Renewable Based Power Generation Research Articles

Signal processing and artificial intelligence based HVDC network protection: A systematic and state-up-the-art review

In this modern era, the smart grid (SG) is progressively empowered for direct current (DC) power transmission, either at high voltage (HV) or at medium voltage (MV). The high voltage direct current (HVDC) transmission systems have a significant impact on the SG operation even though the integration of add-on green and renewable-based power generation (RPG). In this RPG era, the application of HVDC can be seen in the grid-connected remote offshore wind or photovoltaic (PV) based large RPG, and interconnections amongst the states or countries. In such a context, a smart grid inevitably is ridden with technical complexities (both control and protection viewpoints), and thus signals processing (SP) techniques along with artificial intelligence (AI) methods are considered very much important to understand, effectively plan, draw up plans, and drive the multifaceted electric power system. This paper presents an extensive review of HVDC network protection systems with special attention to SP and AI applications. This paper presents an extensive review of the suggested techniques in the past three decades and discussed the pros and cons of each method. Apart from that critical findings are discussed and analyzed along with the future scope of research toward the development of HVDC protection. This review tries to focus on the gap between the existing protection schemes and topology with the smart grid-based power system perspective and convey to the power engineers and researchers the possibilities of further research as a solution to the associated issues and challenges.

Performance evaluation and optimization of a multigeneration hybrid system for power, hydrogen, cooling, and freshwater production from renewable sources

Designing a hybrid system emerges as a highly efficient strategy for augmenting the performance of renewable-based power plants, owing to their adaptability in integrating diverse subsystems. In this study, a new multigeneration system is introduced, showcasing its capability to concurrently generate power, hydrogen, cooling, and freshwater through a synergy of methanol-steam reforming, modified absorption refrigeration cycles, humidification-dehumidification, and multi-effect desalination. The operational foundation of the system is rooted in the harmonious integration of solar and geothermal energy sources. To comprehensively evaluate the proposed system's performance, a combination of exergy and economic analyses is employed. This research studies the impacts of varying key parameters on the system's operation. Notably, the water-to-methanol ratio is identified as the most influential variable affecting reformer performance, underscoring the pivotal role of reforming pressure. The estimated total fixed cost of the system stands at $4.039 million, with a calculated payback period of 6 years and a net present value of $3.454 million. Furthermore, in its optimized state, the system achieves an impressive exergy efficiency of 49.80 % and a unit product cost of $32.38/GJ. These findings unequivocally validate the feasibility and economic viability of the proposed hybrid renewable-based power generation system. In conclusion, the amalgamation of solar and geothermal energy, coupled with meticulous parameter optimization, substantiates the potential of this innovative system to serve as a sustainable and economically viable solution for power generation.

Assessment of prosumer-based energy system for rural areas by using TRNSYS software

The prosumers are the new performers progressing towards a low-carbon future. Renewable-based power generation is essential, even in rural areas, to pave a path toward sustainable development. This study establishes a simulation model for two identical residential buildings to assess the prosumer's impact on future local (standalone) energy systems. According to the climate condition of Pakistan, the technical, economic, and environmental performance of the standalone system is evaluated using TRNSYS software. Assumptions and modeling of different components, photovoltaic (PV) panels, and batteries are also discussed. This study provides the starting point of a real-time framework of energy trade between two rural houses connected with a common microgrid. Both houses are medium-sized family houses with almost identical electricity demands. One house has PV mounted on the rooftop, with the battery as an optimal energy storage option. Attention is given to peak demand management and surplus energy during enough production hours. A dynamic energy management approach between two buildings is proposed based on TRNSYS and blockchain. Simulation results show that the real-time implementation of local energy production and energy trading at the household level facilitates achieving the dual benefits of reducing consumer costs and maximizing self-consumption.

Role of green hydrogen in the decarbonization of the energy system in Thailand

High dependence on fossil fuels to meet the energy demand is the major source of greenhouse gas (GHG) emissions in Thailand. Decarbonization of the energy system to achieve the pledged climate targets is a challenging task for Thailand. The role of green hydrogen and hydrogen-based technologies in Thailand's energy transition needs to be explored. A bottom-up energy system model developed using AIM/Enduse framework has been used to assess the energy system transformation required with the focus on the role of hydrogen in achieving net zero GHG emissions. A business-as-usual (BAU) scenario and two net zero emissions (NZE) scenarios with low and high-hydrogen use have been assessed. This study finds that GHG emission reduction in the power sector and the transport sector would be the major contributor in achieving net zero emission in 2050 compared to the projected emissions in the BAU level. Energy efficiency improvement in the demand side would lower the electricity consumption compared to the BAU level despite the higher electrification of the end-use technologies. However, the demand to produce green hydrogen in the NZE scenarios would increase the electricity generation in 2050 b y up to 54 % compared to the BAU level. The capacity of electrolyser would reach 50 GW by 2050 in the high hydrogen use scenario. This would require additional solar and wind power capacity of 107 GW and 43 GW, respectively. In addition, it would require 50 million cubic meters of water. Cumulative investments in electrolysers to produce green hydrogen and renewable based power generation during 2031–2050 would be USD 122 billion.

Optimal Design and Performance Analysis of a Digitally Controlled Biogas Power Generation System Suitable for DC Microgrid

Renewable energy sources are gradually becoming an alternative to fossil fuel-based power sources. It helps in overcoming the energy crisis and minimizing the emission of toxic gases as well as ensuring a step towards achieving a carbon–neutral environment. For the uninterrupted supply of power in the area of the weak distribution of electricity, clean energy sources are proven to be a feasible solution for electrification. This work aims to design a unique concept of a biogas-fuelled power-generating system and analysis performance that can enhance renewable-based power generation. The optimization of the bio-power generation system has been carried out through the HOMER software. The power plant’s performance with a digital control system has been investigated in MATLAB at fully loaded conditions. The efficacy of the system has been validated experimentally on a hardware setup resulting in an increase in power generation efficiency of about 14%. A comparative experimental study has been presented to exhibit the benefit of the designed system over the conventional bio-power generation system like the minimization of power losses and reduced no of associated equipment as required in the conventional system. Besides that, the estimated cost of the design of the power plant and its payback period has also been investigated for a limited period of years. It has been revealed that the proposed design of the Biogas power plant and its performance is substantially efficient and can be recommended for utilization in a microgrid or any small-scale power generating system.

Carbon-Oriented Expansion Planning of Integrated Electricity-Natural Gas Systems With EV Fast-Charging Stations

Under the pressure of climate change and the energy crisis, electric vehicles (EVs) have experienced a marked growth in many countries recently. However, the environmental benefits of EVs cannot be achieved if the charging power from the electricity grid is still mainly provided by fossil-fueled power plants. How to construct a low-carbon fast-charging system has become an emerging research topic. In this context, a novel carbon-oriented expansion planning model of the integrated electricity and natural gas system (IEGS) with EV fast-charging stations (FCSs) is proposed to determine the optimal alternatives, locations, and sizes for ecofriendly candidate assets, including roof-top photovoltaic (PV) panels and fuel cell (FC) units in each FCS, as well as renewable energy units and carbon capture and storage (CCS) systems in IEGS. To guarantee the carbon reduction performance of the planning scheme, the carbon emission cap of the EV charging system is specified by the planner and optimally allocated among multiple FCSs according to their carbon reduction abilities. Furthermore, the proposed model can not only consider the initial investment and operation costs but also take into account the expected adaptation cost to future scenarios that are generated in line with three uncertainties, namely, the traffic flow levels, conventional load levels, and renewable-based power generation levels. Finally, the proposed expansion planning method is illustrated by conducting numerical experiments in case studies.

Forecasting solar-thermal systems performance under transient operation using a data-driven machine learning approach based on the deep operator network architecture

Modeling and prediction of the dynamic behavior of thermal systems operating under intermittent energy input and variable load requirements represent one of the greatest challenges in the development of efficient and reliable renewable-based power generation technologies. In this work, a data-driven machine learning modeling framework was developed based on a modified version of the Deep Operator Network architecture where the time coordinate in the trunk net is replaced with historical data of the predicting quantity. The modeling framework can be used to accurately predict the performance of renewable-based energy conversion technologies including wind- and solar-based power plants. This novel framework was applied on a solar-thermal system that consists of a solar collection loop using a flat plate collector, a power generation loop comprising an Organic Rankine Cycle, and a thermal energy storage tank connecting both loops. Variable solar irradiance, air temperature, and power load profiles were used by the Deep Operator Network to predict the State-of-Charge and the efficiency of the thermal system for several days. The results were compared with the State-of-Charge and efficiency functions calculated using a physics-based model. For a simple operation scenario, characterized by a clear sky solar irradiance profile and constant load, the standard deviation in the State-of-Charge prediction by Deep Operator Network is below 0.9% during a seven-day prediction time horizon. For the most realistic operation scenario that considers real solar irradiance and a rough load profile, the maximum standard deviation in the predictions for the State-of-Charge and efficiency are below 6.8% and 2.5%, respectively. A comparison between Deep Operator Network and Long Short Term Memory network was also performed. In general, both networks predict very well the State-of-Charge for different data density conditions; however, a higher accuracy, with a standard deviation below 2.0%, is obtained by the Deep Operator Network during three and half days using sparser training data of 20-minute points. The same accuracy for the State-of-Charge prediction with the Long Short Term Memory network is achieved only for 14 h. Average standard deviations for the State-of-Charge prediction of 1.1% with the Deep Operator Network and 1.5% with the Long Short Term Memory network are obtained for a four-day prediction time using a denser training data of 5-minute points.

Two-objective optimization of a hybrid solar-geothermal system with thermal energy storage for power, hydrogen and freshwater production based on transcritical CO2 cycle

In the light of increasing negative impacts on environment of fossil fuels, development of renewable energy utilization is a high priority. In this regard, hybridization of geothermal and solar energies (as two types of abundant renewable resources) has been proved to be a promising combination for renewable-based power generation systems. For power generation from low-grade renewable resources, the transcritical CO2 (TRCC) power cycle has been considered as an ambitious competitor against ORC and Kalina cycle. This paper aims at development and proposal of a novel efficient trigeneration system based on TRCC cycle for production of power, hydrogen, and freshwater driven by hybrid solar-geothermal energies. In proposed system, thermoelectric generators and HDH desalination unit are employed for waste heat utilization of TRCC cycle. Thermodynamic and thermoeconomic analyses and two-objective optimization are conducted and monthly performance of the system is estimated for real environmental data. Results showed that, the system yields highest values of power, hydrogen and freshwater in May, respectively as 1286 kW, 1.989 kg/h, and 13.38 m3/day. Under the optimized conditions, the system yields energy efficiency of 23.35% with a unit product cost of 17.07 $/GJ. Also, the results revealed efficiency improvement via increasing the solar energy share. In addition it is shown that, developed system in this work can yield higher efficiency compared to a similar trigeneration system based on the Kalina cycle.

Performance assessment of hybrid active filtering technique to enhance the hosting capacity of distorted grids for renewable energy systems

Increased green energy presence in distribution power networks can resolve rising energy demands while also reducing fossil fuel consumption, as well as providing economic and technical merits. However, the quantity of power delivered to customers cannot be allowed to take precedence over the quality of that electricity. This is because the high penetration of the renewable-based power generation comes at the cost of power quality (PQ) deterioration, especially at the point of common coupling (PCC), owing to nonlinear characteristics of renewable-based distributed generation (DG) systems and pre-contamination of modern distribution grids. So, the function of PQ improvement technologies unavoidably comes into the picture when the hosting capacity (HC) of renewable energy systems is constrained by any of the grid PQ performance indices. This paper investigates the application of a hybrid active power filter (HAPF) to improve the harmonic-constrained HC (HC-HC) of renewable DG systems in harmonically polluted distribution systems. A shunt type HAPF designing to achieve maximum HC-HC is considered as an optimization problem and solved by the firefly algorithm under several constraints the system particularly related to individual and total harmonic distortion limits in line current and voltage at PCC, harmonic currents overloading of distribution feeder. Aside from harmonics, constraints related to voltage rise and drop limits, as well as power factor, are also taken into account to ensure the optimal use of the HAPF as a compensator. The proposed HAPF-based HC-HC improvement approach's findings and efficacy are compared to those acquired from one of the literature's recently proposed techniques. Besides, the study is extended for HC-HC enhancement by HAPF, under changing the utility-side's background voltage distortion and the load-side's nonlinearity level conditions. The performed extended investigation of the system shows that HC-HC enhancing the effectiveness of the proposed approach is rather less susceptible to utility or customer side contribution to background distortion at the PCC. Highlights Hybrid harmonic filtering is investigated for enhancing the harmonic-constrained hosting capacity of distributed generation in distorted grid conditions. A hybrid filter has been found as a better alternative to achieve an advanced level of renewable energy penetration in distorted distribution grids. Along with increasing the penetration level of distributed generation the harmonic distortion is also substantially reduced instead of being at the verge of the standard limit. With the increase in background voltage distortion, the percentage enhancement in HC-HC degrades. High load-side's nonlinearity also results in a relatively lower enhancement in HC-HC. Background nonlinearity negatively influences the percentage of HC enhancement more than the load-side's nonlinearity. Though the utility, as well as customer-side nonlinearity, affect the performance of the hybrid filter also yet the variation is significantly lesser than the purely passive filtering-based approach.

Spatio-Temporal and Power–Energy Scheduling of Mobile Battery Storage for Mitigating Wind and Solar Energy Curtailment in Distribution Networks

Several technical, computational, and economic barriers have caused curtailing a share of renewable-based power generation, especially in systems with higher penetration levels. The Mobile Battery Energy Storage (MBES) can cope with this problem considering the spatial and temporal distribution of the curtailed energy. Accordingly, a new operation model is proposed for optimal scheduling of the MBES in a distribution network with wind and photovoltaic (PV) resources. The network experiences curtailment situations because of bus overvoltage, feeder overload, and power over-generation. The MBES is a truck-mounted battery system compacted in a container. The proposed model seeks to determine the optimal spatio-temporal and power–energy status of the MBES to achieve a minimum curtailment ratio. The model considers transportation time and cost of the MBES efficiently while both active and reactive power exchanges are modeled. The model is linear, without convergence and optimality problems, applicable to real-life large-scale networks, and can be easily integrated into the commercial distribution management software. The implementation results on a test system demonstrate its functionality to recover a considerable share of the curtailed energy for both wind and PV resources at all curtailment patterns and scenarios.

TES-PD: A Fast and Reliable Numerical Model to Predict the Performance of Thermal Reservoir for Electricity Energy Storage Units

The spread of renewable resources, such as wind and solar, is one of the main drivers to move from a fossil-based to a renewable-based power generation system. However, wind and solar production are difficult to predict; hence, to avoid a mismatch between electricity supply and demand, there is a need for energy storage units. To this end, new storage concepts have been proposed, and one of the most promising is to store electricity in the form of heat in a Thermal Energy Storage reservoir. However, in Thermal Energy Storage based systems, the critical component is the storage tank and, in particular, its mathematical model as this plays a crucial role in the storage unit performance estimation. Although the literature presents three modelling approaches, each of them differs in the considered parameters and in the method of modelling the fluid and the solid properties. Therefore, there is a need to clarify the model differences and the parameter influences on plant performance as well as to develop a more complete model. For this purpose, the present work first aim is to compare the models available in the literature to identify their strengths and weaknesses. Then, considering that the models’ comparison showed the importance of adopting temperature-dependent fluid and storage material properties to better predict the system performance, the authors developed a new and more detailed model, named TES-PD, which works with time and space variable fluid and solid properties. In addition, the authors included the tank heat losses and the solid effective thermal conductivity to improve the model accuracy. Based on the comparisons between the TES-PD model and the ones available in the literature, the proposal can better predict the first cycle charging time, as it avoids a 4% underestimation. This model also avoids overestimation of the delivery time, delivered energy, mean generated power and plant round-trip efficiency. Therefore, the results underline that a differential and time-accurate model, like the TES-PD, even if one-dimensional, allows a fast and effective prediction of the performance of both the tank and the storage plant. This is essential information for the preliminary design of innovative large-scale storage units operating with thermal storage.

Application of SMES Technology in Improving the Performance of a DFIG-WECS Connected to a Weak Grid

Wind Turbine Generator (WTG) has become one of the most popular renewable-based power generation that is broadly connected to electricity grids worldwide. Till the year 2019, the total WTGs installed worldwide reached about 650.8GW. The WTG is interfaced to the electricity grid through power electronic converters with a proper control algorithm to facilitate a smooth power delivery as well as maintaining the system voltage and frequency stability during wind intermittency. However, power grids are usually subjected to load expansion which affects the stiffness of the grid and hence its stability. A weak electricity grid exhibits voltage instability that may affect the performance of WTGs and in some cases may lead to serious damages to the wind turbines and the entire system. In this paper, superconducting magnetic energy storage (SMES) technology based on fuzzy logic controller is implemented to effectively resolve this issue and improve the overall performance of WTGs. Hysteresis current and fuzzy logic-based control system is proposed to control the energy exchange between the SMES coil and the investigated system. Results show the effectiveness of the SMES to improve the overall system performance and along with the fault ride-through capability of the doubly-fed induction generator (DFIG).

Current Control Mode Based Analysis of Grid Connected PV System

Renewable based power generation is rising rapidly due to high energy demand and carbon issues. Partial shading effect of the PV system leads to a significant reduction of the energy yield and quantity of reduction is depending on the geometry of shading effect. The incremental conductance and integral controller are used in this paper to display the maximum power point in the PV device. During bad weather conditions, insulated PV systems do not have enough power to supply the consumer. The key suggestions of this paper are to use the deficient power needed for the PV system through grid and to fed the PV system surplus power to the grid. The new control mode is used to monitor the PV current that uses two control loops for feedback. The current mode control system is a scheme for regulating electronic DC-DC power converters. On a PV system using MATLAB/SIMULINK a complete model of 100KW grid connected solar photovoltaic system using current mode controlled two-level three-phase VSC.

Deep Reinforcement Learning-Based Voltage Control to Deal with Model Uncertainties in Distribution Networks

This paper addresses the voltage control problem in medium-voltage distribution networks. The objective is to cost-efficiently maintain the voltage profile within a safe range, in presence of uncertainties in both the future working conditions, as well as the physical parameters of the system. Indeed, the voltage profile depends not only on the fluctuating renewable-based power generation and load demand, but also on the physical parameters of the system components. In reality, the characteristics of loads, lines and transformers are subject to complex and dynamic dependencies, which are difficult to model. In such a context, the quality of the control strategy depends on the accuracy of the power flow representation, which requires to capture the non-linear behavior of the power network. Relying on the detailed analytical models (which are still subject to uncertainties) introduces a high computational power that does not comply with the real-time constraint of the voltage control task. To address this issue, while avoiding arbitrary modeling approximations, we leverage a deep reinforcement learning model to ensure an autonomous grid operational control. Outcomes show that the proposed model-free approach offers a promising alternative to find a compromise between calculation time, conservativeness and economic performance.

A chance-constrained energy management in multi-microgrid systems considering degradation cost of energy storage elements

Energy storage devices are very popular in systems with the presence of renewable-based power generation. However, inappropriate usage of them may lead to aging and also the early failure of such systems. The factors affecting the aging of the batteries as the main energy storage devices in electrical systems are frequent changes in charging and discharging status, deep discharge, and a large number of power transactions in a short time. Furthermore, the development of microgrids (MGs) in distribution networks (DNs), leads to more complicated structures known as multi-microgrid (MMG) systems. In this paper, the day-ahead scheduling for the MMG system considering the degradation cost of the energy storage systems has been proposed. To model the uncertainties, chance-constrained programming (CCP) approach has been employed. The CCP helps the system operator to make better decisions without jeopardizing the system security. The degradation cost has been designed in a way that in the energy management process, misuse of the batteries is avoided. A hierarchical three-stage energy management process is designed. First, deterministic equivalents for the CCP problem are made and using these equivalents, in the first stage, MGs perform local scheduling and in the second stage, again considering deterministic equivalents of the CCP, DN performs global management and finally, MGs perform a rescheduling. Simulations with four case studies on a modified version of the IEEE 33-bus test system have shown the effectiveness of the proposed scheduling framework. The results show that decreasing confidence level in CCP decreases the system operational costs, however, threatens the system stability. Also, considering the battery degradation cost prevents the system operator from misapplication of the battery energy storage systems.

Capacity optimization of grid connected solar/fuel cell energy system using hybrid ABC-PSO algorithm

Hybrid renewable based power generation has emerged as a major breakthrough in electrical power system due to their capability of providing competitive and carbon free electricity to the consumer. This paper presents techno-economic analysis of a grid connected solar photovoltaic-fuel cell based hybrid energy system to supply the electrical load demands of a small shopping complex located at the University campus in India. The proposed system has been designed by considering the cost of energy purchase and sold to the utility grid and further ensuring the smooth power flow management between elements of the system and the utility grid. A simplified mathematical modeling of the proposed hybrid energy system, including power and hydrogen management has been presented to access the potential of hydrogen based energy systems. Component sizing to optimize the net present cost of the proposed system has been performed employing three meta-heuristic algorithms, i.e., artificial bee colony, particle swarm optimization and a hybrid of both. The experimental results obtained from three algorithms have been compared in terms of cost effectiveness of the system. The hybrid system has been designed to cater the total electricity demand of 135 MWh/yr of a small community center. It has emerged from the simulation results that the total electricity demand could be met with a 106 kW solar photovoltaic, 8 kW fuel cell, 45 kW electrolyzer and a 150 kg of hydrogen tank. The proposed grid connected system has 30 kW grid purchase and 25 kW grid sale capacities, respectively. In this paper, grid sale and purchase capacities have been considered as a decision variable along with other system component sizing. Loss of power supply probability is minimized along with objective function i.e., levelized cost of electricity (LCOE) using the exterior penalty method. It is evident from the results that the proposed system is economically viable with a LCOE of 0.104 $/kWh. It has emerged from the results that the proposed hybrid energy system may be helpful to promote hydrogen and solar based energy system to reduce the reliance on the overburden grid, particularly, in developing countries.

Real-time Battery Energy Management for Residential Solar Power System

Abstract An energy storage system is a key element of renewable-based power generation. Its flexible operational capabilities reduce not only the impact of intermittent power generation but also operational costs. In this paper, a dynamic penalty function is proposed to the charging term of the cost function to efficiently manage the battery energy and thereby reducing operational costs. The charging/discharging periods of the battery are effectively controlled based on the solar power generation and residential real-time electricity prices (RRTP). The optimisation problem formulated for the application of real-time energy management is solved with the help of particle swarm optimisation (PSO). It is shown that the proposed cost function can reduce operational costs over a time horizon of 96 hours by 4.2 per cent as compared to the cost function reported in the literature. Simulation studies are carried out to demonstrate the effectiveness of the proposed cost function over the existing cost function.

Excess electricity and power-to-gas storage potential in the future renewable-based power generation sector in the United Arab Emirates

Future power generation scenarios for the United Arab Emirates (UAE) that emphasize solar photovoltaic (PV) and concentrated solar power (CSP) with thermal energy storage are analyzed at PV:CSP generation ratios of 1:1 to 4:1, and up to 50% renewable share. Such scenarios enable up to 24–38% reduction in primary fuel consumption at 30–50% renewable share, respectively, relative to the base case scenario (i.e., business-as-usual), with accompanying reductions in carbon dioxide emissions of up to 34–54%, respectively. In parallel, over the same range of renewable shares, excess electricity is generated in the range of 1–13 TWh (0.4–5% of annual demand) and 3–25 TWh (1–10%) annually, at PV:CSP ratios of 1:1 and 4:1, respectively. Monthly excess electricity maxima and minima occur in winter and summer, respectively. At 40% renewable electricity share, the PV:CSP 4:1 scenario would lead to annual power generation costs, inclusive of power-to-gas (PtG), approximately 11% higher than for the base case scenario, and 13% lower than for the PV:CSP 1:1 scenario. At an electricity tariff of 0.05 USD/kWh and 50% capacity utilization, the levelized costs of hydrogen and synthetic natural gas (SNG) are estimated at 101 USD/MWhth and 127 USD/MWhth, respectively.

The Philippines energy future and low-carbon development strategies

This paper presents an assessment of alternative, long-term energy supply and low-carbon strategies for the Philippine power sector from 2014 to 2040 using TIMES model. It examines the potential contribution of renewable energy to diversify the Philippine energy supply-mix to meet future electricity demands. The reference scenario compares the impact of four alternative policy goals: (1) carbon tax, (2) targeted renewable-based power generation, (3) limited coal share in supply-mix, and (4) renewables subsidy. The reference scenario shows a significant increase of the share of coal-based power generation and import dependency of fossil-fuel increases from 227 PJ in 2016 to 1073 PJ in 2040. The model results for the alternative policy scenarios show a large potential for renewable energy-based power generation. The alternative policy options show a significant decrease of import dependency in the energy supply-mix for power generation. Most alternative policy scenarios project a higher total system cost, with the exception of the subsidy scenario. System cost increases only 2.6% in the renewables target scenario relative to the reference scenario. However, long-term benefits from investing in the alternative policy options would need to be considered, including diversification of energy supply-mix, improved energy security, and progress toward a low-carbon society.

Harmonics-Based Enhanced Passive Islanding Method for Grid-Connected System

Now a day, distributed sources are emerging widely due to their advantages owing to renewable-based power generation. In this paper, a novel passive islanding method for the inverter-based grid-connected system is presented. This method can be suitably applied along with the conventional passive methods like under/over voltage and under/over frequency islanding techniques. Total harmonic distortion (THD) at the point of common coupling (PCC) is used to discriminate the islanding condition, and to safely disconnect the grid. This method has an advantage that it is able to discriminate non-detection zone (NDZ) when the local load matches the inverter output power. The results using MATLAB simulations and laboratory prototype have been presented.