDs without affecting the efficiency of bare Si solar cell.

Towards a Power Production from 100% Renewables: The Italian Case Study

The need to reduce greenhouse gas emissions is driving many actions to decarbonize the most impactful sectors. Among these, the energy sector accounts for almost one third of emissions. Increasing the penetration of renewable energy in the energy mix could easily reduce the emissions of this sector. Theoretically, the target to aim for would be 100% renewable energy production. However, the variable nature of power production from photovoltaic and wind systems, which are expected to play a key role in the energy transition, may pose several limitations to the effective penetration of renewable energy. Many concerns arise when one considers the large diffusion of renewable energy that would be required to meet green targets, and the operating conditions of other systems in charge of compensating for renewable energy variations. This study aims to investigate the potential impact of an increase in the amount of renewable energy installed in a country, particularly in Italy. A simplified approach has been used, based on the assumption of knowing the hourly demand and power generation mix, and multiplying the intermittent power generation by a certain factor. Although not accurate, this approach allows the authors to highlight some critical aspects regarding the potential surplus of renewable energy and the operating conditions of other energy sources. The results of this study may provide a useful basis for a preliminary system evaluation, in particular to assess the feasibility of surplus recovery and the operability of residual generation systems. In addition, it may be easily replicated in other countries for similar estimations.

Local Power Distribution—A Review of Nanogrid Architectures, Control Strategies, and Converters

Environmental issues and the global need to extend sustainable access to electricity have fostered a huge amount of research in distributed generation by renewables. The challenges posed by the widespread deployment of distributed generation by renewables, such as intermittent power generation, low inertia, the need for energy storage, etc., call for the development of smart grids serving specific local areas or buildings, referred to as microgrids and nanogrids, respectively. This has led in the last decades to the proposal and actual implementation of a wide variety of system architectures and solutions, and along with that the issue of the power converters needed for interfacing the AC grid with DC micro- or nanogrids, and for DC regulation within the latter. This work offers an overview of the state of the art of research and application of nanogrid architectures, control strategies, and power converter topologies.

Enhanced Situational Awareness for DER-Rich Distribution Systems Under Pre- and Post-Outage

Situational awareness (SA) is critical to properly operating active power distribution systems during normal and outage conditions. Appropriate SA tools should provide an accurate estimate of system voltage and current variables, the operational network topology, and the power injections from distributed energy sources (DERs), including behind-the-meter (BTM) photovoltaics (PVs). Obtaining an accurate SA, especially by estimating network topology and gross load demand, is increasingly challenging with the proliferation of BTM DERs with intermittent power generation. Moreover, the SA required for distribution system restoration is even more challenging after a medium to a prolonged outage due to inaccurate estimates of cold load pickup (CLPU) and switch statuses. This paper proposes an integrated real-time model update (RTMU) module for SA enhancement to help distribution system operators (DSOs) understand the power system conditions in dynamic and DER-rich environments. The proposed RTMU consists of several modules to obtain the required level of SA for operational decision-making. It includes estimators for a) BTM PV power, b) network topology, and c) CLPU. The proposed approaches leverage multiple data resources, deep learning approaches, and domain knowledge of the power system to provide the required level of SA to DSOs. The dependencies among these modules are actively leveraged to enhance SA under normal conditions and during power outages. We demonstrate and analyze the performance of the proposed RTMU on a modified IEEE 123-node feeder and a utility distribution system from the Western United States.