Renewable Energy-based Microgrid Research Articles

Renewable energy-based electrical microgrid of cold ironing energy supply for berthed ships

The importance of ports, which are the gateways between maritime transport and other modes of transport, is growing every day. In addition, the amount of cargo that ports can handle is increasing rapidly every year. At the same time, the need for energy is increasing. Ships hoteling at ports account for a large portion of the power demand at ports. Today, ships hoteling at ports meet their energy needs with their own auxiliary engines running on fossil fuels. In order to achieve decarbonization and zero emissions targets, it is essential to minimize the use of fossil fuels in ports and to increase the use of renewable energy. In this context, meeting the ship's power needs in port through a renewable energy-based microgrid will help reduce emissions. In this study, after determining the energy needs, the scenarios developed with the HOMER program were used to design electrically and economically suitable microgrid systems and to meet the electricity needs of the ships in port using renewable energy.

Techno-Economic Planning of a Fully Renewable Energy-Based Autonomous Microgrid with Both Single and Hybrid Energy Storage Systems

This paper presents both the techno-economic planning and a comprehensive sensitivity analysis of an off-grid fully renewable energy-based microgrid (MG) intended to be used as an electric vehicle (EV) charging station. Different possible plans are compared using technical, economic, and techno-economic characteristics for different numbers of wind turbines and solar panels, and both single and hybrid energy storage systems (ESSs) composed of new Li-ion, second-life Li-ion, and new lead–acid batteries. A modified cost of energy (MCOE) index including EVs’ unmet energy penalties and present values of ESSs is proposed, which can combine both important technical and economic criteria together to enable a techno-economic decision to be made. Bi-objective and multi-objective decision-making are provided using the MCOE, total met load, and total costs in which different plans are introduced as the best plans from different aspects. The number of wind turbines and solar panels required for the case study is obtained with respect to the ESS capacity using weather data and assuming EV demand according to the EV population data, which can be generalized to other case studies according to the presented modelling. Through studies on hybrid-ESS-supported MGs, the impact of two different global energy management systems (EMSs) on techno-economic characteristics is investigated, including a power-sharing-based and a priority-based EMS. Single Li-ion battery ESSs in both forms, new and second-life, show the best plans according to the MCOE and total met load; however, the second-life Li-ion shows lower total costs. The hybrid ESSs of both the new and second-life Li-ion battery ESSs show the advantages of both the new and second-life types, i.e., deeper depths of discharge and cheaper plans.

Hybrid whale artificial bee colony optimized improved Landsman converter for renewable energy-based microgrid application

Hybrid whale artificial bee colony optimized improved Landsman converter for renewable energy-based microgrid application

Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty

Microgrids can assist in managing power supply and demand, increase grid resilience to adverse weather, increase the deployment of zero-emission energy sources, utilise waste heat, and reduce energy wasted through transmission lines. To ensure that the full benefits of microgrid use are realised, hybrid renewable energy-based microgrids must operate at peak efficiency. To offer an optimal solution for managing microgrids with hybrid renewable energy sources (HRESs) while taking microgrid reserve margins into account, the particle swarm optimisation (PSO) method is suggested. The suggested approach demonstrated good performance in terms of charging and discharging BESS and maintaining the necessary reserve margins to supply critical loads if the grid and renewable energy sources are unavailable. On a clear day, the amount of electricity sold to the grid increased by 58%, while on a partially overcast day, it increased by 153%. Microgrids provide a good return on investment for their operators when they are run at peak efficiency. This is because the BESS is largely charged during off-peak hours or with excess renewable energy, and power is only purchased during less expensive off-peak hours.

An Improved Power Quality in a Renewable Energy-based Microgrid System Using Adaptive Hybrid UPQC Control Strategy

Due to its ability to integrate renewable energy, improve energy efficiency, and fortify the power system's resilience, microgrids are widely used as regional energy systems. But these advantages do have certain drawbacks in terms of control and Power Quality (PQ). In order to guarantee the proper operation of equipment that is connected and the system's general health, PQ is crucial in microgrids. For microgrids to operate successfully, governing stratagems in concurrence with front-line power electronics devices bid a firm context to knob PQ challenges like Voltage Sag and Swell, Source/Grid current harmonics, Voltage imbalances, Active and Reactive power compensation etc. Multifunctional systems that can integrate clean energy generation and as well as enhance PQ are necessary to meet the demands of complex loads that have the capability of operating in the situation of an unavailable network grid and power electronics devices, which necessitates the need for clean energy. Hence, this paper provides enactment of an adaptive hybrid control strategy based on an Adaptive Leaky Least Mean Square (AL_LMS) algorithm combined with Fuzzy Logic (FL) to the Unified Power Quality Conditioner (UPQC) in a Solar-PV energy based Microgrid system in improving microgrid power quality. The concert of UPQC is assessed by the conventional PI controller and Fuzzy Logic control with MATLAB/SIMULINK software platform, simulation results are conversed with proportionate studies in improving the PQ by minimizing Total Harmonic Distortion (THD), the Voltage Sag & Swell and the result comparison shows the effectiveness of the Fuzzy Logic in coordination with the AL_LMS algorithm resulting improved power quality within the IEEE Power Quality-519 Standards.

Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty

Weather-driven uncertainties and other extreme events, particularly with the increasing reliance on variable renewable energy (VRE), have made achieving a reliable microgrid operation increasingly challenging. This research proposes a comprehensive and integrated planning strategy for capacity sizing and operational planning, incorporating forecasting and demand response program (DRP) strategies to address microgrid operation under various conditions, accounting for uncertainties. The microgrid includes photovoltaic systems, wind turbines, and battery energy storage. Uncertainties in VREs and load fluctuations are modeled using Monte Carlo simulations (MCSs), while forecasting is based on the long short-term memory (LSTM) model. To determine the best techno-economic planning approach, six cases are formulated and solved using a multi-objective particle swarm optimization with multi-criteria ranking for these three objectives: total lifecycle costs (TLCC), reliability criteria, and surplus VRE curtailment. Shortage/surplus adaptive pricing combined with variable peak critical peak pricing (SSAP VP-CPP) DRP is devised and compared with a time-of-use VP-CPP DRP in mitigating the impacts of both critical and non-critical events in the system. The simulation results show that the integrated planning, which combines LSTM forecasting with DRP strategies, achieved about 7% and 5% TLCC reductions for deterministic and stochastic approaches, respectively. The approach allowed optimal sizing and operation planning, improving the utilization of VREs and effectively managing uncertainty, resulting in the most cost-effective and robust VRE-based microgrid with enhanced resilience and reliability.

Optimal design and performance analysis of coastal microgrid using different optimization algorithms

AbstractOwing to the stochastic behavior of renewable energy activity and the multiple design considerations, the advancement of hybrid renewable energy-based microgrid (HREMG) systems has become a complex task. This study proposes a design optimization algorithm for the long-term operation of an autonomous HREMG along with the optimal system capacities. The investigated energy system comprises photovoltaic panels, wind turbines, diesel generators, and batteries. It aims to energize a remote coastal community with a daily load demand of 400 kWh in Marsa Matruh, Egypt. Since most studies utilize commercial tools in the design optimization procedure, the African vultures optimization approach (AVOA) is developed to find the optimal energy alternative and determine the optimal component’s capacity considering achieving the minimum energy cost and loss of power supply probability. Moreover, an adequate energy management strategy is suggested to coordinate the power flow within the energy system in which renewable energy sources are fully penetrated. To check the AVOA robustness and efficacy, its performance is compared with the HOMER Pro most popular commercial tool as well as with new metaheuristic algorithms, namely the grasshopper optimization algorithm (GOA) and Giza pyramid construction (GPC) under the same operating environment. The results revealed that the proposed AVOA achieved superior economic results toward the least net present cost ($346,614) and energy price (0.0947 $/kWh). Moreover, over 20 independent runs, the AVOA showed a better performance in terms of convergence and execution time compared to other tools/algorithms. The obtained findings could be a useful benchmark for researchers in the sizing problem of hybrid energy systems.

Optimal generation planning in a micro-grid for supplying electrical and thermal loads in order to reduce pollutant emissions

The depletion of fossil fuels and growing environmental concerns have propelled the need for renewable energy sources. Micro-grids (MGs) have emerged as a viable solution for utilizing these resources effectively. This paper addresses the unit sizing problem for a two MG system using an intelligent method that considers air pollution emissions while catering to electrical and thermal loads. The MG incorporates photovoltaic (PV) panels, wind turbines (WTs), and a storage energy system comprising an electrolyzer, hydrogen tank, and fuel cell (FC) for combined heat and power (CHP) generation. In cases of excess power generation, an electric heater and backup boiler are employed to meet thermal loads. To account for air pollutant emissions, it is considered as a cost constraint in the objective function. Reliability is ensured by incorporating load curtailment costs as a limiting constraint. The MG is simulated in both islanding and grid-connected modes, with a generation planning program considering load growth. The IEEE RTS data is used for a peak 500 kW electrical load, and seasonal climate changes are factored in when deriving the thermal load. The modified particle swarm optimization (MPSO) algorithm is employed to find the global optimal solution. This research addresses key motivations, problems, and solutions in the field of renewable energy-based MGs. It introduces hydrogen tanks and batteries as storage systems to enhance system reliability. The integration of complementary energy sources, such as wind and solar, is proposed to improve system reliability and affordability. The use of batteries, solar, wind, and boilers for electrical and thermal load provision is more economically viable than hydrogen storage due to fuel prices. The proposed model significantly reduces pollution, enhances efficiency, decreases MG costs, and reduces dependence on fossil fuels. Integration with the grid reduces boiler usage, minimizes additional wasted power, generates income from power sales, and further reduces costs. Optimal size determination reveals the affordability challenges of wind and solar sources compared to microturbines, necessitating government intervention through subsidies or penalties for polluting resources. The results demonstrate that increasing subsidies expands the use of wind and solar resources, while the growth of fuel prices impacts resource size and necessitates storage use. Fine-tuning the penalty factor reduces pollution, but exceeding the limit reduces the adoption of renewables. Lastly, interest rate increases influence resource installations in different stages of the development plan. The outcomes of this study highlight the feasibility and effectiveness of the proposed model in addressing the challenges of MG design, operation, and integration of renewable energy sources. The findings contribute to the advancement of sustainable energy systems and provide valuable insights for policymakers and researchers in the field.

Towards smart energy management for community microgrids: Leveraging deep learning in probabilistic forecasting of renewable energy sources

The intermittent nature of renewable resources like solar and wind presents challenges for small-scale energy markets and off-grid regions. Localized forecasting of these resources is crucial to incentives the attention of citizens and urban designers in adopting small-scale generation systems, i.e., hybrid or renewable energy-based microgrids, in their communities. This study employs Encoder–Decoder Sequence-to-Sequence (Seq2Seq) models with two different recurrent neural network architectures (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU), to forecast wind speed and solar radiation on a location appointed to install microgrids. The models were trained using meteorological data obtained from weather stations while taking into account data on extreme weather events, such as wind gusts, to enhance the predictive accuracy of the models. Particularly relevant in the context of the increasing menace of climate change and the resulting rise in the frequency and severity of extreme weather events. An ensemble method is also employed to quantify uncertainty in the model’s prediction to increase the confidence that an end-user of a microgrid can deposit in the forecasting for decision-making. We compared the Seq2Seq-LSTM and Seq2Seq-GRU models using deterministic and probabilistic metrics. Both models showed satisfactory accuracy and confidence in wind speed and solar radiation forecasting. However, the Seq2Seq-GRU model stood out for its significantly faster computation times. On average, it achieved a 20% reduction in processing time for the same dataset. The models performed best for shorter prediction horizons (1-hour) and higher lookback periods (k = 720-hrs). We also analyzed the models’ performances according to the season. The results show that the seasonal variation significantly affects the accuracy and uncertainty of wind speed forecasting, with fall and summer demonstrating the highest accuracy and winter and spring the lowest. For solar radiation, the models show greater reliability, narrower prediction intervals, and higher accuracy during winter, with opposite trends observed during summer. The deep learning framework proposed in this study can help anticipate daily surpluses or shortfalls in energy generation across different seasons, contributing to the efficient operation of microgrids.

Optimizing Integration of Fuel Cell Technology in Renewable Energy-Based Microgrids for Sustainable and Cost-Effective Energy

This article presents a cost-effective and reliable solution for meeting the energy demands of remote areas through the integration of multiple renewable energy sources. The proposed system aims to reduce dependence on fossil fuels and promote sustainable development by utilizing accessible energy resources in a self-contained microgrid. Using the Hybrid Optimization Model for Electric Renewable (HOMER) software, the study examined the optimal combination of energy sources and storage technologies for an integrated hybrid renewable energy system (IHRES) in the Patiala location of Punjab. The total life cycle cost (TLCC) is the main objective of this manuscript. The HOMER result is taken as a reference, and the results are compared with the optimization hybrid algorithm (PSORSA). From this, it is clear that the proposed algorithm has less TLCC as compared to others. Two combinations of energy sources and storage technologies were considered, namely solar photovoltaic (PV)/battery and solar PV/fuel cell (FC). The results showed that the solar PV/FC combination is more cost-effective, reliable, and efficient than the solar PV/battery combination. Additionally, the IHRES strategy was found to be more economically viable than the single energy source system, with lower total life cycle costs and greater reliability and efficiency. Overall, the proposed IHRES model offers a promising solution for meeting energy demands in remote areas while reducing dependence on fossil fuels and promoting sustainable development.

Optimal Capacity and Operational Planning for Renewable Energy-Based Microgrid Considering Different Demand-Side Management Strategies

A bi-objective joint optimization planning approach that combines component sizing and short-term operational planning into a single model with demand response strategies to realize a techno-economically feasible renewable energy-based microgrid is discussed in this paper. The system model includes a photovoltaic system, wind turbine, and battery. An enhanced demand response program with dynamic pricing devised based on instantaneous imbalances between surplus, deficit, and the battery’s power capacity is developed. A quantitative metric for assessing energy storage performance is also proposed and utilized. Emergency, critical peak pricing, and power capacity-based dynamic pricing (PCDP) demand response programs (DRPs) are comparatively analyzed to determine the most cost-effective planning approach. Four simulation scenarios to determine the most techno-economic planning approach are formulated and solved using a mixed-integer linear programming algorithm optimization solver with the epsilon constraint method in Matlab. The objective function is to minimize the total annualized costs (TACs) while satisfying the reliability criterion regarding the loss of power supply probability and energy storage dependency. The results show that including the DRP resulted in a significant reduction in TACs and system component capacities. The cost-benefit of incorporating PCDP DRP strategies in the planning model increases the overall system flexibility.

An Improved Control Strategy to Reduce Operating Hours of DG Genset in Solar PV-BES-DG Based AC Microgrid

The diesel-engine (DG) Genset-based microgrids are extensively used in remote areas. However, DG Genset 24 hours operation increases the overall cost of electricity and also pollutes the environment, diminishing the main purpose of renewable energy-based microgrids. However, the microgrid's dependency on the DG Genset cannot be eliminated, but it can be minimized by using a proper control strategy. By focusing on it, this work presents an improved control strategy to reduce the DG Genset's working hours in a solar photovoltaic (SPV) battery energy storage (BES) and DG Genset-based microgrid. The designed scheme is based on a set of rules, which are formulated by checking the status of the SPV array generation, BES, and load demand, and accordingly operates the microgrid with DG Genset or without DG Genset modes. Compared to the existing state-of-the-art scheme, the designed scheme operates the DG Genset during only an emergency, reducing its overall working hours. In addition, it provides a stable microgrid operation compared to the existing scheme, when the microgrid's operating mode is shifted from one to another. Besides, DG Genset is operated at its rated power with a unity power factor, giving maximum fuel efficiency. Its power quality performance is also monitored and ensured that the IEEE std. 519 does not defy in case of nonlinear/unbalanced loading. To improve power quality, the designed control scheme uses the cascaded generalized second-order integrator (C-SOGI) based phase-locked loop (PLL). A comprehensive simulation study is performed to show the advantages of the developed scheme over the existing schemes, which is supported by experimental results.

Environmental assessment of optimized renewable energy-based microgrids integrated desalination plant: considering human health, ecosystem quality, climate change, and resources.

Using hybrid renewable energy technology is an efficient method for greenhouse gas mitigation caused by fossil fuel combustion. However, these renewable microgrids are not free from environmental damages, especially during the lifetime of hybrid renewable energy systems (HRES). The main objective of this study is to assess the environmental impacts of three optimized HRES for the Sea Water Reverse Osmosis Desalination (SWROD) plant. An objective optimization was developed using the division algorithm, and the environmental impacts of the optimized HRES were investigated by the life cycle assessment approach. The results showed that producing 1m3 freshwater by an optimal size SWROD integrated with wind turbine/battery is responsible for 3.56E - 07 disability-adjusted life year (DALY). It is significantly less than 1m3 freshwater production by an optimal size SWROD integrated with solar PV/battery (5.88E - 07 DALY) and solar PV/wind turbine/battery (5.13E - 07 DALY) energy systems. Moreover, 1m3 freshwater by a SWROD integrated with proposed microgrids in this study led to a damage of 0.089 to 0.193 potentially disappeared fraction of species (PDF)*m2*yr to ecosystem quality. It also results in an emission of 0.143 to 0.339kg CO2 eq per 1m3 freshwater. Furthermore, resources for 1m3 freshwater production by a SWROD are calculated at 2.77 to 4.806MJ primary. Freshwater production by an optimal size SWROD integrated with solar wind/battery compared with solar PV/battery and solar PV/wind turbine/battery had less damage to ecosystem quality, climate, and resources. The results showed reductions of 91.23% in human health, 73.51% in an ecosystem quality, 92.43% in climate change, and 90.08% in resources for producing 1m3 of freshwater using SWROD integrated with wind turbine/battery bank compared to fossil-based desalination. Finally, the result showed that solving the optimization problem using the division algorithm compared to other algorithms leads to less environmental damage in freshwater production.

Techno-Economic Analysis of Renewable-Energy-Based Micro-Grids Considering Incentive Policies

Renewable-energy-based microgrids (MGs) are being advocated around the world in response to increasing energy demand, high levels of greenhouse gas (GHG) emissions, energy losses, and the depletion of conventional energy resources. However, the high investment cost of the MGs besides the low selling price of the energy to the main grid are two main challenges to realize the MGs in developing countries such as Iran. For this reason, the government should define some incentive policies to attract investor attention to MGs. This paper aims to develop a framework for the optimal planning of a renewable energy-based MG considering the incentive policies. To investigate the effect of the incentive policies on the planning formulation, three different policies are introduced in a pilot system in Iran. The minimum penetration rates of the RESs in the MG to receive the government incentive are defined as 20% and 40% in two different scenarios. The results show that the proposed incentive policies reduce the MG’s total net present cost (NPC) and the amount of carbon dioxide (CO2) emissions. The maximum NPC and CO2 reduction in comparison with the base case (with incentive policies) are 22.87% and 56.13%, respectively. The simulations are conducted using the hybrid optimization model for electric renewables (HOMER) software.

Energy management of renewable energy-based microgrid system with HESS for various operation modes

This paper proposes a ramp-rate control approach for a grid-connected MG with a hybrid energy storage system. Distributed energy sources (DERs), such as solar photovoltaic (PV) and wind, combined with energy storage (ES) and controllable loads, are critical to a power grid that can handle the intermittent nature of renewable energy sources. Therefore, the complexity of the system is increasing as researchers move towards a more renewable based power grid. An energy management system (EMS) for microgrids must consider the power available in RESs as well as the storage capacity of energy storage devices (ESSs). Modern MGs include a wide range of converters for a variety of applications, including distributed generation interconnection, grid integration, energy storage management systems, and demand management, among others. So, the ramp-rate control smooths fluctuations in photovoltaic power, which increases system reliability. In the proposed system, 80 V DC is used to supply high and low power DC loads. The suggested system can extract the maximum amount of energy from RESs, maintain efficient ESS management, and achieves quick DC-link voltage regulation with settling time of 230 ms throughout all operating modes. These conditions are met by the energy management system, which gives the MG with operational capability and ensures its reliability. The MG with proposed features and EMS was validated using the MATLAB/Simulink environment, and results were obtained using the Hardware-in-the-Loop (HIL) experimental test bench. The proposed small-scale RES-based MG can be used to develop and test algorithms for a wide range of MG applications.

Two-stage coordinated operation framework for virtual power plant with aggregated multi-stakeholder microgrids in a deregulated electricity market

The virtual power plant (VPP) is a promising paradigm to promote the integration of renewable energy-based microgrid (MG) into the power system. This paper addresses the coordinated operation and energy trading problem for VPP aggregated with multi-stakeholder MGs. A two-stage hierarchical coordinated operation framework for VPP is developed consisting of the day-ahead bidding and intra-day dispatch fully considering the settlement of deregulated electricity market, the profit of the VPP operator and the aggregation interest of MGs. To cope with the intra-day operational uncertainties of individual MGs, receding horizon-based model predictive control (MPC) is adopted and extra power balancing services are provided by the VPP operator. The proposed solution is extensively assessed through simulation experiments in presence of four MGs with different ownerships and penetration levels of RDERs, and the numerical results confirm that the proposed solution outperforms two benchmarks. Further, the comparison with two existing methods demonstrates that the adoption of time-of-use price (ToUP) and extra power balancing services in the proposed solution can benefit both MGs and the VPP operator with enhanced energy management performance.

Uncertainty modeling methods for risk-averse planning and operation of stand-alone renewable energy-based microgrids

The accuracy of models to capture the uncertainty of renewables significantly affects the planning and operation of renewable energy-based stand-alone (REB-SA) microgrids. This paper aims to first study different stochastic and deterministic models for renewables, then evaluate the performance of an REB-SA microgrid planning problem and provide qualitative and quantitative comparisons. A modified Metropolis-coupled Markov chain Monte Carlo simulation is considered for the first time in the planning of an REB-SA microgrid to predict the behavior of renewables with minimum iterations. The modified model is benchmarked against two prevalent models including the retrospective model with worst-case scenarios and the Monte Carlo simulation. The operations of three designed microgrids (by these three methods) are evaluated using the last three-year historical data of a city in northern Sweden including solar radiation, wind speed, the water flow of a river, and load consumption. The impacts of the considered methods on using PV panels and hydrogen systems are investigated. The results verify that the modified model decreases the risk of planning and operation of an REB-SA microgrid from the energy and power shortage viewpoints. Moreover, the designed microgrid with the modified model can cope with all possible scenarios from economic, technical, and environmental viewpoints.

Optimization of stand-alone and grid-connected hybrid solar/wind/fuel cell power generation for green islands: Application to Koh Samui, southern Thailand

This paper presents the optimization of stand-alone and grid-connected hybrid power generation systems for green islands, with application to Koh Samui in southern Thailand. A techno-economic optimization analysis is applied using the Hybrid Optimization Model for Electric Renewable (HOMER) Pro simulation tool. Four scenarios are identified to select the most suitable solution for a hybrid renewable energy system (HRES) integrating solar photovoltaic (PV), wind turbine generator (WTG), fuel cell (FC), and battery energy storage (Li-Ion), with backup diesel generation or grid connection with the mainland as options. The NASA-SSE and MERRA databases are used as inputs to analyze the solar and wind energy potentials, respectively. The results show that the levelized cost of energy (LCOE) of the HRES with a grid connection to the mainland (Scenario 3) has the lowest LCOE (0.132 US$/kWh), but at large greenhouse gas (GHG) emission costs (20.5 ktonnes/year) due to the high carbon intensity of Thailand’s power portfolio. A stand-alone, 100% renewable energy system (Scenario 4) includes a PV capacity of 182 MW, a wind power capacity of 8 MW, a fuel cell system of 10 MW, a 17.9 MW power converter, and 211 MWh of battery storage. The net present cost (NPC) of this system is 542 MUS$ and the LCOE is 0.309 US$/kWh, with 89% of the energy generated by the solar PV system. Therefore, a 100% renewable energy-based microgrid system is possible on Koh Samui as it can provide a more suitable and reliable solution when considering a HRES system for the total load demand (104 MW of peak load) of the island. Finally, the economic analysis also reveals that the investment in a HRES system is feasible as the payback period is 9 years under an internal rate of return (IRR) of 10% and an appropriate Feed-in-Tariff (FiT) of 0.385 US$/kWh.

A novel application of jellyfish search optimisation tuned dual-stage (1 + PI)TID controller for microgrid employing electric vehicle

ABSTRACT The main purpose of this paper is to implement a novel jellyfish search optimisation-based dual-stage (1+ proportional integral) tilt-integral derivative controller in an isolated and two-area renewable energy-based microgrid cluster to achieve stabilisation of frequency and tie-line power in the system. To fulfil this objective, the proposed cluster consists of a dish Stirling solar generator, micro-hydro turbine, diesel generator, flywheel energy storage device, super magnetic energy storage device and electric vehicle. The supremacy of the dual-stage controller over simple proportional integral derivative controller as well as tilt order controllers such as tilt integral derivative filter double integral and tilt integral tilt derivative with filter is demonstrated. The performance of the jellyfish search optimisation-based dual-stage controller is also compared with the black widow optimisation algorithm, genetic algorithm and particle swarm optimisation-based controller with respect to overshoot, undershoot, settling time and figure of demerit. In addition to this, a sensitivity analysis has been executed by changing the system parameters in the range of ±20% from its nominal value. Lastly, an effort is made to assess the impact of controllable load i.e. electric vehicle with the proposed controller in isolated microgrid.

Optimal planning of green microgrid with minimisation of environmental emissions

In this article, optimal usage of renewable energy sources employed in an Indian microgrid network located in a rural district area of Orissa state, i.e. Sundargarh, is presented. Unlike the existing literature on microgrid development strategies for the Indian microgrid scenario, in this study, the known renewable energy fraction is being utilised to determine an optimal solution for the usage of different variants of possible renewable energy sources employed in a microgrid network. Note that while considering the operating cost, total net present cost, levelized cost of energy and environmental emissions herein the optimal size of wind, solar photovoltaic, hydro, diesel generator, power converter and battery are altogether considered for evaluating the performance of the microgrid network. Nevertheless, this study facilitates estimating the overall cost reduction in a heavily dispersed renewable energy-based microgrid network.