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Conventional energy resource problems made power system planners apply more renewable energy sources (RESs) in the power system. RESs have a significant impact on fulfilling the world's energy demand, protecting the environment at least in the exploration phase, and providing energy reliability as well as security for the new generation of power grids. In this regard, wind power generation is considered a promising solution. However, by increasing the share of wind power generation units in the power sector, some challenges, such as voltage problems, transient stability, increasing system losses, and line congestions can occur. Consequently, an alternative solution is required to accelerate wind power units' connections to the grid by preventing the problems caused by the penetration of wind power. In this regard, Flexible Alternating Current Transmission Systems (FACTS) devices can play an important role while they can improve power grid dynamic performance. This paper presents a review of the optimization techniques applied to accelerate the penetration of wind energy by exploiting FACTS devices. The topic identifies the objectives and optimization formulations, as well as schemes and models available in the published literature. This survey gives noteworthy insights to the researchers to cover available solutions in these regards. The literature in the field is summarized in different aspects, such as FACTS technology, objective functions, constraints, optimization methods, planning or operation purposes, and load flow types. At each aspect, a comparison is derived to recognize the most important issues. The result of the review is the finding of future work and contributions which can be done in the field of wind energy integration acceleration using FACTS devices and the output of the paper can be used as a guide for researchers to find a new path for research in this area. [ABSTRACT FROM AUTHOR]

Based on the concept of sustainable development, to promote the development and application of renewable energy and enhance the capacity of renewable energy consumption, this paper studies the design and optimization of renewable energy hydrogen production systems. For this paper, six different scenarios for grid-connected and off-grid renewable energy hydrogen production systems were designed and analyzed economically and technically, and the optimal grid-connected and off-grid systems were selected. Subsequently, the optimal system solution was optimized by analyzing the impact of the load data and component capacity on the grid dependency of the grid-connected hydrogen production system and the excess power rate of the off-grid hydrogen production system. Based on the simulation results, the most matched load data and component capacity of different systems after optimization were determined. The grid-supplied power of the optimized grid-connected hydrogen production system decreased by 3347 kWh, and the excess power rate of the off-grid hydrogen production system decreased from 38.6% to 10.3%, resulting in a significant improvement in the technical and economic performance of the system. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) sources play a special role in the operation of active energy networks. The microgrid (MG) is known as a suitable substrate for the development and installation of DGs. However, the future of energy distribution networks will consist of more interconnected and complex MGs, called multi-microgrid (MMG) networks. Therefore, energy management in such an energy system is a major challenge for distribution network operators. This paper presents a new energy management method for the MMG network in the presence of battery storage, renewable sources, and demand response (DR) programs. To show the performance of each connected MG's inefficient utilization of its available generation capacity, an index called unused power capacity (UPC) is defined, which indicates the availability and individual performance of each MG. The uncertainties associated with load and the power output of wind and solar sources are handled by employing the chance-constrained programming (CCP) optimization framework in the MMG energy management model. The proposed CCP ensures the safe operation of the system at the desired confidence level by involving various uncertainties in the problem while optimizing operating costs under Mixed-Integer Linear Programming (MILP). The proposed energy management model is assessed on a sample network concerning DC power flow limitations. The procured power of each MG and power exchanges at the distribution network level are investigated and discussed. [ABSTRACT FROM AUTHOR]

Introduction: Energy system simulation modeling plays an important role in understanding, analyzing, optimizing, and guiding the change to sustainable energy systems. Objectives: This review aims to examine energy system simulation modeling, emphasizing its role in analyzing and optimizing energy systems for sustainable development. Methods: The paper explores four key simulation methodologies; Agent-Based Modeling (ABM), System Dynamics (SD), Discrete-Event Simulation (DES), and Integrated Energy Models (IEMs). Practical applications of these methodologies are illustrated through specific case studies. Results: The analysis covers key components of energy systems, including generation, transmission, distribution, consumption, storage, and renewable integration. ABM models consumer behavior in renewable energy adoption, SD assesses long-term policy impacts, DES optimizes energy scheduling, and IEMs provide comprehensive sector integration. Case studies demonstrate the practical relevance and effectiveness of these models in addressing challenges such as data quality, model complexity, and validation processes. Conclusions: Simulation modeling is essential for addressing energy challenges, driving innovation, and informing policy. The review identifies critical areas for improvement, including enhancing data quality, refining modeling techniques, and strengthening validation processes. Future directions emphasize the continued importance of simulation modeling in achieving sustainable energy systems. [ABSTRACT FROM AUTHOR]

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model's superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system's ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures. [ABSTRACT FROM AUTHOR]

In the context of power system decarbonization, the demand-side strategy for increasing the share of renewable energy is studied for two constrained energy systems. This strategy, which is currently widely suggested in policies on the energy transition, would impact consumer behavior. Despite the importance of studying the latter, the focus here is on decisions regarding the type, location, and timeframe of implementing the related measures. As such, solutions must be assessed in terms of cost and feasibility, technological learning, and by considering geographical and environmental constraints. Based on techno-economic optimization, in this paper we analyze the evolution of the power system and elaborate plausible long-term trajectories in the energy systems of two European islands. The case studies, Procida in Italy and Hinnøya in Norway, are both electrically connected to the mainland by submarine cables and present issues in their power systems, which are here understood as relatively isolated power systems. Renewable energy integration is encouraged by legislative measures in Italy. Although not modeled here, they serve as a backbone for the assumptions of increasing these investments. For Procida, rooftop photovoltaics (PV) coupled with energy storage are integrated in the residential, public, and tertiary sectors. A price-based strategy is also applied reflecting the Italian electricity tariff structure. At a certain price difference between peak and off-peak, the electricity supply mix changes, favoring storage technologies and hence decreasing imports by up to 10% during peak times in the year 2050. In Norway, renewable energy resources are abundant. The analysis for Hinnøya showcases possible cross-sectoral flexibilities through electrification, leading to decarbonization. By fine-tuning electric vehicle charging tactics and leveraging Norway's electricity pricing model, excess electricity demand peaks can be averted. The conclusions of this double-prospective study provide a comparative analysis that presents the lessons learnt and makes replicability recommendations for other territories. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive survey of recent literature on European energy system modeling and analysis with special focus on grid development. Spanning the years from 2013 to 2023, we analyze 59 selected articles, organizing them by geographical scope, grid expansion strategies, research focus, and methodology. Additionally, we provide an overview of established and recurring frameworks, including ELMOD, EMPIRE, AnyMOD, LIMES, TIMES, FlexPlan, PyPSA, REMix, and Balmorel. Further, we elaborate on the recent trends in research and modeling. Based on our observations, we propose avenues for future research. For instance, considering recent changes in the geopolitical environment, we suggest shifting the geographical research focus from the North Sea region to the Central and Eastern European regions. Other suggestions include investigating grid development under imperfect market competition, merging the study of grid development with sector coupling, and increasing the focus on blue hydrogen, which appear to not receive much focus, as opposed to green hydrogen. Overall, this work may serve as a useful resource for newcomers to grid-related research and a practical guide for seasoned researchers in the field. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal sizing of Hybrid Renewable Energy Systems (HRESs), encompassing wind, solar, and battery systems, with the aim of delivering reliable performance at a reasonable cost. The focus is on mitigating unscheduled outages on the national grid in Iraq. The proposed On–off-grid HRES method is implemented using MATLAB and relies on an iterative technique to achieve multi-objectives, balancing reliability and economic constraints. The optimal HRES configuration is determined by evaluating various scenarios related to energy flow management, electricity prices, and land cover effects. Consumer requirements regarding cost and reliability are factored into a 2D optimization process. A battery model is developed to capture the dynamic exchange of energy among different renewable sources, battery storage, and energy demands. A detailed case study across fifteen locations in Iraq, including water, desert, and urban areas, revealed that local wind speed significantly affects the feasibility and efficiency of the HRES. Locations with higher wind speeds, such as the Haditha lake region (payback period: 7.8 years), benefit more than urban areas (Haditha city: payback period: 12.4 years). This study also found that not utilizing the battery, particularly during periods of high electricity prices (e.g., 2015), significantly impacts the HRES performance. In the Haditha water area, for instance, this technique reduced the payback period from 20.1 to 7.8 years by reducing the frequency of charging and discharging cycles and subsequently mitigating the need for battery replacement. [ABSTRACT FROM AUTHOR]

A wind power forecast is an useful support tool for planning and operating wind farm production, facilitating decisions regarding maintenance and load share. This paper presents an evaluation of a cooperative method, which uses a time series pre-processing strategy, artificial neural networks, and multi-objective optimization to forecast wind power generation. The proposed approach also evaluates the accuracy of the hybridization of variational mode decomposition (VMD) with bootstrap aggregation and extreme learning machine model for forecasting very short and short-term wind power generation. Multi-objective strategy aggregates the VMD-based components and obtains the final forecasting. The results imply that the presented algorithm has better forecasting performance compared to bootstrap stacking, other machine learning approaches, and statistical models, with a reduction of root mean squared error of approximately 12.76%, 25.25%, 31.91%, and 34.76%, respectively, for out-of-sample predictions. The forecasting results indicate that the presented approach can improve generalizability and accuracy in cases of very short and short-term wind energy generation. [ABSTRACT FROM AUTHOR]

This study examines the research climate on machine learning applications in renewable energy (MLARE). Therefore, the publication trends (PT) and bibliometric analysis (BA) on MLARE research published and indexed in the Elsevier Scopus database between 2012 and 2021 were examined. The PT was adopted to deduce the major stakeholders, top-cited publications, and funding organizations on MLARE, whereas BA elucidated critical insights into the research landscape, scientific developments, and technological growth. The PT revealed 1218 published documents comprising 46.9% articles, 39.7% conference papers, and 6.0% reviews on the topic. Subject area analysis revealed MLARE research spans the areas of science, technology, engineering, and mathematics among others, which indicates it is a broad, multidisciplinary, and impactful research topic. The most prolific researcher, affiliations, country, and funder are Ravinesh C. Deo, National Renewable Energy Laboratory, United States, and the National Natural Science Foundation of China, respectively. The most prominent journals on the top are Applied Energy and Energies, which indicates that journal reputation and open access are critical considerations for the author's choice of publication outlet. The high productivity of the major stakeholders in MLARE is due to collaborations and research funding support. The keyword co-occurrence analysis identified four (4) clusters or thematic areas on MLARE, which broadly describe the systems, technologies, tools/technologies, and socio-technical dynamics of MLARE research. Overall, the study showed that ML is critical to the prediction, operation, and optimization of renewable energy technologies (RET) along with the design and development of RE-related materials. [ABSTRACT FROM AUTHOR]

Various strategies for optimizing the renewable energy system size exist, and they vary depending on the system design. In this paper, a standalone hybrid renewable energy system composed of a photovoltaic power source, wind turbines, storage, and diesel generators was considered. Linear programming was used to optimize the system in terms of multiple objectives. Bender's decomposition, lexicographic optimization, and epsilon constraint are among the linear programming techniques employed. The main objective function is to keep excess power to a minimum while also keeping unsatisfied demand to a minimum. This paper considers a full-year time span, optimizes household self-consumption, reduces curtailed power, maximizes local use of renewable generated power, lowers operational costs, lowers CO2 emissions, considers multiple renewable energy sources, considers system sizing as an objective, and incorporates the net present worth value of the components into the sizing. The aforementioned objectives and considerations have never been brought together yet in the literature. Furthermore, using a 365-day span is more comprehensive than using a 24-h span, which is typically used to limit the size of the linear programming issue. Therefore, in terms of integrated objectives, cost analysis, considerations, and timeframe, the proposed method is considered one of the most comprehensive. The ability to adjust the feed in limit to the grid and the change in electricity price during the day are also considered in this work. [ABSTRACT FROM AUTHOR]

This review paper provides a summary of methods in which artificial intelligence (AI) techniques have been applied in the management of variable renewable energy (VRE) systems, and an outlook to future directions of research in the field. The VRE types included are namely solar, wind and marine varieties. AI techniques, and particularly machine learning (ML), have gained traction as a result of data explosion, and offer a method for integration of multimodal data for more accurate forecasting in energy applications. The VRE management aspects in which AI techniques have been applied include optimized power generation forecasting and integration of VRE into power grids, including the aspects of demand forecasting, energy storage, system optimization, performance monitoring, and cost management. Future directions of research in the applications of AI for VRE management are proposed and discussed, including the issue of data availability, types and quality, in addition to explainable artificial intelligence (XAI), quantum artificial intelligence (QAI), coupling AI with the emerging digital twins technology, and natural language processing. [ABSTRACT FROM AUTHOR]

Energy storage is one of the most important components of microgrids with non-dispatchable generators and can offer both energy and flexibility services when the microgrid operates in grid-connected mode. This paper proposes a scenario-based stochastic optimization model that can be used to determine the energy and flexibility dispatch of a residential microgrid with solar and stationary battery systems. The objective of the model is to minimize the expected energy and peak power cost as well as the battery aging cost, while maximizing the expected revenue from flexibility. The formulated stochastic optimization problem is solved in rolling horizon with the uncertainty model being dynamically updated to consider the most recent forecast profiles for solar power and electricity demand. The benefits of the proposed approach were demonstrated by simulating the daily operation of a real building. The results showed that the estimated flexibility was successfully dispatched yielding an economic value of at least 7% of the operation cost of the building microgrid. The model can be used by flexibility providers to assess their flexibility and design a bidding strategy as well as by system operators to design incentives for flexibility providers. [ABSTRACT FROM AUTHOR]

Recently, due to the restructuring of power systems and the high penetration level of local renewables, distribution systems have encountered with the complexity of power management. Therefore, the modern systems would be operated in a multi-agent structure which facilitates the power management as well as privacy protections of independent entities. In this structure, the distribution system is assumed to compose of several agents who independently schedule their local resources in order to maximize their own profits. Consequently, this paper provides an efficient peer-to-peer (P2P) active power management framework in a multi-agent distribution system while considering network constraints (i.e., line loadings and losses). In this context, in the proposed P2P scheme, the distribution system operator (DSO) model the network constraints in the form of line-usage costs within the transactive signals. Respectively, the developed transactive control signals enable the DSO to model the power loss as well as alleviate the congestion in the grid. Therefore, the agents automatically consider the network constraints in their power transactions management procedure without any direct interferences of the DSO in their resource scheduling. Finally, the proposed model is implemented on the modified-IEEE-37-bus-test system in order to investigate its effectiveness in the energy management of multi-agent systems. [ABSTRACT FROM AUTHOR]

Energy is essential to sustain life and development and to fulfill daily requirements. A clean source of energy like the Solar Chimney Power Plant (SCPP) is essential or hybridized with conventional sources to meet the demand. Also, it will provide energy during the night and it will reduce the need and expense of physical batteries in comparison to the photovoltaic energy storage system. In this paper, a mathematical study has been done for the generation of 10 MW power from the SCPP. This paper optimized the core parameter of SCPP such as chimney height and collector radius under different operating conditions. It has been evaluated on the basis of data produced by the developed mathematical model. The current research work highlights the modern application of state‐of‐the‐art Deep Learning (DL) techniques that is, Functional Link Convolutional Neural Network (FLCNN) to map developed mathematical models. The proposed FLCNN is compared with various other Machine Learning algorithms including Support Vector Regression, K‐Nearest Neighbors, Random Forest, Naïve Bayes, and Multilayer Perceptron. Proposed FLCNN performed efficiently and achieved 91% and 93% of Adjusted R2 score for predicting values of collector radius and chimney height, respectively, which is comparable to other ML algorithms. [ABSTRACT FROM AUTHOR]

This paper proposed a model and method for multi-area economic/emission dispatch in the presence of renewable energy sources (RESs) and electric vehicles (EVs). The economic/Emission load dispatch model includes uncertain modeling of wind and solar renewable energy sources along with an uncertain model of electric vehicles and network demand with the objective of reducing economic and pollution costs of generation units. In this paper, the day-ahead electricity market is modeled on a 24-hour time period. Since it will be very difficult to solve the problem by increasing the time period and the number of generation units as well as considering the uncertainty, a meta-heuristic algorithm with the ability to solve large-scale problems and Investigate the concept of affordability based on the application of a thorough researched fast convergence has been presented. A modified particle swarm optimization (MPSO) algorithm has been proposed to solve the proposed problem due to the rapid convergence of hard optimization problems and achieve the optimal global result. A case study with four areas has been considered for the analysis of the proposed model and method, and two different approaches have been presented to illustrate the multi-area economic/emission dispatch and the results indicated the efficiency of the proposed model and method. [ABSTRACT FROM AUTHOR]

The unavailability of a constant power supply has been a major problem in remote communities in Africa as it impedes the proper operation of healthcare facilities in these locations. This has deprived inhabitants of free access to good healthcare services, thereby resulting in an increase in maternal and child mortality rates in rural communities in Africa. Therefore, in order to address this problem and render a life-saving intervention for rural dwellers and to also improve their healthcare service delivery, this paper focuses on the optimal configuration of a hybrid energy system for the rural electrification of community healthcare facilities. It presents an analysis of an off-grid hybrid energy system comprised of diesel generators, wind turbines and solar PV with a battery storage system to meet the energy demand of healthcare facilities in a remote community in Nigeria. In this study, hybrid energy systems are considered owing to the high reliability and availability of the intensity of solar radiation and wind speeds in Nigeria. An optimization model was developed which seeks to minimize the operational cost of hybrid energy systems. The proposed model was implemented using four case studies and solved using algebraic modeling language. The results obtained from the sensitivity analysis indicate that the configuration that includes solar PV, wind turbines, a battery storage system and a diesel generator provides the optimum power required for a rural healthcare center with a suitable energy cost and emission reduction from the system of diesel generators. [ABSTRACT FROM AUTHOR]

A microgrid is considered to be a smart power system that can integrate local renewable energy effectively. However, the intermittent nature of renewable energy causes operating pressure and additional expense in maintaining the stable operation by the energy management system in a microgrid. The structure of multi-microgrids provides the possibility to construct flexible and various energy trading framework. In this paper, in order to reduce the adverse effects of uncertain renewable energy output, a distributed robust model predictive control (DRMPC)-based energy management strategy is proposed for islanded multi-microgrids. This strategy balances the robustness and economy of single-microgrid system operation by combining the advantages of robust optimization and model predictive control, while coping with the uncertainty of renewable energy sources. Furthermore, a dynamic energy trading market is formed among microgrids, which can enhance the overall economy of the multi-microgrids system. Simulation results verify the feasibility of the proposed DRMPC strategy. [ABSTRACT FROM AUTHOR]

A virtual energy storage (VES) model is proposed in this paper to accommodate renewable energy under a special market regulation. Such VESs can provide or consume electricity to the main power grid under the premise that the daily net electricity energy is balanced. Furthermore, a multi-stage distributionally robust optimization (MSDRO) model is set up in this paper to address the temporal uncertainties in the day-ahead economic dispatch model. Compared with the traditional two-stage distributionally robust optimization, the proposed multi-stage approach provides more flexibilities so that the decision variables can be adjusted at each time period, leading to a complex nested formulation. To efficiently solve the MSDRO model, a stochastic dual dynamic programming method is employed to decompose the original large-scale optimization model into several sub-problems in the stages, as two steps: forward pass and backward pass. In the forward pass, the expected cost-to-go function is approximated by piecewise-linear functions and then several samples are used to generate a lower bound; the backward pass will generate Benders’ cuts at each stage from the solution of the forward pass. The forward and backward passes are performed iteratively until the convergence is reached. Numerical results on an IEEE 118-bus system and a practical power system in China verify the proposed method. [ABSTRACT FROM AUTHOR]

Recently, renewable energy resources (RESs) have been utilized to supply electricity to remote areas, instead of the conventional methods of electrical energy production. In this paper, the optimal design of a standalone hybrid RES comprising photovoltaic (PV), wind turbine (WT), and biomass sources as well as an energy storage system, such as a hydro-pumped storage system, is studied. The problem of the optimal sizing of the generating units in the proposed energy system is formulated as an optimization problem and the algorithms heap-based optimizer (HBO), grey wolf optimizer (GWO), and particle swarm optimization (PSO) are applied to achieve the optimal sizing of each component of the proposed grid-independent hybrid system. The optimization problem is formulated depending on the real-time meteorological data of the Ataka region on the Red Sea in Egypt. The main goal of the optimization process is to minimize the cost of energy (COE) and the loss of power supply probability (LPSP), while satisfying the constraints of system operation. The results clarify that the HBO algorithm succeeded in obtaining the best design for the selected RE system with the minimum COE of 0.2750 USD/kWh and a net present cost (NPC) of USD 8,055,051. So, the HBO algorithm has the most promising performance over the GWO algorithm in addressing this optimization problem. [ABSTRACT FROM AUTHOR]

Microgrid technology has recently gained global attention over increasing demands for the inclusion of renewable energy resources in power grids, requiring constant research and development in aspects such as control, protection, reliability, and management. With an ever-increasing scope for maximizing renewable energy output, there is also a need to reduce the curtailment of power on both the generation and demand sides by increasing forecasting accuracies and using resources more effectively. This paper proposes a dual-stage dispatch employing a novel "split-horizon" strategy, in a bid to enhance energy management in a standalone microgrid. The split-horizon is essentially the considered time horizon split into equal operational periods of the dual-stage dispatch. The proposed strategy utilizes a custom-designed novel variant of the inertia-weight-based particle swarm optimization (PSO), termed customized PSO, to perform the optimal schedule and dispatch operation by benefitting from the simplicity of PSO and customization as per the considered objectives. A modified IEEE 34-node test system is derived into a standalone microgrid with added distributed energy resources to test the proposed strategy, while another standalone microgrid, a modified IEEE 69-node test feeder, is also considered for scalability. Furthermore, the validation of the strategy is performed appropriately with a case study while also validating the proposed optimization algorithm. It is observed that the proposed energy management strategy provides approximatelya 7% reduction in costs. [ABSTRACT FROM AUTHOR]

Decarbonization of the power sector in China is an essential aspect of the energy transition process to achieve carbon neutrality. The power sector accounts for approximately 40% of China's total CO2 emissions. Accordingly, collaborative optimization in power generation expansion planning (GEP) simultaneously considering economic, environmental, and technological concerns as carbon emissions is necessary. This paper proposes a collaborative mixedinteger linear programming optimization approach for GEP. This minimizes the power system's operating cost to resolve emission concerns considering energy development strategies, flexible generation, and resource limitations constraints. This research further analyzes the advantages and disadvantages of current GEP techniques. Results show that the main determinants of new investment decisions are carbon emissions, reserve margins, resource availability, fuel consumption, and fuel price. The proposed optimization method is simulated and validated based on China's power system data. Finally, this study provides policy recommendations on the flexible management of traditional power sources, the market-oriented mechanism of new energy sources, and the integration of new technology to support the attainment of carbon-neutral targets in the current energy transition process. [ABSTRACT FROM AUTHOR]

The increased interest in renewable-based microgrids imposes several challenges, such as source integration, power quality, and operating cost. Dealing with these problems requires solving nonlinear optimization problems that include multiple linear or nonlinear constraints and continuous variables or discrete ones that require large dimensionality search space to find the optimal or sub-optimal solution. These problems may include the optimal power flow in the microgrid, the best possible configurations, and the accuracy of the models within the microgrid. Metaheuristic optimization algorithms are getting more suggested in the literature contributions for microgrid applications to solve these optimization problems. This paper intends to thoroughly review some significant issues surrounding microgrid operation and solve them using metaheuristic optimization algorithms. This study provides a collection of fundamental principles and concepts that describe metaheuristic optimization algorithms. Then, the most significant metaheuristic optimization algorithms that have been published in the last years in the context of microgrid applications are investigated and analyzed. Finally, the employment of metaheuristic optimization algorithms to specific microgrid issue applications is reviewed, including examples of some used algorithms. These issues include unit commitment, economic dispatch, optimal power flow, distribution system reconfiguration, transmission network expansion and distribution system planning, load and generation forecasting, maintenance schedules, and renewable sources max power tracking. [ABSTRACT FROM AUTHOR]

Deployment of photovoltaic (PV) systems has recently been encouraged for large-scale and small-scale businesses in order to meet the global green energy targets. However, one of the most significant hurdles that limits the spread of PV applications is the dust accumulated on the PV panels' surfaces, especially in desert regions. Numerous studies sought the use of cameras, sensors, power datasets, and other detection elements to detect the dust on PV panels; however, these methods pose more maintenance, accuracy, and economic challenges. Therefore, this paper proposes an intelligent system to detect the dust level on the PV panels to optimally operate the attached dust cleaning units (DCUs). Unlike previous strategies, this study utilizes the expanded knowledge and collected data for solar irradiation and PV-generated power, along with the forecasted ambient temperature. An expert artificial intelligence (AI) computational system, adopted with the MATLAB platform, is utilized for a high level of data prediction and processing. The AI was used in this study in order to estimate the unprovided information, emulate the provided measurements, and accommodate more input/output data. The feasibility of the proposed system is investigated using actual field data during all possible weather conditions. [ABSTRACT FROM AUTHOR]

This paper presents a thermoeconomic analysis of a polygeneration system based on solar-assisted desiccant cooling. The overall plant layout supplies electricity, space heating and cooling, domestic hot water, and freshwater for a residential building. The system combines photovoltaic/thermal collectors, photovoltaic panels, and a biomass boiler coupled with reverse osmosis and desiccant air conditioning. The plant was modeled in TRNSYS and simulated for 1 year. A parametric study defined the system's setup. A thermoeconomic optimization determined the set of parameters that minimize the simple payback period. The optimal structure showed a total energy efficiency of 0.49 for the solar collectors and 0.16 for the solar panels. The coefficient of performance of the desiccant air conditioning was 0.37. Finally, a sensitivity analysis analyzed the influence of purchase electricity and natural gas costs and the electricity sell-back price on the system. The optimum simple payback was 20.68 years; however, the increase in the energy cost can reduce it by up to 85%. [ABSTRACT FROM AUTHOR]

Demand for electricity is constantly increasing, and production is facing new constraints due to the current world situation. An alternative to standard energy production methodologies is based on the use of renewable sources; however, these methodologies do not produce energy consistently due to weather factors. This results in a significant commitment of the user who must appropriately distribute loads in the most productive time slots. In this paper, a comparison is made between two methods of predicting solar energy production, one statistical and the other meteorological. For this work, a system capable of presenting the scheduling of household appliances is tested. The system is able to predict the energy consumption of the users and the energy production of the solar system. The system is tested using data from three different users, and the mean percentage of consumption reduction is about 77.73%. This is achieved through optimized programming of appliance use that also considers user comfort. [ABSTRACT FROM AUTHOR]

The transportation sector is one among the key sources of greenhouse gas emissions (GHGs) leading to climate change and global warming. Energy transition through electrified transportation is one of the solutions to tackle the issues. Electric vehicles (EVs) offer significant environmental and economic advantages against the conventional Internal Combustion Engine (ICE) vehicles. EVs are called mobility loads and their connectivity to the utility grid for charging is unpredictable. The large penetration of such unpredictable loads into the utility grid will lead to undesirable impacts on the utility service. This paper highlights the importance of managing and optimizing the charging schedules. The optimization of EV charging has diverse aspects, and the perspectives of EV charging differ among consumers, aggregators, and utility services. Proper planning and management of EV charging is essential to achieve harmony amongst these stakeholders. A comprehensive review on the objectives of electric vehicle charging optimization from various perspectives is presented and discussed in this paper. EV charging optimization techniques including mathematical programming, meta heuristics algorithms and machine learning techniques are explored. The main objectives, constraints, strength, and limitations of different charging optimization techniques are analyzed in detail. A brief discussion on the communication strategies for data exchange in EV charging framework is presented and the need for a communication security constrained EV charging scheduling is also emphasized.

The transition towards net-zero emissions is inevitable for humanity's future. Of all the sectors, electrical energy systems emit the most emissions. This urgently requires the witnessed accelerating technological landscape to transition towards an emission-free smart grid. It involves massive integration of intermittent wind and solar-powered resources into future power grids. Additionally, new paradigms such as large-scale integration of distributed resources into the grid, proliferation of Internet of Things (IoT) technologies, and electrification of different sectors are envisioned as essential enablers for a net-zero future. However, these changes will lead to unprecedented size, complexity and data of the planning and operation problems of future grids. It is thus important to discuss and consider High Performance Computing (HPC), parallel computing, and cloud computing prospects in any future electrical energy studies. This article recounts the dawn of parallel computation in power system studies, providing a thorough history and paradigm background for the reader, leading to the most impactful recent contributions. The reviews are split into Central Processing Unit (CPU) based, Graphical Processing Unit (GPU) based, and Cloud-based studies and smart grid applications. The state-of-the-art is also discussed, highlighting the issue of standardization and the future of the field. The reviewed papers are predominantly focused on classical imperishable electrical system problems. This indicates the need for further research on parallel and HPC approaches applied to future smarter grid challenges, particularly to the integration of renewable energy into the smart grid. [ABSTRACT FROM AUTHOR]

This work aims to find an optimal hybrid renewable system design using solar, wind energy, battery storage, thermal loads, thermal load controller (TLC), boiler, and a diesel generator (DG) for the considered site. The prefeasibility techno-economic analysis has been carried out using HOMER software to meet the load demand requirement of the village. For identifying winning system architecture, minimum net present cost (NPC), lowest cost of energy (COE), and the highest renewable fraction (RF) are used as the criteria. The obtained results show that the least cost-optimal hybrid system consists of 614kW-PV,850kW-WT,800kW-DG, 1212 no. of batteries and 591 kW converter along with TLC-2000kW, having minimum NPC: $5.48M, least COE: $0.272/kWh and highest RF:91.6% can provide 92% reliable power supply to onsite load demand of 4502.95kWh/day with 70% renewable sources in the considered site. This paper also presents a sensitivity analysis of the hybrid system with variations in load demand, diesel fuel price, project lifetime, and interest rate. [ABSTRACT FROM AUTHOR]

Improving the performance of distribution systems is one of the main objectives of power system operators. This can be done in several ways, such as network reconfiguration, system reinforcement, and the addition of different types of equipment, such as distributed generation (DG) units, shunt capacitor banks (CBs), and voltage regulators (VRs). In addition, the optimal use of renewable and sustainable energy sources (RSESs) has become crucial for meeting the increase in demand for electricity and reducing greenhouse gas emissions. This requires the development of techno-economic planning models that can measure to what extent modern power systems can host RSESs. This article applies a new optimization technique called RUN to increase hosting capacity (HC) for a rural Egyptian radial feeder system called the Egyptian Talla system (ETS). RUN relies on mathematical concepts and principles of the widely known Runge–Kutta (RK) method to get optimal locations and sizes of DGs, CBs, and VRs. Furthermore, this paper presents a cost-benefit analysis that includes fixed and operating costs of the compensators (DGs, CBs, and VRs), the benefits obtained by reducing the power purchased from the utility, and the active power loss. The current requirements of Egyptian electricity distribution companies are met in the formulated optimization problem to improve the HC of this rural system. Uncertain loading conditions are taken into account in this study. The main load demand clusters are obtained using the soft fuzzy C-means clustering approach according to load consumption patterns in this rural area. The introduced RUN optimization algorithm is used to solve the optimal coordination problem between DGs, CBs, and VRs. Excellent outcomes are obtained with a noteworthy reduction in the distribution network power losses, improvement in the system's minimum voltage, and improvement of the loading capacity. Several case studies are investigated, and the results prove the efficiency of the introduced RUN-based methodology, in which the probabilistic HC of the system reaches 100% when allowing reverse power flow to the utility. In comparison, this becomes 49% when allowing reverse power to flow back to the utility. [ABSTRACT FROM AUTHOR]

Design of hybrid Nano-Grid system (HNGS) necessitates a careful thought to benchmark reliable and cost effective solutions for remote areas. Optimal sizing of HNGS using Honey Badger Algorithm (HBA) for futuristic residential area in Mahendragarh, Haryana, India is presented in this paper. The HNGS comprise combination of solar panels, wind turbines, storage batteries, diesel generator, inverter and residential load components. The primary objective functions considered in this paper are Cost of Electricity (COE) and Net Present Cost (NPC), in continuation these problems are stated as multi objective optimization issues. Numerous permutations of diverse resources were examined to assess parameters such as NPC, COE, battery storage, land utilization, emission and potential employment opportunities. HBA is almost independent of traps in the local minimum, therefore the results obtained are superior as compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Biogeography Basic Optimization (BBO), Improved Artificial Ecosystem Optimization (IAEO) and Grey Wolf Optimization (GWO) as well as HOMER software, in order to determine the quality of solution the proposed HBA algorithm has obtained. HNGS system can be readily and efficiently optimized using the suggested HBA algorithm a detailed sensitivity analysis is done to better understand the system behavior for a wider region use. • A well optimized HNGS design is cost-effective, highly reliable and simple system for remote areas. • An optimization problem is formulated and solved by means of Honey Badger algorithm. • The optimization aim is to find HNGS size in terms of number of individual sources which supplies demand successfully. • Outcomes show that HBA effectively optimizes HNGS by lowering the NPC and CoE, ensuring the electricity supply. • The HBA's results has been compared to GA, PSO, BBO, IAEO, and GWO, as well as HOMER software. • Sensitivity analysis has been used to explore the impact of weather and component parameters on COE and NPC. [ABSTRACT FROM AUTHOR]

The energy supply of healthcare facilities is of great importance under different circumstances. In this study, supplying the energy of a clinic using maximum renewable resources under normal and crisis conditions is examined. This paper is novel in that it designs an energy system specifically for times of crisis. The proposed clinic is located in two different regions in Iran. This paper considers a solar panel, wind turbine, battery, inverter, and controller for electricity generation from renewable resources, a steam boiler for heating needs, and a diesel generator as a backup system. Scenarios, including changes in the type of controller and the price of different parts, were examined. In the optimal scenario, where the clinic is in normal conditions in terms of patient acceptance, the net present cost and cost of energy were estimated to be $2.57 million and 0.0606 $/kWh for Rasht, and $3.09 million and 0.0732 $/kWh for Shiraz, respectively. In a new scenario, in a critical time of the COVID-19 outbreak, the net present cost and cost of energy were calculated to be $4.29 million and 0.0608$/kWh for Rasht, and $5.31 million and 0.0755 $/kWh for Shiraz, respectively. Also the clinic will generate an annual income of $0.12 million by selling excess energy produced in this scenario during normal conditions.

With the gradual increase in load demand due to population and economic growth, integrating renewable energy sources (RES) into the grid represents a solution for meeting load demand. However, integrating RES might change the power system type from radial to non-radial, where the current can flow forward and backward. Consequently, power system analysis methods must be updated. The impact on power systems includes changes in the load flow affecting the voltage level, equipment sizing, operating modes, and power system protection. Conventional power system protection methods must be updated, as RES integration will change the power flow results and the short circuit levels in the power system. With an RES contribution to short circuit, existing settings might experience missed coordination which will result in unnecessary tripping. This paper considers the impact of integrating renewable energy sources into power system protection on overcurrent time delay settings. A new method to upgrade/adjust time delay settings is developed utilizing genetic algorithm (GA) optimization. The proposed optimization method can be used to evaluate the impact of integrating RES on the exiting overcurrent setting, and can provide new settings without the need to replace existing protection devices when the short circuit is within equipment thermal limits. [ABSTRACT FROM AUTHOR]

The rise in demand for underwater wireless communication networks (UWCN) has been driven by the emergence of new applications including unmanned underwater vehicles, deep-sea exploration, maritime and underwater archaeology research, and diver communications. For various applications, underwater network lifetime must be long, since unlike terrestrial sensor networks, it is a major job to change/recharge node batteries in underwater environments. In this paper, we introduce a solution where critical nodes in a UWCN are periodically recharged by small renewable energy sources. Further, a caching mechanism is introduced to relieve critical nodes of heavy workload when their residual energies run low. By leveraging these two techniques of energy replenishment and content-caching, the network lifetime extension challenge is formulated as an optimization problem. The results indicate that our proposed solution offers up to 45% longer UWCN lifetime compared to existing solutions. [ABSTRACT FROM AUTHOR]

While the contribution of renewable energy technologies to the energy system is increasing, so is its level of complexity. In addition to new types of consumer systems, the future system will be characterized by volatile generation plants that will require storage technologies. Furthermore, a solid interconnected system that enables the transit of electrical energy can reduce the need for generation and storage systems. Therefore, appropriate methods are needed to analyze energy production and consumption interactions within different system constellations. Energy system models can help to understand and build these future energy systems. However, although various energy models already exist, none of them can cover all issues related to integrating renewable energy systems. The existing research gap is also reflected in the fact that current models cannot model the entire energy system for very high shares of renewable energies with high temporal resolution (15 min or 1-h steps) and high spatial resolution. Additionally, the low availability of open-source energy models leads to a lack of transparency about exactly how they work. To close this gap, the sector-coupled energy model (UCB-SEnMod) was developed. Its unique features are the modular structure, high flexibility, and applicability, enabling it to model any system constellation and can be easily extended with new functions due to its software design. Due to the software architecture, it is possible to map individual buildings or companies and regions, or even countries. In addition, we plan to make the energy model UCB-SEnMod available as an open-source framework to enable users to understand the functionality and configuration options more easily. This paper presents the methodology of the UCB-SEnMod model. The main components of the model are described in detail, i.e., the energy generation systems, the consumption components in the electricity, heat, and transport sectors, and the possibilities of load balancing. [ABSTRACT FROM AUTHOR]

Studies have shown that the role of energy storage systems in human life is increasing day by day. Therefore, this research aims to study the latest progress and technologies used to produce energy storage systems. It also discusses and compares the most recent methods used by researchers to model and optimize the size of these tools and evaluates the strengths and weaknesses of each. Investigations have shown that using energy storage systems in hybrid stand-alone power generation systems based on renewable energy increases the reliability of the power generation systems and increases their efficiency. It has also reduced the cost of transmitting the power grid to remote areas. Furthermore, this study showed that advances in energy storage technology in recent years have led to the development and promotion of clean microgrids. In addition, this review paper also addresses energy storage technology issues and proposes practical and applied solutions. [ABSTRACT FROM AUTHOR]

Renewable energy resources are deemed a potential energy production source due to their cost efficiency and harmless reaction to the environment, unlike non-renewable energy resources. However, they often fail to meet energy requirements in unfavorable weather conditions. The concept of Hybrid renewable energy resources addresses this issue by integrating both renewable and non-renewable energy resources to meet the required energy load. In this paper, an intelligent cost optimization algorithm is proposed to maximize the use of renewable energy resources and minimum utilization of non-renewable energy resources to meet the energy requirement for a nanogrid infrastructure. An actual data set comprising information about the load and demand of utility grids is used to evaluate the performance of the proposed nanogrid energy management system. The objective function is formulated to manage the nanogrid operation and implemented using a variant of Particle Swarm Optimization (PSO) named recurrent PSO (rPSO). Firstly, rPSO algorithm minimizes the installation cost for nanogrid. Thereafter, the proposed NEMS ensures cost efficiency for the post-installation period by providing a daily operational plan and optimizing renewable resources. State-of-the-art optimization models, including Genetic Algorithm (GA), bat and different Mathematical Programming Language (AMPL) solvers, are used to evaluate the model. The study's outcomes suggest that the proposed work significantly reduces the use of diesel generators and fosters the use of renewable energy resources and beneficiates the eco-friendly environment. [ABSTRACT FROM AUTHOR]

Smart campus microgrids are considered in this paper, with the aim of highlighting their applicability in the framework of the sustainable energy transition. In particular, the campus of the Hellenic Mediterranean University (HMU) in Heraklion, Crete, Greece, is selected as a case study to highlight the multiple campus microgrids' advantages. Crete represents an interesting insular power system case, due to the high renewable energy sources capacity and the large summer tourism industry. There is also a high density of university and research campuses, making the campus microgrid concept a promising solution for the energy transition and decarbonization of the island. In this sense, policy directions that could facilitate the development of the smart campus microgrid are also given, to motivate areas with similar characteristics. For the performed case study, the HMU microgrid is assumed to consist of PV systems, wind turbines, battery energy storage systems and EV chargers. The analysis explores the financial feasibility and environmental impact of such an investment through the optimal sizing of the systems under investigation, while a sensitivity analysis regarding the battery system cost is also performed. Apart from the financial benefits of the investment, it is evident that the main grid experiences a significant load reduction, with the microgrid acting as a RES producer for many hours, hence improving system adequacy. Moreover, it is shown that the location of HMU makes the investment more sustainable compared to other locations in northern Europe, such as Stockholm and London. The methodology and the derived results are expected to motivate such investments, especially in areas with high RES capacity and a high density of university and research campuses. [ABSTRACT FROM AUTHOR]

This paper proposes a holistic framework of data-driven distributionally robust joint chance constrained economic dispatch (ED) optimization, which seamlessly incorporates deep learning-based optimization for effective utilization of renewable energy in power systems. By leveraging a deep generative adversarial network (GAN), an f-divergence-based ambiguity set of wind power distributions is constructed as a ball centered around the probability distribution induced by a generator neural network. In particular, the GAN is well suited for capturing complicated spatial and temporal correlations of wind power. Based upon this ambiguity set, a distributionally robust joint chance constrained ED model is developed to hedge against distributional uncertainty present in multiple constraints, without assuming a perfectly known probability distribution. The proposed deep learning based ED optimization framework greatly mitigates the conservatism inflicting on distributionally robust individual chance constrained optimization. Theoretical a priori bound on the required number of synthetic wind power data generated by GAN is explicitly derived for the multi-period ED problem to guarantee a predefined risk level. The effectiveness and scalability of the proposed approach are demonstrated in the six-bus and IEEE 118-bus systems by comparing with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel liquefied air energy storage (LAES) system driven by wind energy and natural gas, integrated with a two‐stage supercritical carbon dioxide cycle is proposed and investigated. Three different sensible thermal energy storage (TES) systems are considered in this study to store the heat produced in the compressions stage and use it later to improve the performance. The proposed system is analyzed in terms of energy, exergy, exergo‐economic, and environmental impacts. A detailed economical and technical investigation is carried out to determine system hotspots, exergy destruction rates, and the performance of the system and subsystems. Finally, by considering ten design variables, the nondominated sorting genetic algorithm‐II (NSGA‐II) is employed to evaluate the optimal design solutions for double objective functions, including cost per unit of exergy and overall system exergy efficiency. Results indicate that for a 50 MW of discharging power, 80.75 MW of charging electricity and 1.22 kg/s of fuel are required. Additionally, the product cost per unit of exergy and the levelized costs are calculated as 32.02 $/GJ and 0.133 $/kWh, respectively. The results of the optimization study also show that for the optimal Pareto solution, the energy efficiency would be 52.2% while the exergy efficiency is 45.26%. [ABSTRACT FROM AUTHOR]

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Recently, implementation of Battery Energy Storage (BES) with photovoltaic (PV) array in distribution networks is becoming very popular in overall theworld. Integrating PV alone in distribution networks generates variable output power during 24-hours as it depends on variable natural source. PV can be able to generate constant output power during 24-hours by installing BES with it. Therefore, this paper presents a new application of a recent metaheuristic algorithm, called SlimeMould Algorithm (SMA), to determine the best size, and location of photovoltaic alone or with battery energy storage in the radial distribution system (RDS). This algorithm is modeled from the behavior of SMA in nature. During the optimization process, the total active power loss during 24-hours is used as an objective function considering the equality and inequality constraints. In addition, the presented function is based on the probabilistic for PV output and different types of system load. The candidate buses for integrating PV and BES in the distribution network are determined by the real power loss sensitivity factor (PLSF). IEEE 69-bus RDS with different types of loads is used as a test system. The effectiveness of SMA is validated by comparing its results with those obtained by other well-known optimization algorithms. [ABSTRACT FROM AUTHOR]

Chance-constrained economic dispatch (CCED) is widely used to cope with uncertainties brought by renewable energy sources. In CCED, security of power system is guaranteed by restricting risks under permitted level. As the penetration rate of renewable energy increases rapidly, the CCED model may be infeasible because of inadequate system reserve and/or transmission congestion under certain circumstances. Therefore, the curtailment of renewable energy is indispensable to guarantee security in some scenarios for power systems with high penetration of renewable energy. To address this crucial issue, this paper proposes a novel CCED model, where generation of conventional units and curtailment strategies of renewable energy are co-optimized to minimize total operational cost and restrict operational risks. An efficient solution method is developed to schedule generation and curtailment sequentially, in which tractable optimization models are formulated for each step. Numerical tests demonstrate that the proposed model can significantly reduce the probability of emergency control of curtailment compared with conventional CCED models. Furthermore, this solution method also outperforms scenario-based method with significantly improved computational efficiency and higher-quality solution. [ABSTRACT FROM AUTHOR]

This paper develops a novel demand side management (DSM) approach to incorporate in optimal sizing of solar photovoltaic (PV), wind turbine (WT), and battery storage (BS) for a standalone household. The DSM strategy is based on the state-of-charge level of battery and day-ahead forecasts of solar insolation and wind speed. The core of the DSM is a fuzzy logic method which decides for efficient load shifting and/or load curtailment. The day-ahead forecasting errors, obtained by an artificial neural network technique, are considered not only in the DSM strategy but also in maintaining an operating reserve. The battery capacity degradation is calculated using the Rainflow counting algorithm to obtain a realistic battery model and estimate its lifetime. A typical household in South Australia (SA) is considered as a case study. Three different configurations (PV-BS, WT-BS, and PV-WT-BS) of the electricity supply system are optimized using the proposed method. It is found that the PV-WT-BS system is the best configuration that provides the lowest cost of electricity for both with and without applying the proposed DSM strategy. Comparison of the results of the best system configuration with an actual system in SA and two recently published articles indicates that the proposed method is very effective in lowering the electricity cost with zero-emission. [ABSTRACT FROM AUTHOR]

In light of the rapid growth of data centers around the world and their huge energy consumption, several researchers have focused on the task scheduling and resource allocation problem in order to minimize the energy consumed by the data center. Other initiatives focus on the implementation of green energy sources in order to minimize the consumption of fossil fuels and their emission of CO 2 . As part of the ANR DATAZERO project (Pierson et al. in IEEE Access 7, 2019. https://doi.org/10.1109/ACCESS.2019.2930368), several research teams have engaged in efforts at defining the main concepts of a full green data center, powered only by renewable energy. Achieving this goal necessitates a focus on the efficient management of an autonomous hybrid power system consisting of solar panels, wind turbines, batteries, and fuel cell systems. The purpose of this work is not to show that a stand-alone data center is economically viable, but rather is feasible. This paper proposes a set of models based on mixed integer linear programs capable of managing the energy commitment to address data center power demand. The approach takes the season and weather forecasts into account at the time of optimization. [ABSTRACT FROM AUTHOR]