Excess energy (PExc) in isolated microgrids based on hybrid renewable energy systems (HRES) causes reliability and protection issues. This paper presents the optimal design of HRES by minimizing the levelized cost of energy (LCOE) and PExc management in isolated microgrids (IMGs) with different types of energy storage systems (ESS), including battery energy storage (BES) technologies, pumped hydro storage (PHS), hydrogen energy storage (HES), and thermal energy storage (TES). The IMGs design with the various BES technologies minimizes the LCOE in the range of 0.1050 $/kWh-0.3307 $/kWh, ensuring supply reliability above 96% and limiting PExc generation below 10%. Similarly, the IMGs with PHS, HES, and TES offer the electricity at LCOEs of 0.4094 $/kWh, 0.2824 $/kWh, and 0.1429 $/kWh, respectively. The optimally designed IMG with the HES reduces the highest 92.39% greenhouse gas emissions. The African Vultures Optimization Algorithm (AVOA) minimizes the LCOE with a faster convergence rate and higher accuracy.

An existing designed off-grid microgrid (OMG) can generate excess energy (EEx) up to 74.7% of its total production, which adversely affects supply reliability and the performance of protection systems. The study presents the optimal design, excess energy management, and techno-economic-environmental assessments of a hybrid renewable energy system based on OMG integrated with multi-energy storage systems (MESS), including battery energy storage (BES) and hydrogen energy storage. The OMG includes Fuel Cell, Electrolyzer, Hydrogen Tank, Solar Photovoltaic, Wind Turbine, BES, and Converter. The proposed OMG design is obtained by minimizing the levelized cost of energy (LCOE) and limiting EEx, while maintaining high supply reliability through reliability constraints on energy not supplied probability (ENSP) and percentage of excess or waste energy generation. The outage rates of systems are also taken into account to obtain an accurate design for OMG. The Grey Wolf optimization algorithm (GWO), Marine predator algorithm (MPA), and Moth-flame optimization (MFO) metaheuristics algorithms are used for optimization. The results indicate that GWO is superior to MPA and MFO. The proposed OMG integrated with MESS limits the EEx to 4.0% and 2.94% in Case-I and Case-II, respectively, while minimizing the LCOE to 0.1769 $/kWh and 0.1863 $/kWh, respectively. The OMG design reduces GHGs by 94.98% and 94.85% in Cases-I and II, respectively. The sensitivity analysis reveals that an increase in load results in larger size and higher CO2 emissions, while the impact on LCOE remains minimal. Moreover, with variations in capital costs, interest rates, inflation rates, and ENSP, the LCOE also varies.

This research enhances residential electric vehicle (EV) charging by integrating maximum demand (MD)-based regulation, which improves grid management while minimizing infrastructure and maintenance costs. Multiple EVs connected to distribution transformers (DTs) during peak hours lead to voltage drops, increased losses, and unfair charging distribution across households. This article introduces a decentralized EV charging system that integrates MD-based regulation, considering local parameters such as: 1) power consumption of household loads; 2) grid supply voltage; and 3) state of charge (SOC) of the battery. The proposed approach maintains EV charging within the rated capacity of the DT and the maximum allowable load, effectively minimizing load variance while ensuring a continuous power supply to household loads. To validate the proposed system: 1) a hardware-in-loop (HIL) simulation is conducted with Opal-RT; 2) the responses under various loadings are tested in a Simulink model; 3) a comparison study conducted using real household consumption data; and 4) network integration test and comparison study conducted using modified IEEE low-voltage distribution network (LVDN) in Simulink. The results demonstrate that this approach minimizes load variance, reduces peak-to-average ratios, and achieves fair, reliable charging across the network, regardless of EV connection points, making it a scalable and efficient solution for residential EV integration.

Isolated microgrids generate excess energy (Pexg) up to 70.1% of total generation, disturbing supply reliability and protection systems. This study presents the Pexg management, optimal design, and techno-economic-environmental analysis in a Hybrid Renewable Energy System (HRES) based isolated microgrid. The optimal sizing is obtained with the minimization of Levelized Cost Of Energy (LCOE), subject to Deficiency of Power Supply Probability (DPSP), and Percentage of Excess Power Generation (PEPG) to maintain the supply reliability and restrict the Pexg generation. The outage rate of Solar Photovoltaic (SPV) and Wind Turbine (WT) units is also considered to obtain the microgrid design. The proposed model is optimized using the African Vultures Optimization Algorithm (AVOA), Dragonfly Algorithm (DA), and Grey Wolf Optimization algorithm (GWO). Results show that GWO performs superior to AVOA and DA in standings of execution time and accuracy. The proposed microgrid with energy management techniques restricts the Pexg at 4.84% and 9.64% for Case-A and Case-B, respectively. The minimized LCOE of most techno-economical-environmentally friendly configuration SPV-WT-BG-BES is 0.2414 $/kWh and 0.1133 $/kWh for Case-A and Case-B, respectively, obtained with GWO. This configuration reduces the GHG emissions by 76.09% and 89.33% for Case-A and Case-B, respectively. The sensitivity analysis shows that LCOE varies significantly with the growth in load demand and capital costs. The CO2 emissions increase almost linearly with the raise in load growth. Thus, the proposed isolated microgrid design offers a techno-economically-environmental friendly system as it offers minimum LCOE, lowest Pexg, high supply reliability, and 100% Renewable Energy Fraction (REF).

Reliable supply of electricity in isolated rural areas is challenging due to the uneconomical accessibility of the national grid. The issue can be resolved with an isolated microgrid. This study presents an optimal design and techno-economic analysis of an isolated microgrid based on hybrid renewable energy systems (HRES) for meeting the electricity demand of a rural area, 'Kanur,' Maharashtra, India. The proposed microgrid integrates the wind turbine (WT), solar photovoltaic (PV), biogas generator (BG), and battery energy storage system (BES). The HOMER software minimizes the net present cost (NPC) and cost of energy (COE) to offer a reasonable costoptimal design at desired system reliability. At 0.0 % capacity shortage, the optimal sizing of PV, WT, BG, and BES units are 113 kW, 22 kW, 17 kW, and 362, respectively. At capacity shortages of 0.0 % and 2.5 %, the optimal WT/PV/BG/BES configuration achieves an NPC of $529,459 and $399,680 with COE of 0.146$/kWh and 0.112$/kWh, respectively. Excess energy generation is 23 % and 13.9 % of total annual generation, respectively. The proposed HRES is 71.2 % more cost-effective than diesel generator (DG) supply. The sensitivity analysis highlights the impact of system parameter variations on component sizing, NPC, and COE, aiding in the most cost-effective design selection.

Solar energy is a leading renewable energy source with one of the highest growth rates worldwide due to its versatile nature and plug- and-play role. The primary limitation of solar photovoltaics is its lower efficiency. Consequently, it is crucial to optimize the parameters of solar panels. The effectiveness of a photovoltaic panel is governed by factors such as its orientation, geographical conditions of solar panels with different tilt angles in various coastal zones of India to evaluate their performance. The installation nominal power of the plant is 72 kWp, consumer’s average daily energy requirement is 11 kWh/day and the performance ratio, effective energy output is calculated considering the system and component losses. The overall highest annual effective energy output of the array was recorded in Ahmedabad. The reduced energy output of the system is due to various environmental, system and component losses.

Due to the integration of hybrid sources, the current power system network is very complex and is being utilized to its full capacity in terms of economic scenario and asset utilization. The stability margin needs to be obtained for improving security and to avoid voltage collapse in the system on a larger scale. The main contribution of the current work relates to the following issues: (i) the proposed cumulative L-Index method has been used estimate the position of distributed generations (DGs) to reduce voltage deviation and increase the voltage stability margin. (ii) The impact of solar PV has been determined by the battery’s voltage stability margin and size. (iii) The optimal battery size with the solar PV and D-STATCOM has been determined. (iv) The total cost consists of fuel cost of DG, energy loss, emission cost of DGs, fixed cost, and operation and maintenance costs have been determined. The VSM has been enhanced by 41.60

A reliable, optimally designed, fully renewable energy based isolated microgrid is required to handle the excess power generated by the renewable energy systems (RES), which is neither stored in the batteries nor fed to the load after reaching the maximum storage capacity of batteries and meeting the load demand. This excess power can lead to overvoltage and larger dump load sizes and affect the battery energy storage in the isolated microgrids. This paper proposes an optimal design and energy management system for a fully RES based isolated microgrid consisting of a wind turbine (WT), solar photovoltaic (PV), and battery energy storage system (BESS) for the electrification of 'Kanur' in Maharashtra, India, considering Loss of power supply probability (LOPSP), Percentage of excess energy (PEE), and outage of generating units, to account for component failure during operation. The sizes of WT, PV, and BESS are optimized using the African vulture optimization algorithm (AVOA), Particle swarm optimization (PSO), Whale optimization algorithm (WOA), Moth flame optimization (MFO), algorithms to minimize the Levelized cost of energy (LCOE). The studies are performed with the LOPSPmax at 5 % in Case-I and 10 % in Case-II, with PEEmax kept at 20 %. The results show that the proposed system attains a minimum LCOE of 0.1229 $/kWh (Case-I) and 0.1020 $/kWh (Case-II) from the PSO algorithm, which is lower than the LCOE in the literature. With the PEE consideration, the proposed microgrid design limits the excess energy generation to 19.91 % in Case-I and 19.52 % in Case-II. However, without PEE, excess energy generation is 27.96 % and 24.78 %. The LOPSP attains 4.98 % in Case-I and 9.98 % in Case-II, to maintain the high supply reliability of 95.02 % and 90.02 %. The proposed microgrid is also analyzed with the load demand growth, and it found that the LCOE is nearly identical. Proposed microgrid also reduces greenhouse gas emissions by 91.2% with Case-I and 93.29% with Case-II. The sensitivity of LCOE and total life cycle cost (TLCC) towards LOPSP determines the parameter cost with supply. Further, the statistical analysis verifies that the PSO has better accuracy and minimum execution time for optimal microgrid design.

This paper presents a techno-economic evaluation of an optimally designed isolated microgrid for a remote village 'Kanur,' Maharashtra, India. The microgrid is designed using HOMER consisting of a Solar Photovoltaic (PV), Wind turbine (WT), Diesel generator (DG), and Battery energy storage (BES). The optimal design is evaluated based on the net present cost (NPC) and cost of energy (COE). Additionally, a sensitivity analysis is also presented for the variation of NPC and COE with the nominal discount rate (NDR) and inflation rates. The results show that the system can be operated with 0.0% capacity storage, i.e., the design offers sufficient generation to meet the load demand. The optimal configuration PV/WT/DG/BES/converter provides the lowest NPC 569,275 $ and COE 0.157 $/kWh among six other feasible configurations to meet the load demand. This configuration with DG provides the most economical COE and lowest excess energy generation at 28.6%. The sensitivity analysis shows that with change in NDR and expected inflation rate, NPC and COE significantly vary. It shows that it is difficult to evaluate the system performance of the isolated microgrid at the design state. However, a conservative choice of the NDR and inflation may seem optimal at the initial stage, but may lead to difficulties in the operational stages later. Thus, the selection of these parameters should be done after the sensitivity analysis.

Energy storage systems (ESS) address the uncertainty of renewable energy sources (RES) in renewable energy based isolated microgrids. One type of ESS is unable to provide 100% supply reliability. Moreover, there are either excess energy or reliability issues due to a lack of energy management techniques. Therefore, this paper proposes hybrid RES (HRES) with multiple-ESS (MESS), including battery and hydrogen storage system (HSS). The techno-economic analysis and optimal design of HRES-based microgrids, which include wind turbine (WT), solar photovoltaic (PV), fuel cell (FC), electrolyzer, HSS, and battery energy storage (BES) are performed using HOMER. The design objectives are to minimize the net present cost (NPC) and cost of energy (COE) in the proposed HRES with MESS. The analysis is performed for a multi-energy storage system. The results show that WT/PV/FC/Electrolyzer/HSS/BES/converter is the optimal microgrid with the lowest NPC of $838832, $679605, and COE of 0.232 $/kWh, 0.189 $/kWh at capacity shortages of 0.0% and 1.0%, respectively. The excess energy and unmet loads are also the lowest for the system at the same capacity shortage. The sensitivity analysis shows the impact of uncertain techno-economical parameters on NPC and COE.

In this paper modern systematic literature analysis of AC and DC microgrids (MG) in combination with renewable energy sources (RESs) as distributed generations units (DG), loads and battery bank to store electricity. A review on the different DG units arrangements consisting distribution networks of the low voltage AC and DC using numerous applications of microgrid systems in the perspective of current and the future customer apparatus energy market is broadly deliberated. Based on the techno-economic and ecological advantages of the renewable energy-based DG units, a detailed comparison of AC and DC microgrid systems is done. This paper also explores the possibility, controlling and energy management schemes of the both microgrid systems depending upon the most recent research findings. Eventually, the protection and the power management techniques in microgrid systems are discussed and delivered in detail. Out of this literature analysis, it could be evidenced that both type of microgrid systems consisting multiconverter gadgets are inherently conceivable for the upcoming energy systems to attain the consistency, better power supply and efficiency.

This study proposes an optimal microgrid design for rural electrification in India’s Leh and Ladakh regions, using wind energy, solar, energy, and battery energy storage system. The Dragonfly Algorithm (DA) is used to calculate the optimal number of microgrid units, and results are compared with popular optimization algorithms such as Grey Wolf optimization (GWO), Differential Evolution (DE), and Discrete Harmony Search (DHS). The optimal design is based on an objective function to minimize the Levelized cost of energy (LCOE) while keeping the loss of power supply probability (LOPSP) as a reliability constraint. Three configuration studies are carried out, with three cases, each with a different maximum permissible LOPSP (LOPSPmax) value. The results show that optimal design and efficient energy management reliably meet the load demand. The energy generated from the proposed microgrid is clean compared to the grid supply, and the amount of greenhouse gas (GHG) emissions is reduced by 91.2% from Configuration-I, Case-I, which is the most economical configuration. The LCOE obtained from Configuration-I, Case-I is 0.129 $/kWh, the lowest among similar systems available in the literature. To determine the parameter cost with supply, the LCOE and Total life cycle cost (TLCC) sensitivity to LOPSPmax are considered. Furthermore, statistical analysis shows that DA outperforms GWO, DE, and DHS in terms of accuracy and convergence rate.

This paper proposes a 100 % renewable fraction based isolated microgrid with wind turbine, solar photovoltaic, battery, and pumped hydro storage to minimize the levelized cost of energy (LCOE) considering a) Loss of power supply probability (LOPSP) to enhance reliability and b) Percentage of excess generation (PEG) to limit excess energy (Pex) generation. The reliability assessment is performed considering the outage rate of generating units. Isolated Microgrids with a 100 % renewable fraction generate Pex, a short-duration and infrequent event, adding to system uncertainty. The presence of Pex causes operational and reliability issues, increasing the system cost. Diesel generators are a popular choice to overcome the Pex; however, they cause greenhouse gas (GHG) emis-sions. The proposed microgrid is simulated on MATLAB for a year using Grey wolf optimization (GWO), Moth flame optimization (MFO), and the Dragonfly algorithm (DA). The GWO algorithm outperforms the other al-gorithms in terms of accuracy and execution time. The results show that the proposed microgrid restricts the Pex to 4.91 % (Case-I) and 4.95 % (Case-II) of total generation. The LCOE is 0.3588 $/kWh and 0.2491 $/kWh for Case-I and Case-II, respectively, using GWO. With the hybridization of energy storage, GHG emissions are reduced by 82.37 % and 93.15 % for Case-I and Case-II, respectively, compared to grid-supplied. The sensitivity analysis shows that CO2 emissions vary linearly with generation. However, LCOE variation is non-linear due to its significant correlation with energy storage. Thus, the isolated microgrids operate at 100 % renewable fraction with minimum LCOE, Pex, and low GHG emissions.

Energy storage system (ESS) plays a critical role in maintaining the reliability of microgrids. ESS selection for microgrids depends on energy density, specific power, specific energy, and economics. This paper analyses the economic benefits of various combinations of short-, medium-, and long-term ESS, i.e., multi-energy storage systems (MESS) in a microgrid. The economic feasibility of the system is analyzed using Homer software. The net present cost (NPC), the Levelized cost of energy (LCOE), and pollutant gas emission are chosen as parameters for analyzing the economic feasibility of the microgrids. The results show that amongst all the scenarios, the system with Hydrogen Storage System (HSS) with Proton exchange membrane fuel cell (PEMFC) and electrolyzer is the most feasible solution with the lowest LCOE and pollutant emission.

: Shunt active power filters (APFs) have emerged as a potential player to address power quality issues imposed by exponentially increasing nonlinear loads.The shunt APF used in this work is essentially a three-phase, three-wire PWM based current controlled voltage source inverter (VSI). Hysteresis current controllers (HCCs) are extensively used for gate pulse generation in converter circuits due to easy implementation and better dynamic response. This work explores the possibility of performance enhancement of shunt APF using a proposed modified HCC based on digital logic. The proposed modified HCC based shunt APF provides significant reduction in switching frequency, switching losses, better mitigation of harmonics and smoother reactive power flow. The dynamic analysis of performance of shunt APF with conventional and modified hysteresis current controller (HCC) is performed under randomly varying non-linear loading conditions. The simulated results confirm the ascendency of the modified HCC scheme over conventional HCC under transient as well as steady state conditions. The modified HCC emerges out to be a better scheme with manifold advantages and enhances the performance of shunt APF tremendously.

This paper proposed modeling of STATCOM to resolve power system problems. The STATCOM is a shuntconnected and second generation of static FACTS device that is connected parallel to the power system and it works on the principle of reactive power transfer between the STATCOM and the power system. The power system has several linear and non-linear types of loads. To control the STATCOM, PI control-based park transformations have been adopted. This functionality has been developed to improve active power generation and minimize power loss. This observation has been implemented on different types of loads where the source is 415V and the frequency is 50Hz, the objective is to improve the compensation capability of STATCOM. The proposed work is modeled in the MATLAB /Simulink platform and the system behaviors are verified.

Optimization is a complex process whose success depends on the formulation of the objective function (OF) and the selection of parameters in the optimization algorithm. This paper presents a statistical approach to select the values of such parameters in the optimization algorithms. It uses statistical tests to determine the stability of the algorithm and identify the best value of the parameter. The approach determines the chemotactic parameter in the Bacterial-Foraging Optimization Algorithm (BFOA). The advantage of the proposed approach is that it does not require (i) complete knowledge of the subject, (ii) mathematical analysis to determine the parameter, and (iii) it is extendible to n-dimensional systems. Also, a logical approach, based on the parameter sensitivity and error dynamics, to select the OF for PI controller-tuning problem in Shunt Active Power Filter (SAPF) is presented. The results obtained using the proposed approach are verified analytically.

Electric power has always been one of the driving forces for progress in human life. This has popularized electric energy as the most utilized form of energy. However, dispersed locations of energy resources and continuously increasing demand of electricity have led to a large electric power transmission network across the landscape. To operate the system effectively, a large number of components, such as protection systems, monitoring systems, operational procedures, etc. are required to work in a synchronized and efficient manner; otherwise, contingencies may arise in the system. Development and integration of renewable energy sources into the existing power system has enhanced the complexity in the network. Efficient operation of this complex network is a tedious task for the authorities. Thus, to simplify the planning, operational, and other tasks, grid codes have been developed. Grid codes are the rules laid by the authorities for all its stakeholders, i.e., the users and power generating stations for connecting to the network and operate as per the standards. These grid codes implement the regulations for smooth operation of the grid and its connected components. It implies to the existing and future plants. This chapter gives an overview of the grid codes, its various components and their development considering integration of renewable energy into the grid. Various aspects, such as classification and specifications of the grid codes, the anomalies that exist between the grid codes developed and standards used in conventional power plants are discussed in this chapter.