Search

Pakistan is faces significant challenges in meeting its energy demand and consumption needs for consumers. This country's energy production from primary sources such as petroleum and natural gasses is incompetent in fuel-use and hence unable to meet feasibility cost. With an increasing population, Pakistan's energy consumption per capita has been steadily rising. This behaviour is leading to critical energy issues, especially in remote rural areas. This trend in rising energy costs and demand factors are similar to those in the energy markets in the South and South-East Asia. The primary energy sources in Asia continent, including fossil fuels, are insufficient supply to meet this growing demand in production and thus resulting in frequent electricity blackouts. Consequently, renewable energy sources such as solar photovoltaic (PV) and wind power have substantially started to produce energy and to provide a huge portion of Pakistan's daily energy needs apart in conventional energy currently. However, these sources are not yet as reliable, conventional energy bases have a challenge for sustainable energy production. As a result, renewable energy factors nonetheless initial started have effectively stabilized energy consumption, particularly for green electricity with net-zero carbon emissions. The aim of this study is to evaluate the feasibility and cost-effectiveness of integrating a microgrid hybrid system with combined (solar PV/wind power) renewable energy as well as conventional fossil fuel generators. This evaluation focuses on predicting energy production and its costs using Hybrid Optimization of Multiple Energy Resources (HOMER) software, and to enhance the electricity standards at NUST (National University for Sciences and Technology), Pakistan. The proposed methodology of microgrid hybrid system, when evaluated using HOMER software, shows a significant improvement in energy stability and cost efficiency. Moreover, this proposed system can reduce reliance on fossil fuels by a substantial percentage, enhances the predictability of energy production, and optimizes its energy consumption. These can achieve better performance metrics in terms of reliability, cost, and environmental impact; feasible solution for Pakistan and the developing countries. This proposed methodology offers a novel approach by integrating renewable energy sources with conventional generators to create a balanced and efficiency factor by microgrid system. This hybrid system goals as an investigation is to optimize this energy production, reduce carbon emissions, and provide a more stable and cost-effective energy supply.

The performance of photovoltaic (PV) modules is greatly impacted by dust accumulation and defects appearing in photovoltaic (PV) modules. Existing studies primarily focus on the effect of dust on general photovoltaic performance, neglecting the interactions with pre-existing defects such as snail trails. These defects are known to degrade the efficiency of PV modules. However, their interaction with environmental factors like dust accumulation has not been extensively analyzed. This research comprehensively analyzes the impact of dust accumulation on the performance of PV modules affected by snail trails. Using an experimental setup under outdoor conditions, the study incorporates thermal imaging, current-voltage characteristic curve tracing (IV curve tracing), electroluminescence (EL) imaging, and chemical analysis of the accumulated dust, to evaluate the electrical and thermal parameters affecting PV module performance. The study focuses on three types of modules, clean serves as a reference module (PV-R), normal unclean (PV-N), and snail trail-affected unclean PV module (PV-S). Compared to the PV-R module, the study meticulously quantifies the effect of accumulated dust on key performance indicators such as output power, V, and I. The PV-N module exhibits reductions of 17.7 % in current, 3.91 % in voltage, and 18.15 % in power output. The PV-S module experienced a decrease of 7.4 % in current, 7.55 % in voltage, and 14.87 % in power output under the dust deposition density of 6.984 g/m^2 having a mean particle size of 2.2279 μm. The dust deposition reduced the transmittance of glass, which indicates a potentially adverse impact on the PV module's efficiency. The findings highlighted in the current work provide a significant understanding of the detrimental impacts of dust accumulation on defected photo voltaic modules, highlighting the need for regular maintenance and cleaning to ensure optimal performance.

Hybrid renewable energy systems with electric vehicle charging stations can provide reliable and environmentally friendly power output for telecom Base Transceiver Stations (BTS). This paper provides an optimized BTS telecom deployment method. The proposed framework uses actual load profiles to conduct a techno-economic assessment of 26 independent sites in North, South, and Central regions to reduce NPC, operational costs, LCOE, and diesel generator running hours. To complete this study’s financial model, these 26 BTS sites are connected to the grid for net metering and environmentally friendly electric vehicle (EV) charging stations, and incentives (bonus depreciation and investment tax credit) are added. Finally, numerous uncertain elements are sensitively analyzed, and a carbon emission reduction environmental evaluation is done. When connected to a grid for net metering, freestanding hybrid systems (diesel generator-photovoltaic-wind-battery) have a significantly lower LCOE. Standalone hybrid systems have LCOEs between 0.1096 and 0.2325 $/kWh. On-grid hybrid systems have an average LCOE of 0.004065 to 0.03559 $/kWh. After adding electric vehicle charging stations and incentives, the average LCOE drops further. Optimization and comparative studies reveal that the best grid expansion hybrid charging station is cheaper, greener, and more sustainable than the stand-alone hybrid system. The Sustainable Development Goals (SDGs) and governments and corporate investors can utilize the suggested studies to make decisions and optimize policies.

Multi-agent based small scaled smart grid reinforcement scheme is proposed to manage energy resources by enhancing resilience to supply power to critical loads in peak demand by leveraging demand side management (DSM) for smoothing load profile and optimal energy storage system (ESS) scheduling in response to grid cost. Interconnected microgrids comprise diverse energy resources that allow sharing energy to balance fluctuation by deploying a market that rewards cheap energy. Three stages are defined in this proposed scheme. In the first stage, without DER, consumption charges are reduced to 7.1% through DSM, but customer comfort is compromised. In the second stage, microgrids comprise RER; energy consumption-related charges are reduced to 92.2% by the feed-in tariff (FIT) Program. In the final stage, the inclusion of energy market for trading among microgrids (ESS, diesel and RER) resulting in reduced charges greater than charges incurred from the grid is defined by 122.1%. The findings show that the average power drawn from utility grid increased from 134.7kW to 205.3kW, and peak demand decreased from 591kW to 495kW. Integer linear programming optimization technique is applied to allow the optimal dispatch of microgrids generation to reduce the operation cost to 42.64%. Less reliance on the grid, flexible energy use, and avoiding costly electricity purchases are achieved through DSM, effective resources scheduling, and energy trading.

Floating Photovoltaic (FPV) systems are gradually becoming more desirable due to a multitude of reasons, encompassing proximity to urban water reservoirs (facilitating city access) and their technical advantages. Climate change potentially presents risks of drought and FPV can potentially benefit by providing clean energy as well as saving water from evaporation. However, detailed studies are required to comprehensively evaluate the potential of FPV considering not only the technical parameters but evaluating the climatic effects as well. This paper presents an integrated multi-dimensional framework for the analysis of 2.5 MW grid-connected FPV systems over different climatic zones. In the first layer, a techno-economic and performance evaluation is carried out by fine-tuning different inputs of systems to make it ideal for proposed analyses under actual FPV conditions. Similarly, in the second layer environmental along with forest absorbing carbon analyses are performed. While socio analysis observed in the third fold is based on various SDGs and their indicators. Results reveal that the Dam with cold in winter and hot in summer climate conditions observed a most feasible site with a Levelized cost of energy (LCOE) of $0.047/kWh and a Net present value (NPV) of million $1.7705, respectively. In contrast, a Dam with mild cold climate conditions proves the least feasible site with LCOE of $0.057/kWh and NPV of million $1.0256, respectively. Similarly, the former Dam saved 20.50% higher CO2 emissions as compared to the latter, as well as required hectares of forest absorbing carbon. A comparative analysis observes a capacity factor of 22% and a performance ratio (PR) of 5%–10% higher as compared to solar photovoltaic (SPV) for dams with extreme weather.

Hybrid microgrids, integrating local energy resources, present a promising but challenging solution, especially in areas with limited or no access to the national grid. Reliable operation of off-grid energy systems necessitates sustainable energy sources, given the intermittent nature of renewables. While fossil fuel diesel generators mitigate risks, they increase carbon emissions. This study assesses the viability of integrating a very small modular renewable energy reactor into a microgrid for replacing conventional diesel generators, substantially curbing greenhouse gas emissions. A comprehensive analysis, including design and economic evaluation, was conducted for an off-grid community microgrid with an annual generation and load of 8.5 GWh and 7.8 GWh, respectively. The proposed microgrid configurations incorporate very small modular reactors, alongside solar, wind, and battery storage systems. MATLAB modeling and simulation across eight cases, accounting for seasonal variations, demonstrate the technical and economic feasibility of case 7. This configuration, integrating modular reactors, photovoltaics, wind turbines, and battery storage, satisfactorily meets load demands. Notably, it boasts a high internal rate of return up to ∼31% and a shorter payback period of around 4 years compared to alternative scenarios.

In this paper, an innovative transactive energy approach is proposed as viable option for coordinated distribution system planning across a certain horizon. The proposed approach is evaluated across a multi-looped (meshed) test system and is implemented with load growth having prosumers participating in the electrical market in transactive energy system aiming at evaluation on techno-economic basis. Apart from prosumer sensitivity analysis, evaluations have been carried across reducing total production cost of energy, reduction in per unit price, active power losses. Whereas improving voltage profile, cost of scheduling and consumer per kWh purchase and sales in comparison with traditional counterpart. The proposed framework includes optimization algorithm aiming at sources scheduling and IEEE 69 system for validation. The algorithm minimizes cost, maximizes energy efficiency, increases renewable energy mix and reduces consumers cost of energy purchase. Reduction of 51.44 % in cost of energy is achieved, whereas loss reduction of 12.6% is achieved. The comparison of IEEE 69-bus base case with the 10 %, 15% and 20% transactive energy applied with simulations to evaluate performance parameters that will directly benefit both prosumers and utility alike in-terms of low bills and further reduction of stress on the grid amid load growth across multiple years.

Rapid growth in a number of developing nations’ mobile telecommunications sectors presents network operators with difficulties such as poor service quality and congestion, mostly because these locations lack a dependable and reasonably priced electrical source. In order to provide a sustainable and reasonably priced energy alternative for the developing world, this study provides a detailed examination of the core ideas behind renewable energy technology (RET). A multi-agent-based small-scaled smart base transceiver station (BTS) site reinforcement strategy is presented to manage energy resources by boosting resilience so to supply power to essential loads in peak demand periods by leveraging demand-side management (DSM). Diverse energy sources are combined to create interconnected BTS sites, which enable energy sharing to balance fluctuations by establishing a market that promotes economical energy. A MATLAB simulation model was developed to assess the effectiveness of the proposed system by using real load data and fast electric vehicle charging loads from five different base transceiver stations (BTSs) located throughout Pakistan’s southern area. In this proposed study, the base transceiver station (BTS) sites can share their energy through a multi-agent-based system. From the results, it is observed that, after optimization, the base transceiver station (BTS) sites trade their energy with the grid at rate of 0.08 USD/kWh and with other sites at a rate of 0.04 USD/kWh. Therefore, grid dependency is decreased by 44.3% and carbon emissions are reduced by 71.4% after the optimization of the base transceiver station (BTS) sites.

This paper developed an intelligent multi-agent system (MAS) with a multi-layer framework for multi-microgrids (MMGs) using robust and modern communication patterns for deployed agents to achieve distributed tasks. The MMG paradigm introduces three microgrids (MGs) based on the type of load, working environment, and living habitat: residential, commercial, and industrial. In addition, a day-ahead and real-time model is proposed for day-ahead and real-time signals. Intelligent agents in the multi-layer MAS framework make smart decisions based on multiple algorithms to optimize schedule power and minimize costs, considering demand dispatch and demand response as core components. Maximum renewable energy utilization aims to increase user comfort and reduce greenhouse gas (GHG) emissions. Load agents deployed in each MG ensure maximum efficiency. The proposed framework recommends various tariff rates and tariff adjustment strategies to promote and offer an economic evaluation across the respective indices. To minimize the monopoly of the energy market, an efficient energy market model is developed for the proposed MMG paradigm to maximize the competition by incorporating future and spot-market trading schemes for day-ahead and real-time signals. The comparative analysis indicates optimized results based upon the cost-benefit analysis, cost reduction, power transaction in the market, and maximum utilization of renewable energy resources (RERs).

This article presents a techno-economic analysis of upgrading a Photovoltaic (PV) power system at large. A comparative study has been carried out considering various technical and economic factors like per unit cost, annual load, energy demand growth, and PV panel efficiency, annual performance degradation of system, cost of electricity, payback period, initial capital expenditure, and rate of return in the form of saving in energy bills. The PV system is proposed to be upgraded from 1 MW to 2 MW at the National University of Sciences & Technology (NUST), Islamabad. The PV system is subject to be upgraded due to the growth rate of annual load and amount saved by PV contribution, which has been forecasted using data trends of the above-mentioned technical and economic factors. While considering these factors, four economic models have been evaluated and compared, which include Self-Built via Contractor with self-financing, Self-Built with bank financing, Power purchase agreement (PPA), also known as rental agreement and Built, Own, Operate & Transfer (BOOT) model. Merits and demerits of these models have been discussed, and the most feasible model has been proposed based on the mentioned key factors and long-term sustainable development goals (SDG) for affordable clean energy to contribute to climate action, i.e., adaptation of renewable energy resources (RER) and reduction of greenhouse gases (GHG).

Escalating energy demands and climate change challenges necessitate the adaptation of renewable-based microgrid systems in the energy sector. The proposed work employs a robust Multi Agent System (MAS) technique to achieve efficient and automated control of the hybrid microgrid operation. The hybrid microgrid system incorporates Renewable Energy Sources (RES), a diesel generator, and a battery storage system. The operation of the hybrid microgrid consists of three distinct modes: islanded, transition to grid, and grid-oriented mode. The system’s performance is optimized by considering factors like climatic patterns, energy costs, connected source characteristics, and load demand. Different climatic scenarios are assessed for each mode of operation, where the best, extreme sunny, extreme cloudy, and worst climate conditions are considered for islanded mode; sunny and cloudy scenarios are considered for transition to grid mode as well as grid-feed and grid-tied modes are considered for grid-oriented operation of the microgrid. The simulation studies are performed using the MATLAB/Simulink R2021a environment. Furthermore, Particle Swarm Optimization (PSO) is implemented to optimize power allocation within the microgrid and enhance its cost-effectiveness. The optimization results demonstrate efficient utilization of available energy sources along with effective energy management facilitated by the MAS control system. The results emphasize the importance of adopting a MAS approach for achieving smart energy management through comprehensive analysis and integrating decentralized energy management techniques for optimal accommodation of distributed energy resources in hybrid microgrids.

This paper presents a policy approach for the technological transition from hydropower to green energy in developing countries. Conventional electricity generation technologies have proven to be a burden on the economy of a country, as they have to depend on fossil fuels to meet their energy needs, such as diesel and gasoline. Pakistan has been taken as a case study to show the growing electricity demand of a developing country that implores major technological initiation. This serves as the assessment of suitability and resource regarding the installation of floating solar photovoltaic (FSPV) on reservoirs of hydroelectric dams, thus acting as a virtual battery. Generation from FSPV can complement the hydropower generation in the dry period, consequently meeting the peak demand. In real-time, by employing both technologies at the same substation, hydropower covers the intermittency issues of floating PV technology by improving power quality. Seven dams selected for this study have been analyzed based on their percentage surface area of the reservoir for electricity generation, water conservation, and emission reduction. Tarbela dam has been further analyzed for demonstrating the virtual battery effect by improving the electricity generation profile. The proposed approach meets the electricity needs of about 15.5% of the total population and serves the water need of 2.3 million people in the country.

The paradigm shift of power markets towards the smart grid, involves mass energy transfer between entities and requires the support of effective policies under comprehensive framework. This paper presents a technical-economic framework for energy wheeling (FEW) that encompass a self-sufficient smart energy network of regional microgrids and virtual power plants. The generation capacity design of regional microgrids (MGs) is in accordance with the forecasted load of the regional population, whilst MG’s excess energy is made available for wheeling. The Facebook Prophet forecasting model is used to forecast the load in accordance to regional population growth for twenty years. Each virtual power plant (VPP) is the coalition of wind and solar resources based regional MGs. The underlying network of regional VPPs supports the wheeling framework by communicating with each other through a trading platform that follows an energy distribution model (EDM), based on intelligent publisher–subscriber (Pub/Sub) pattern. The results of this framework are implemented on a developing country i.e. Pakistan. Through this framework, 39.9 MW solar and wind energy will be injectable in the country’s power system, thus raising the renewables share from 4.6% to 4.7% of the total generation capacity. The achieved results are supported with economic analysis.

Abstract One of the challenging issues in the realization of medium‐voltage DC (MVDC) distribution system is high‐speed protection scheme design against quickly rising short circuit fault‐DC current. This paper proposes a high‐speed protection scheme based on the processing of energy signals through mathematical morphology (MM). The voltage and current signals measured at a distribution line section (DLS) are used to obtain the energy signal. The proposed scheme develops a modified MM‐based filter to extract the transient information in the energy signal rapidly and distinctly in the form of positive/negative polarity. The polarity detection of any disturbance in the analysed energy signal indicates the occurrence of a sudden disturbance in the system. The sudden disturbance occurrence confirmation triggers the faulty DLS identification and classification algorithms of the proposed scheme. The faulty DLS identification and classification algorithms are based on the detected polarities of the energy signal. The protection scheme requires low bandwidth communication. The proposed protection scheme is evaluated by using a ±2.5 kV radial distribution network containing 4 feeders under different fault conditions in MATLAB/SIMULINK software. Simulation results verify that the proposed scheme can isolate the faulty portion within a few milliseconds after fault inception under a variety of fault cases.

Presents corrections to the paper, (Corrections to “Multi-Micro Grid System Reinforcement Across Deregulated Markets, Energy Resources Scheduling and Demand Side Management Using a Multi Agent-Based Optimization in Smart Grid Paradigm”).

Recently, the concept of microgrids has emerged in the world due to the integration of distributed energy resources (DERs) at the distribution end. The design of a reliable protection strategy is one of the top-most challenges associated with microgrids. This is because of the transition of microgrids between grid-tied and autonomous modes of operation. This paper presents a state-of-the-art microgrid protection scheme based on the Kalman filter (KF). The proposed scheme uses the one-end current signal of a distribution line for the detection and classification of faults. Firstly, the KF is applied to each phase of a three-phase current signal individually to generate residuals and total harmonic distortion (THDs). Next, the variations in the residuals and THDs of each phase are compared with pre-specified threshold values to detect the faulty events in the microgrid. As each phase is processed through KF individually, therefore, the proposed scheme is inherently phase segregated. Afterward, the KF is applied to extract the third harmonic component from the three-phase current and voltage signals. Then, the KF-based reactive power (KFBRP) is obtained from the extracted third harmonic components. Finally, the directional properties of the three-phase KFBRP are used to locate the faulty section in the microgrids. Extensive simulations in MATLAB/ Simulink software are performed for the grid-tied as well as the autonomous modes of operation under radial and meshed topologies. The results show that the proposed scheme is highly robust in all testing scenarios without any false tripping and blinding issues.

The increased addition of DERs, their intermittent nature, and the high cost of ESSs are the key hurdles for interconnected multi-microgrids to operate economically. It is important to have a system that is simple to adopt, computationally inexpensive, does not share private data. In this paper, an energy management scheme is proposed for multiple interconnected renewable energy resources within the multi-microgrid paradigm. The core objectives aimed at minimizing the operational cost of each microgrid and utilize RER most economically without any load interruptions. The proposed scheme is performed in two steps. In the first step, the load is shifted in accordance with the daily price curve via resource scheduling i.e., charging and discharging of battery storage, day-ahead forecast of RER generation, and respective loads. In the second stage, the cost of energy is further reduced using opportunistic trading with neighboring microgrids in real-time. The mismatch between forecasted generation and load is also adjusted via either energy trading or with efficient scheduling of available power that is stored during off-peak hours. The effectiveness of the proposed scheme is applied to a test system consisting of four interconnected microgrids. Results indicate that daily average power imported from the utility grid and peak demand is changed from 103 kW to 162 kW, and from 661 kW to 527 kW, respectively. Likewise, as per comparison, significantly favorable results have been achieved in terms of efficient available resources utilization, market trading, and reduction of the respective bills.

The energy management in new distribution paradigms are amongst one of core research dimension, particularly in smart grids. This paper proposes a hierarchical energy management system for inter-connected multi-smart buildings with an inclusion of local Power Market. As home appliances have huge contribution in load of buildings, the appliances are scheduled in order to minimize operational cost while taking into account the user comfort and other system constraints. The objectives of this paper aim to minimize operational cost, CO2 emissions, grid dependency while maximize user comfort and revenue. The proposed technique enables a prosumer with two options, either they can sell excess energy to the utility or can bid and sell in market with high price compare to utility. Besides increase in revenue, the consumer is enabled to buy electricity from utility or from local market with low prices compare to utility grid aiming at reducing operational cost. The proposed framework is evaluated across three algorithms namely, JAYA, teacher learning based optimization (TLBO) and Rao1, respectively. As per comparative analysis, the JAYA algorithm outperforms the others in achieving the aimed objectives in-terms of favorable achieved numerical values. Different cases are created in order to test the effectiveness of proposed system. The overall simulation results validate the proposed approach with highest operational cost reduction of 151.48%, peak load reduction 76.76%, grid dependency reduction 95.61%, and minimum emission of CO2 is 3.70 Kg/Day as compare to base case.

Distribution networks are facing new challenges with the emergence of smart grids, such as capacity limitations, voltage instability, and many others. These challenges can potentially lead to brownouts and blackouts. This paper presents an innovative technique for optimal siting and sizing of distributed generators (DGs) in radial distribution networks (RDNs). The proposed technique uses a novel algorithm that combines improved grey wolf optimization with particle swarm optimization (I-GWOPSO) by incorporating dimension learning-based hunting (DLH). The proposed I-GWOPSO employs a novel aspect of DLH to reduce the gap between local and global searches to maintain a balance. The main optimization objectives aim to optimally site and size the DG with minimization of active power loss, voltage deviation, and improvement of voltage stability in RDNs. Case studies are simulated with IEEE 33-bus and IEEE 69-bus test systems, for the optimal allocation of DG units by considering various power factors. The results validate the efficacy of the proposed algorithm with a significant reduction in real power loss (up to 98.1%), improvement in voltage profile, and optimal reduced cost of DG operation with optimal sizing across all considered cases. A comparative analysis of the proposed approach with existing literature validates the improved performance of the proposed algorithm.

A virtual power plant (VPP) is a cloud based distributed power plant that aggregates the capacities of diverse distributed energy resources (DERs) for the purpose of enhancing power generation as well as trading or selling power on the electricity market. The main issue faced while working on VPPs is energy management. Smart energy management of a VPP is a complex problem due to the coordinated operation of diverse energy resources and their associated uncertainties. This research paper proposes a real-time (RT) smart energy management model for a VPP using a multi-objective, multi-level optimization-based approach. The VPP consists of a solar, wind and thermal power unit, along with an energy storage unit and some flexible demands. The term multi-level refers to three different energy levels depicted as three homes comprising of different amounts of loads. RT operation of a VPP is enabled by exploiting the bidirectional communication infrastructure. Multi-objective RT smart energy management is implemented on a community-based dwelling system using three alternative algorithms i.e., hybrid optimal stopping rule (H-OSR), hybrid particle swarm optimization (H-PSO) and advanced multi-objective grey wolf optimization (AMO-GWO). The proposed technique focuses on achieving the objectives of optimal load scheduling, real-time pricing, efficient energy consumption, emission reduction, cost minimization and maximization of customer comfort altogether. A comparative analysis is performed among the three algorithms in which the calculated real-time prices are compared with each other. It is observed that on average H-PSO performs 7.86 % better than H-OSR whereas AMO-GWO performs 10.49% better than H-OSR and 5.7% better than H-P-SO. This paper concludes that AMO-GWO is the briskest, most economical, and efficient optimization algorithm for RT smart energy management of a VPP.

Abstract The efficiency of DC microgrid needs investigation from a smart grid perspective, since their spread has expected to prevail in comparison with AC counterparts. Furthermore, there is a need to address the limitations (majorly to cater the intermittency of distributed energy resources (DERs) as well as the time dependency of systematic parameters etc.) in previous model and propose a new mathematical model to evaluate system efficiency for given parameters and scenarios. The core focus of current study aims at formulation of an improved (composite) mathematical model, that is capable of bridging issues and serve as a tool to address requirements of future DC systems including microgrids (MGs) and multi‐microgrids (MMGs). This research work offers such a mathematical model that consists of 3D matrices based on newly derived set of discrete time dependent equations, which evaluates the system efficiency of residential DC‐MMGs. Each DC‐MG is embedded with intermittent DERs, storage, components (with efficiency variations), and multi‐class load (with discrete time dependency), for evaluation across worst, normal, and best scenarios. A comprehensive sensitivity analysis across various cases and respective scenarios are also presented to evaluate overall system performance. Also, the impacts of system parameters on various system variables, states, and overall system efficiency have presented in this paper.

COVID-19 is a severe global pandemic that has caught the whole world unprepared. In the absence of a clear timeline for this pandemic to end, it is need of the hour to investigate the effect of this pandemic on both previous and anticipated investments. Global economic unrest has hindered the ramping deployment of Renewable energy projects. The most quick actions that may be taken to mitigate the effects and to up-rise the investment portfolio policies are a very critical tool in hands of government for a very immediate effect have also been made without keeping the context of COVID-19 into account. New variants of diff rent nature are being discovered and every now and then new lock downs are happening. In this context different policies have to be evaluated under the pandemic scenario. A case study of a large scale renewable energy project for a higher education institute in Pakistan is being used to measure the difference during COVID and pre COVID times. This paper provides a framework to investigate the impact of COVID on renewable energy system projects under current net-metering, net-billing and self-consumption policies. A recent investment in a photovoltaic system is assessed based on previously projected financial benefits versus the pandemic effected ones. This research concludes that investing in photovoltaic systems are still a viable option even in an extreme pandemic situation with less than 0.5 years increase in payback period, and the government can still provide a stimulus for investing in green energy by implementing net-metering policies on a larger scale.

Using a method of multidimensional analysis, this study compares several renewable resources and technologies. Using the check-list matrix method, the environmental impacts of various renewable technologies are evaluated and compared based on their significance, impact, and duration. Based on their impact on soil, air, and water resources, solar thermal technologies and direct combustion of bio-mass resource have the highest impact scores, 31 and 27, respectively. With an impact score of 12, renewable energy from run-of-river is the most promising and sustainable option, followed by wind and solar PV with scores of 23 and 24 respectively. Based on the qualitative analysis conducted in the study, it can be concluded that all renewable technology options have a low to high varying significance impact on the environment and cannot be considered a comprehensive solution to Pakistan's environmental problem.

Renewable energy is considered to be sustainable solution to the energy crisis and climate change. The transition to renewable energy needs to be considered on a sectoral basis and one such sector that can potentially decarbonized with renewable energy is the telecommunication sector. Several base transceiver stations (BTS) in remote regions have unstable electric supply systems. Diesel generators (DG) are a common solution to energy problems on such telecommunication sites. However, they have high fuel costs on the global market and contribute to high carbon emissions. Hybrid renewable energy systems may provide a stable power output by integrating multiple energy sources, essential for supplying a dependable and uninterrupted power supply in the context of the telecom sector, notably base transceiver stations (BTS). Deploying such a system might also help BTS, which relies mainly on diesel generators with battery storage backup, reduce operational costs and environmental problems. This study presents the framework for large-scale photovoltaic system penetration based on techno-economic analysis (based on actual on ground data with least assumptions) in base transceiver stations (BTS) encapsulating telecom sector spread across various geographical regions. The proposed framework includes a mathematical model complemented with system design in HOMER software tool. The techno-economic aspects of the study were spread across 2, 12 and 263 sites, along with comparison analysis of photovoltaic system installation with and without energy storage devices, respectively. The sites included both on-grid and off-grid sites, which were exposed to high levels of power outages and subjected to reliance on costly and environmentally hazardous diesel generators. Optimization results showed that the photovoltaic system with a diesel generator and battery storage system provide a promising solution to the energy problem, with an average decrease in LCOE of 29%, DG hour’s reduction by 82% with 92% reduction in carbon emission and a reduction in NPC of 34% due to the high availability of solar. The techno-economic analysis indicated that optimized photovoltaic system and storage results in both on–off grid BTS sites with better options, amid low cost of energy and free accessibility of solar. Moreover, the results spread across geographical regions aiming at a reliable and environmentally friendly option that reduces load on utility grid across on-grid BTS sites and substantial overall reduction in diesel usage.

Over the last few decades, distributed generation (DG) has become the most viable option in distribution systems (DSs) to mitigate the power losses caused by the substantial increase in electricity demand and to improve the voltage profile by enhancing power system reliability. In this study, two metaheuristic algorithms, artificial gorilla troops optimization (GTO) and Tasmanian devil optimization (TDO), are presented to examine the utilization of DGs, as well as the optimal placement and sizing in DSs, with a special emphasis on maximizing the voltage stability index and minimizing the total operating cost index and active power loss, along with the minimizing of voltage deviation. The robustness of the algorithms is examined on the IEEE 33-bus and IEEE 69-bus radial distribution networks (RDNs) for PV- and wind-based DGs. The obtained results are compared with the existing literature to validate the effectiveness of the algorithms. The reduction in active power loss is 93.15% and 96.87% of the initial value for the 33-bus and 69-bus RDNs, respectively, while the other parameters, i.e., operating cost index, voltage deviation, and voltage stability index, are also improved. This validates the efficiency of the algorithms. The proposed study is also carried out by considering different voltage-dependent load models, including industrial, residential, and commercial types.

Load forecasts are fundamental inputs for the reliable and resilient operation of a power system. Globally, researchers endeavor to improve the accuracy of their forecast models. However, lack of studies detailing standardized model development procedures remains a major issue. In this regard, this study advances the knowledge of the systematic development of short-term load forecast (STLF) models for electric power utilities. The proposed model has been developed by using hourly load (time series) of five years of an electric power utility in Pakistan. Following the investigation of previously developed load forecast models, this study addresses the challenges of STLF by utilizing multiple linear regression, bootstrap aggregated decision trees, and artificial neural networks (ANNs) as mutually competitive forecasting techniques. The study also highlights both rudimentary and advanced elements of data extraction, synthetic weather station development, and the use of elastic nets for feature space development to upscale its reproducibility at global level. Simulations showed the superior forecasting prowess of ANNs over other techniques in terms of mean absolute percentage error (MAPE), root mean squared error (RMSE) and R2 score. Furthermore, an empirical approach has been taken to underline the effects of data recency, climatic events, power cuts, human activities, and public holidays on the model’s overall performance. Further analysis of the results showed how climatic variations, causing floods and heavy rainfalls, could prove detrimental for a utility’s ability to forecast its load demand in future.

Smart community setups nowadays are subjected to complicated issues such as instability, intermittent integration of the load at the demand side, and lack of intelligent two-way communication process. These issues need to be addressed in terms of a balanced power demand dispatch (DD) in the real-time or day-ahead duplex signal regime under multi-microgrids. This paper offers an intelligent multi-agent-based approach that works between different levels of communication and their respective layers for a community-based system to optimize the power in community-based multi-microgrids model. This will further enhance user personal comfort. Constraints relative to cost minimization also have a relation with this model. A three-level structure with various layers of autonomous agents take intelligent decisions based on prioritized particle swarm optimization (P-PSO), prioritized plug and play (PPnP), and knapsack; considering DD as the main driver of the system to address objectives like price and power consumption uncertainties. Distinct smart home models, depending upon their living habits, are keenly observed providing their power infrastructure and personal comfort. Load appliances considered as load agents are individually contemplated for maximum proficiency. Furthermore, two-way communication between utility and consumers lowers down the risk of the inefficiency of the system.

The increasing demand of energy in the traditional grids is getting more complex, less feasible, harmful, uneconomical and high in power losses. This paper presents an efficient energy management approach to mitigate such issues with smart micro grid (SMG) and aims at a solution that is both cost effective and eco-friendly, within energy market paradigm. Goals are achieved with the help of Home Energy Management Controller (HEMC), Energy Market Management Controller (EMMC) and Control Agent (CA). The individual load is managed in the presence of local generation, storage system, user comfort, DGs and Utility within energy market paradigm. Two level energy management approach is proposed to achieve concerned goals. First is to manage load and schedule storage with respect to individual local generation and market pricing. Second is to manage energy market with the help of four different types of priorities and control agent input. The problem is solved with a variant of meta-heuristic method, Multi Objective Grey Wolf Optimization (MOGWO), which gives more comprehensive solution by comparing with Particle Swarm Optimization (PSO). The proposed methodology is implemented on a SMG based-community test system. Homes within that community have different economic conditions and personal priorities. Simulation results demonstrates achievement of aimed goals in presented work.

The availability of sustainable, efficient electricity access is critical for rural communities as it can facilitate economic development and improve the quality of life for residents. Isolated microgrids can provide a solution for rural electrification, as they can generate electricity from local renewable energy sources and can operate independently from the central grid. Residential load scheduling is also an important aspect of energy management in isolated microgrids. However, effective management of the microgrid’s energy resources and load scheduling is essential for ensuring the reliability and cost-effectiveness of the system. To cope with the stochastic nature of RERs, the idea of an optimal energy management system (EMS) with a local energy transactive market (LETM) in an isolated multi-microgrid system is proposed in this work. Nature-inspired algorithms such as JAYA (Sanskrit word meaning victory) and teaching–learning based optimization algorithm (TLBO) can get stuck in local optima, thus reducing the effectiveness of EMS. For this purpose, a modified hybrid version of the JAYA and TLBO algorithm, namely, the modified JAYA learning-based optimization (MJLBO), is proposed in this work. The prosumers can sell their surplus power or buy power to meet their load demand from LETM enabling a higher load serving as compared to a single isolated microgrid with multi-objectives, resulting in a reduced electricity bill, increased revenue, peak-average ratio, and user discomfort. The proposed system is evaluated against three other algorithms TLBO, JAYA, and JAYA learning-based optimization (JLBO). The result of this work shows that MJLBO outperforms other algorithms in achieving the best numerical value for all objectives. The simulation results validate that MJLBO achieves a peak-to-average ratio (PAR) reduction of 65.38% while there is a PAR reduction of 51.4%, 52.53%, and 51.2% for TLBO, JLBO, and JAYA as compared to the unscheduled load.

There are three main parts of an electric power system—power generation, transmission, and distribution. For electric companies, it is a tough challenge to reduce losses of the power system and deliver lossless and reliable power from the generating station to the consumer end. Nowadays, modern power systems are more complex due to gradually increasing loads. In the electrical power system, especially in transmission and distribution networks, there are power losses due to many reasons such as overloading of the line, long distribution lines, low power factors, corona losses, and unsuitable conductor size. The main performance factor of the power system is reliability. Reliability means continuity of the power supply without any interruptions from the generating station to the demand side. Thus, due to these power losses, there are voltage stability problems and economic losses in the electrical system. The voltage stability of the power system can be increased by improving the voltage profile. In this paper, different techniques are analyzed that include the integration of wind power, the integration of photovoltaic power, and reactive power injection by integrating FACTS devices. These techniques are applied to the IEEE 57 bus system with standard data using simulation models developed in MATLAB. Thus, the results of the analysis of these techniques have been compared with each other.

In the above article [1], Fig. 3 (a) and (b) and Fig. 5 (a) and (b) were inadvertently duplicated during the proofreading and typesetting stage. This resulted in Fig. 3 (a) and (b) in [1] not representing the correlation of load with dry bulb temperature and dewpoint temperature as intended and stated in the figure caption. The correct representation of the correlation of load with both the dry bulb temperature and dewpoint temperature to be considered is shown below in Fig. 3. However, Fig. 5 (a) and (b) in [1] already correctly represents the average dry bulb temperature of eight weather stations and the average dew point temperature of eight weather stations. Therefore, Fig. 3 given below should replace Fig. 3 (a) and (b) in [1].

Modern distribution mechanisms within the smart grid paradigm are considered both reliable in nature and interconnected in topology. In this paper, a multiple-criteria-based sustainable planning (MCSP) approach is presented that serves as a future planning tool for interconnected distribution mechanisms and aims to find a feasible solution among conflicting criteria of various genres. The proposed methodology is based on three stages. In the stage 1, a weighted voltage stability index (VSI_W) and loss minimization condition (LMC) based approach aims at optimal asset optimization (sitting and sizing). In this stage, an evaluation of alternatives (solutions) is carried out across four dimensions (technical, economic, environmental, and social) of performance metrics. The assets considered in the evaluations include distributed generation (DG), renewable DGs, i.e., photovoltaic (PV), wind, and distributed static compensator (D-STATCOM) units. In the stage 2, various multicriteria decision-making (MCDM) methodologies are applied to ascertain the best trade-off among the available solutions in terms of techno-cost (economic) (TCPE), environment-o-social (ESPE), and techno-economic-environmental-socio (TEES) performance evaluations (OPE). In the stage 3, the alternatives are evaluated across multiple load growth horizons of 5 years each. The proposed MCSP approach is evaluated across a mesh-configured 33-bus active distribution network (ADN) and an actual NUST (which is a university in Islamabad, Pakistan) microgrid (MG), with various variants of load growth. The numerical findings of the proposed MCSP approach are compared with reported works the literature supports its validity and can serve as an important planning tool for interconnected distribution mechanisms for researchers and planning engineers.

The integration of commercial onshore large-scale wind farms into a national grid comes with several technical issues that predominately ensure power quality in accordance with respective grid codes. The resulting impacts are complemented with the absorption of larger amounts of reactive power by wind generators. In addition, seasonal variations and inter-farm wake effects further deteriorate the overall system performance and restrict the optimal use of available wind resources. This paper presented an assessment framework to address the power quality issues that have arisen after integrating large-scale wind farms into weak transmission grids, especially considering inter-farm wake effect, seasonal variations, reactive power depletion, and compensation with a variety of voltage-ampere reactive (Var) devices. Herein, we also proposed a recovery of significant active power deficits caused by the wake effect via increasing hub height of wind turbines. For large-scale wind energy penetration, a real case study was considered for three wind farms with a cumulative capacity of 154.4 MW integrated at a Nooriabad Grid in Pakistan to analyze their overall impacts. An actual test system was modeled in MATLAB Simulink for a composite analysis. Simulations were performed for various scenarios to consider wind intermittency, seasonal variations across four seasons, and wake effect. The capacitor banks and various flexible alternating current transmission systems (FACTS) devices were employed for a comparative analysis with and without considering the inter-farm wake effect. The power system parameters along with active and reactive power deficits were considered for comprehensive analysis. Unified power flow controller (UPFC) was found to be the best compensation device through comparative analysis, as it maintained voltage at nearly 1.002 pu, suppressed frequency transient in a range of 49.88–50.17 Hz, and avoided any resonance while maintaining power factors in an allowable range. Moreover, it also enhanced the power handling capability of the power system. The 20 m increase in hub height assisted the recovery of the active power deficit to 48%, which thus minimized the influence of the wake effect.

The modern distribution networks under the smart grid paradigm have been considered both interconnected and reliable. In grid modernization concepts, the optimal asset optimization across a certain planning horizon is of core importance. Modern planning problems are more inclined towards a feasible solution amongst conflicting criteria. In this paper, an integrated decision-making planning (IDMP) approach is proposed. The proposed methodology includes voltage stability assessment indices linked with loss minimization condition-based approach, and is integrated with different multi-criteria decision-making methodologies (MCDM), followed by unanimous decision making (UDM). The proposed IDMP approach aims at optimal assets sitting and sizing in a meshed distribution network to find a trade-off solution with various asset types across normal and load growth horizons. An initial evaluation is carried out with assets such as distributed generation (DG), photovoltaic (PV)-based renewable DG, and distributed static compensator (D-STATCOM) units. The solutions for various cases of asset optimization and respective alternatives focusing on technical only, economic only, and techno-economic objectives across the planning horizon have been evaluated. Later, various prominent MCDM methodologies are applied to find a trade-off solution across different cases and scenarios of assets optimization. Finally, UDM is applied to find trade-off solutions amongst various MCDM methodologies across normal and load growth levels. The proposed approach is carried out across a 33-bus meshed configured distribution network. Findings from the proposed IDMP approach are compared with available works reported in the literature. The numerical results achieved have validated the effectiveness of the proposed planning approach in terms of better performance and an effective trade-off solution across various asset types.

This paper offers a new improved voltage stability assessment index (VSAI_B)-centered planning approach, aiming at the attainment of technical and cost related objectives with simultaneous multiple asset deployment in a mesh distribution systems (MDS). The assets such as multiple distributed generation (DG) and distributed static compensator (D-STATCOM) units have been utilized; aiming at voltage stabilization, loss minimization, and associated objectives. The proposed planning approach incorporates expressions of VSAI_B aiming at initial simultaneous assets placement followed by loss minimization conditions (LMC) for appropriate asset sizing, which is further utilized for performance evaluations. The VSAI_B-LMC-based integrated planning approach is applied to configured MDS models such as a 33-bus test distribution system (TDS) for detailed analysis. The performance evaluations with the presented approach have been conducted for different cases along with respective scenarios considering various technical and cost-economic performance metrics. First, three cases referring to multiple DGs sitting and sizing for various power factors have been presented, followed later by two cases of multiple DGs and D-STATCOMs with respective evaluation scenarios. Finally, benchmark analysis is conducted on a 69-bus TDS for validity demonstration of the proposed approach. The comparison of achieved results in comparison with the available literature points out toward the validity and improved performance of the proposed approach.

The authors wish to make the following corrections to their paper [...]

This paper offers an enhanced voltage stability assessment index (VSAI) and loss minimalize condition (LMC) centered integrated planning approach. The proposed method aims at the simultaneous attainment of voltage stability, loss minimizations and various other related objectives with the employment of multiple distributed generation (DG) units, in mesh distribution systems (MDS). The approach presents two enhanced VSAI expressions based on a multiple-loops configured equivalent MDS model. The main objective of each VSAI expression is to find the weakest buses as potential candidates for single and multiple DG placements with initial optimal DG sizes for aimed objectives attainment in MDS. Later, mathematical expressions for LMC have been presented, based on equivalent MDS model. The LMC aims to achieve significant loss minimization with optimal DG sizes and attain negligible voltage difference across tie-line branches via reduction of respective loop currents. The proposed integrated VSAI-LMC based planning approach is employed with two computation variants and tested on two well-known, 33-Bus and 69-Bus, test distribution systems (TDS). The performance analysis of each TDS is conducted with two cases and respective scenarios, across various performance evaluation indicators (PEIs). The paper also offers a comparative analysis of achieved numerical outcomes of the proposed planning approach with the available research works found in the literature. The numerical results attained have better performance in comparison with the presented literature data and thus shows the effectiveness and validity of the proposed planning approach.

This paper presents a new planning approach based on voltage stability index (VSI) together with improved loss minimization (LM) formulations. The method has employed for application of distributed generation (DG) unit placement (location and size) in a loop (configured) test distribution network (LDN). Initially, VSI relationship for equivalent loop model has employed to find out potential locations for DG placement in LDN. Later, loss minimization formulations and loss minimization conditions (LMC) have been derived on the basis of an equivalent electrical model of LDN, for single and two DGs operating at various power factors, respectively. The proposed approach is comprised of two variants and has demonstrated on the 69-bus test distribution network. The first planning variant as a single case has applied for DG allocation (location, size, number) in LDN under normal load. Similarly, the second planning variant has demonstrated with three cases (six scenarios per case), evaluated under normal load and impact of load growth (across five years), respectively. The proposed approach has analyzed in terms of various performance indicators and results obtained have compared and found in close agreement with existed works in literature. Simulation results verify the validity of the proposed planning approach and establish that LDN performs better than radial distribution network from the perspective of load growth.

Smart grids (SGs), as an emerging grid modernization concept, is spreading across diverse research areas for revolutionizing power systems. SGs realize new key concepts with intelligent technologies, maximizing achieved objectives and addressing critical issues that are limited in conventional grids. The SG modernization is more noticeable at the distribution grid level. Thus, the transformation of the traditional distribution network (DN) into an intelligent one, is a vital dimension of SG research. Since future DNs are expected to be interconnected in nature and operation, hence traditional planning methods and tools may no longer be applicable. In this paper, the smart distribution network (SDN) concept under the SG paradigm, has presented and reviewed from the planning perspective. Also, developments in the SDN planning process have been surveyed on the basis of SG package (SGP). The package presents a SDN planning foundation via major SG-enabling technologies (SGTF), anticipated functionalities (SGAF), new consumption models (MDC) as potential SDN candidates, associated policies and pilot projects and multi-objective planning (MOP) as a real-world optimization problem. In addition, the need for an aggregated SDN planning model has also been highlighted. The paper discusses recent notable related works, implementation activities, various issues/challenges and potential future research directions; all aiming at SDN planning.

Distribution networks (DNWs) are facing numerous challenges, notably growing load demands, environmental concerns, operational constraints and expansion limitations with the current infrastructure. These challenges serve as a motivation factor for various distribution network planning (DP) strategies, such as timely addressing load growth aiming at prominent objectives such as reliability, power quality, economic viability, system stability and deferring costly reinforcements. The continuous transformation of passive to active distribution networks (ADN) needs to consider choices, primarily distributed generation (DG), network topology change, installation of new protection devices and key enablers as planning options in addition to traditional grid reinforcements. Since modern DP (MDP) in deregulated market environments includes multiple stakeholders, primarily owners, regulators, operators and consumers, one solution fit for all planning scenarios may not satisfy all these stakeholders. Hence, this paper presents a review of several planning techniques (PTs) based on mult-objective optimizations (MOOs) in DNWs, aiming at better trade-off solutions among conflicting objectives and satisfying multiple stakeholders. The PTs in the paper spread across four distinct planning classifications including DG units as an alternative to costly reinforcements, capacitors and power electronic devices for ensuring power quality aspects, grid reinforcements, expansions, and upgrades as a separate category and network topology alteration and reconfiguration as a viable planning option. Several research works associated with multi-objective planning techniques (MOPT) have been reviewed with relevant models, methods and achieved objectives, abiding with system constraints. The paper also provides a composite review of current research accounts and interdependence of associated components in the respective classifications. The potential future planning areas, aiming at the multi-objective-based frameworks, are also presented in this paper.