Power Generation Of Renewable Sources Research Articles

Optimal Bidding Scheduling of Virtual Power Plants Using a Dual-MILP (Mixed-Integer Linear Programming) Approach under a Real-Time Energy Market

In recent years, the energy industry has increased the proportion of renewable energy sources, which are sustainable and carbon-free. However, the increase in renewable energy sources has led to grid instability due to factors such as the intermittent power generation of renewable sources, forecasting inaccuracies, and the lack of metering for small-scale power sources. Various studies have been carried out to address these issues. Among these, research on Virtual Power Plants (VPP) has focused on integrating unmanaged renewable energy sources into a unified system to improve their visibility. This research is now being applied in the energy trading market. However, the purpose of VPP aggregators has been to maximize profits. As a result, they have not considered the impact on distribution networks and have bid all available distributed resources into the energy market. While this approach has increased the visibility of renewables, an additional method is needed to deal with the grid instability caused by the increase in renewables. Consequently, grid operators have tried to address these issues by diversifying the energy market. As regulatory method, they have introduced real-time energy markets, imbalance penalty fees, and limitations on the output of distributed energy resources (DERs), in addition to the existing day-ahead market. In response, this paper proposes an optimal scheduling method for VPP aggregators that adapts to the diversifying energy market and enhances the operational benefits of VPPs by using two Mixed-Integer Linear Programming (MILP) models. The validity of the proposed model and algorithm is verified through a case study analysis.

Heat radiation absorption and irreversibility of electromagnetic Williamson hybridized Al2O3–CoFe2O4/H2O nanofluid: A concentrated power generation

The quest for alternative and ecosystem-friendly energy sources to reduce reliance on fossil fuels and minimize cost has stimulated studies on various energy-generating techniques. Solar energy is one of the renewable and low-cost power generation sources for domestic and industrial usage. To optimize the performance of solar power, nanofluids can be considered as the based material. This study focuses on the concentrated power generation through hybridization of electromagnetic Williamson aluminium oxide (Al2O3) and cobalt ferrite (CoFe2O4) nanofluids in water (H2O) with nonlinear radiation. Without the deformation of the material, the fluid is driven by nonlinear thermal convection and pressure gradient in a bounded device. The developed, transformed boundary value mathematical model is solved by the Chebyshev scheme coupled with the collocation integrating method. A theoretical analysis is carried out for the dependent-parameter sensitivities on the flow rate, heat distribution and entropy generation to enhance and maintain continuous power generation. The investigation revealed that the hybridized nanofluid enhanced the concentrated thermal power for about 12.23 %, and the fluid velocity field is raised by 3.45 % with increasing dependent parameters. The Williamson material term optimized the thermodynamic equilibrium of entropy generation.

Financial Implications of Rooftop Solar Penetration in Sri Lanka from Utility Point of View

Solar is one of the fast growing and economical renewable power generation sources. The intention of development of solar power generation is to promote clean energy while addressing the basic requirement of uninterrupted and reliable electricity supply.The Sri Lankan government decided to adopt a policy that would use subsidies to encourage the growth of solar PV-based electricity. As a result, the market developed with a large number of customers eager to install solar rooftop systems, and they received strong assistance from solar power service providers. Hence, the utility is required to absorb the solar energy produced and this study focuses on the impact of solar penetration to the utility.System dispatch data for year 2019 and rooftop solar generation data for a period of 6 months of year 2019 were considered for the analysis in this paper. The model compares the financials of two scenarios to estimate the impact to the utility. The base case scenario, which is business as usual with the already installed solar generation in the system and No solar scenario which assumes that there is no solar in the system and therefore the demand is met by other available generation capacities in the system.This paper examines the two scenarios with careful calculations of how the system can still run without solar in the system. Daily demand patterns were also analysed and then expanded to months and summarized for the year. This research focuses the financial impact of solar electricity penetration to the national grid and how everyone can still be benefitted.

Estimation of Energy Storage Requirements in an Independent Power System from an Energy Perspective

Taiwan’s power system operates as an isolated grid, preventing the export of surplus energy. Excess electricity is either stored or discarded (curtailed). This study aims to estimate the energy storage requirement for the day with the most extreme electricity consumption behavior in a year without energy curtailment. Based on the installed capacity and actual power generation of renewable energy sources in 2022, this research estimates the power generation per GW of the installed capacity at full load. Integrating the government’s annual installation capacity plans, this study forecasts the annual power generation of renewable sources. Using the electrical load during the 2023 Spring Festival as a baseline and assuming an annual electricity consumption growth rate of 2%, combined with a minimum gas power output of approximately 6 GW, this study calculates the unused power generation, which represents the pumped-storage hydroelectricity and battery energy storage systems requirements for that day under a no-curtailment scenario. Considering the semi-annual adjustments in installation planning, this study’s code is open-sourced and designed to provide updated results with new planning data input, facilitating ongoing adjustments.

실시간 동적 송전용량 산정 시스템을 위한 가공 송전선 감시 시스템 개발

As NDC(Nationally Determined Contributions) was raised in the Paris Agreement in 2015, power generation using renewable energy that can minimize carbon use has become a more important task. However if a new renewable power generation source is unilaterally connected to the grid, the transmission capacity increases than the current capacity and due to the nature of new and renewable energy, the fluctuation range of power generation is larger which can increase system instability. Therefore efficient transmission line operation is required. Moreover it is difficult to actually implement the expansion of transmission capacity because social acceptance is not good. Therefore in order to solve this problem real-time dynamic transmission capacity (Forecasted Dynamic Line Rating) calculation technology that can maximize the use of transmission capacity in existing lines is required. This paper describes the development of a system that can monitor the surrounding environment of transmission towers and thermal rating data of overhead lines in real time to implement the calculation algorithm.

Optimal design of PI/PD dual mode controller based on quasi opposition based learning for power system frequency control

Due to uncertain load deviations in the unified power system, it becomes more challenging to uphold the system frequency at a constant desired value. The secondary controller has a significant role in damping the oscillations of frequency and interline power at a faster rate. Here a novel PI/PD dual mode controller (DMC) is suggested to regulate the frequency of unified system comprising renewable and conventional sources of power generation. The various sources of generation instigated in the dual-area system are hydro, diesel, thermal, PV, wind and battery energy storage system (BESS). The gains of the PI/PD DMC are suitably obtained by the novel application of quasi opposition based Sine Cosine Algorithm (QOBSCA) algorithm. The dynamic outcomes of the model are examined with the application of abrupt load deviation of 0.02 per unit in control area 2. During this deviated loading condition QOBSCA algorithm is utilized to achieve the appropriate gains of PID and PI/PD dual mode controller in view of a time based fitness function denoted as integral time absolute error (ITAE). Considering few response evaluative indices like overshoots, settling time and undershoots the ascendency of the projected PI/PD dual mode controller is recognized over popular PID controller during the abnormal loading condition. Additionally, the efficacy of the mentioned PI/PD dual mode controller is verified by applying arbitrary load pattern in control area 2. The robustness of the mentioned control schemes is also evidenced by confirming the stable response during system parameter deviations.

Forecasting China's hydropower generation capacity using a novel grey combination optimization model

Hydropower is the largest renewable energy power generation source with the largest construction scale and power generation capacity. A reasonable prediction of hydropower generation is conducive to achieving the carbon peak and neutrality goals. Hydropower generation is affected by seasons and precipitation, which have significant randomness and uncertainty. To realize a reasonable prediction of hydropower generation in China, this paper constructs a novel grey combination optimization model using different parameter combination optimizations based on the three-parameter discrete grey model TDGM(1,1). Further research shows that a better model performance can not necessarily achieved by a higher number of parameter combination optimizations, mainly due to the different effects and influence of various parameters on the model. Subsequently, the TDGM(1,1,r,ξ,Csz) model is applied to forecast China's hydropower generation. The results show that China's hydropower generation can reach 1687.738 hundred million kWh in 2025, an increase of 24.5% compared with 2020. Finally, the rationality of the prediction results is analyzed, and relevant countermeasures and suggestions are proposed.

Over current protection of distribution system with impact of solar and wind generation using DIgSIlent power factory

The utilization of renewable resources has been growing very fast worldwide recently to manage the increasing energy gap, but it also raises some challenges like protection issues, transient stability issues and security issues in the power system operation. Mainly, wind and solar photovoltaic renewable power generation sources are account for bulky renewable energy share. The transients in power systems including renewables are reduced and have recently attracted wide attention. The impact of renewables generation on power system transients should be effectively analyzed and evaluated to improve power system reliability, stability, operation and security. DIgSILENT Power Factory software is more powerful and useful for providing phasor of fundamental power frequency components better than other existing software’s; therefore, DIgSILENT Power Factory is proposed for modeling and analysis of the system.

Multi-objective risk-based optimal power system operation with renewable energy resources and battery energy storage system: A novel Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm approach

This research work proposes a Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm for the multi-objective optimal scheduling of hybrid power system taking into consideration the risk factor arising due to the intermittent/uncertain nature of the renewable power generation sources. The hybrid power system is modelled considering the thermal generation units, wind energy system, solar photo voltaic system, electric vehicle and battery energy storage system. The multi-objective optimization problem is proposed based on the simultaneous minimization of the total operating cost and system risk. The conditional value at risk is introduced as the risk index to analyse the system risk due to uncertainties in power deliveries by the renewable energy resources, electric vehicle and battery energy storage system during the scheduling process. The integral contribution of this research work focuses on the establishment an optimal generation schedule based on the combined optimization of the total operating cost and system risk. The simultaneous minimization of the operating cost and the risk index is performed with the multi-objective Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm and has been used to develop a Pareto-optimal front. The implementation of the fuzzy min–max technique is opted to fetch the best compromised solution. The standard test systems of IEEE-30 bus and Indian-75 bus system are used to validate the potency of the proposed approach. Comparative analysis has been established to highlight the results obtained with the proposed approach is appreciable than other optimization techniques.

Municipal power plan optimisation accounting for environmental footprints

This paper presents a model for municipal power planning, incorporating renewable power generation sources and energy storage facilities. It proposes a prototyping model architecture including photovoltaic panels, wind turbines and battery power storage, linked to the municipal power demands and interacting with the power grid. The planning model minimises the total system cost, comparing the pure monetary cost minimisation and accounting for the eco-cost related to the Greenhouse Gas and Water Footprints. The provided case study illustrates the economic feasibility of renewables integration, accounting for the eco-cost, maximising the use of photovoltaic panels. Adding the eco-cost minimises the footprints further and achieves a simultaneous reduction of total cost and footprints by approximately 25% and 39%. The sensitivity of the cost and footprints towards the life span of batteries was found marginal – compensated by increased power export to the grid, while the eventual life span reduction of wind turbines exerts a significant influence on the system performance in terms of cost and footprints – up to 40% compared to the base case. The sensitivity to the land availability for PV panels has also been found significant, obtaining cumulative reductions of the cost and footprints of 39% for cost and 50% from the benchmark case. The proposed method lays the foundation for a comprehensive energy planning method for municipalities to further include the social pillar of sustainability.

Estimating Urban Wind Speeds and Wind Power Potentials Based on Machine Learning with City Fast Fluid Dynamics Training Data

Wind power is known as a major renewable and eco-friendly power generation source. As a clean and cost-effective energy source, wind power utilization has grown rapidly worldwide. A roof-mounted wind turbine is a wind power system that lowers energy transmission costs and benefits from wind power potential in urban areas. However, predicting wind power potential is a complex problem because of unpredictable wind patterns, particularly in urban areas. In this study, by using computational fluid dynamics (CFD) and the concept of nondimensionality, with the help of machine learning techniques, we demonstrate a new method for predicting the wind power potential of a cluster of roof-mounted wind turbines over an actual urban area in Montreal, Canada. CFD simulations are achieved using city fast fluid dynamics (CityFFD), developed for urban microclimate simulations. The random forest model trains data generated by CityFFD for wind prediction. The accuracy of CityFFD is investigated by modeling an actual urban area and comparing the numerical data with measured data from a local weather station. The proposed technique is demonstrated by estimating the wind power potential in the downtown area with more than 250 buildings for a long-term period (2020–2049).

Applying analytic hierarchy process to industrial process design in a Nuclear Renewable Hybrid Energy System

Nuclear Renewable Hybrid Energy Systems (NRHESs) co-locate a nuclear power plant with a renewable power generation source, such as solar or wind, with an industrial process(es), and can include a battery for energy storage and a fossil fuel power plant. By co-locating these various components, the nuclear-generated power can be directed to the industrial process when demand is low or renewable generation is high, as well as toward meeting grid demand when needed. This paper will apply the risk assessment and management technique of the fuzzy Analytical Hierarchy Process (AHP) to an NRHES. The goal is to develop a method of evaluating industrial components in preliminary NRHES design. Criteria selected in this study within which each process is assessed are profitability, flexible operation, and safety. Based on this, the fuzzy AHP approach determined desalination to be the strongest candidate based on system definition and related assumptions. Additional studies can be undertaken using this approach for more in depth analysis of NRHES design. With no NRHESs currently deployed, fuzzy AHP can offer a useful decision-making tool.

Stability and optimal forcing analysis of a wind turbine wake: Comparison with POD

Understanding the dynamics and generation of coherent structures in wind-turbine wakes is crucial for efficiency improvement of wind farms, which will most probably represent one of the main renewable power generation sources in 2050. In this paper, we investigate the origin of such coherent structures by performing modal and non-modal stability analysis of the mean flow downstream of a wind-turbine rotor. The database consists of large-eddy-simulation results. Bi-local linear-stability and optimal-forcing analyses are performed at several wake's cross-sections. The most unstable perturbations are compared with the most energetic coherent structures recovered by the proper orthogonal decomposition (POD) analysis, showing a good agreement close to the rotor. Further downstream, these modes are overtaken by others with wavenumbers departing from those of the main POD modes. However, optimal-forcing analysis shows that asymptotically stable modes can be amplified by more than one order of magnitude via quasi-resonance mechanisms, bypassing the growth of the most unstable modes in the far wake. This suggests that the most energetic structures are originated by modal instabilities, which trigger quasi-resonance mechanisms in the far wake, determining the emergence of specific frequencies in the turbulent flow. These findings are crucial for designing efficient control systems to optimize wind farm performance.

State-Space Model of Quasi-Z-Source Inverter-PV Systems for Transient Dynamics Studies and Network Stability Assessment

Photovoltaic (PV) power systems are increasingly being used as renewable power generation sources. Quasi-Z-source inverters (qZSI) are a recent, high-potential technology that can be used to integrate PV power systems into AC networks. Simultaneously, concerns regarding the stability of PV power systems are increasing. Converters reduce the damping of grid-connected converter systems, leading to instability. Several studies have analyzed the stability and dynamics of qZSI, although the characterization of qZSI-PV system dynamics in order to study transient interactions and stability has not yet been properly completed. This paper contributes a small-signal, state-space-averaged model of qZSI-PV systems in order to study these issues. The model is also applied to investigate the stability of PV power systems by analyzing the influence of system parameters. Moreover, solutions to mitigate the instabilities are proposed and the stability is verified using PSCAD time domain simulations.

Management of renewable energy production and distribution planning using agent-based modelling

The use of renewable energy sources is increasing worldwide to achieve energy production sustainability. To reach this goal and mitigate against climate changes all energy resources must be optimally managed. In this paper, an innovative multi agent-based heuristic optimisation system is developed to address renewable energy systems’ challenges associated with the management of renewables including storage devices, planning of energy distribution, and consumption flexibility. A new heuristic algorithm is developed to introduce an efficient and sustainable storage strategy of surplus energy generated by different renewable resource options. This leads to archiving optimised storage levels of energy across different located storage devices in order to face risk of production shortages due to weather conditions. In order to test the behaviour of the developed system and address its benefits, a case study in the Republic of IRAQ is developed. Twelve major cities (in order of population size) distributed in 5 main regions including North, South, East, West and Middle parts of the country are selected, and hybrid renewable power generation sources such as Wind, PV-solar and Hydro are implemented based on weather changes. The results highlight the effect of high suppliers’ rate on electricity exchange and production planning from differently used renewable technologies. A sensitivity analysis is conducted to verify the behaviour of the developed model on different demand behaviour types.

Demand response model based on improved Pareto optimum considering seasonal electricity prices for Dongfushan Island

In this paper, an improved optimization model is proposed for demand response in a remote off-grid microgrid local on the Dongfushan Island, China to develop the energy dispatch and economic benefits considering different electricity price under different seasonal meteorological conditions. First, the seasonal electricity pricing model is built with the power generation of renewable sources in different seasonal meteorological conditions. Second, satisfaction is evaluated by the seasonal electricity price and the power consumption pattern. Improved Pareto optimum based on a distributed learning algorithm is proposed to maximize the satisfaction so that the electricity bills of consumers are reduced and the profits of the retailer is increased. The performance of the proposed optimization model is validated in the HOMER software and Matlab. Simulation results show that the electricity bills of consumers are lower by using the proposed method. For the retailer, the generation cost saves 1216$, and the utilization of renewable energy increased by 3.9% in January 2011.

Aeroderivative gas turbine back-up capability with compressed air injection

The transition to more renewable energy sources of power generation is associated with grid instability and the need for backup power, due to their intermittency. This provides an opportunity for gas turbine engines, especially the aeroderivative (AD) types that generally have higher ramp rates than heavy-duty engines. Nonetheless, higher ramp rates are still necessary to meet more stringent grid requirements, with increased renewables subscription. The study examines ramp rate improvements and performance enhancement through compressed air injection at the back of the high-pressure compressor (HPC). Two configurations of AD engines are considered in the investigation. In-house gas turbine performance simulation software has been used to simulate the steady-state and transient operations for design and off-design performance. Compressed air injection in the study is facilitated by an assumed compressed air storage or an external compressor. The steady-state analysis for power augmentation shows that for the two-spool engine with fixed speed low-pressure compressor (LPC), a 16% increase in power is obtained with 8% of flow injection. The other engine that is intercooled and consists of a variable speed LPC with power turbine shows a 21% increase in power for the same injection amount. Above 8% injection, the HPC of both engines tends towards an adverse rise in pressure ratio. However, up to 15% of flow injection is allowed before the surge point. It is seen generally that the operating point of the LPC moves away from surge, while the opposite is the case for the HPC. For transient simulations focused on ramp rates, the better improvements are shown for the intercooled engine that runs at variable speed. This is a ramp rate improvement of 100% with air injection, while that of the other engine increases by 85%.

Effect of Taiwan's energy policy on unit commitment in 2025

This study presents the effect of Taiwan's energy policy on its unit commitment in the year 2025. Taiwan's energy policy has been changing since 2005, since when it has required the decommissioning of nuclear power plants in response to the crisis in Fukushima, the adoption of the Kyoto Protocol, and the increase of renewable sources of power generation to 20% of its energy mix. The paper models the unit commitment for the Taiwan power system under Taiwan's energy policy. The study compares unit commitments in 2018, summer 2025, and winter 2025 using AMPL Software with the CPLEX 12.9.0 Solver. The results show that the net demand curve will change by 2025, forming a “duck – curve” model due to the high penetration of renewable energy. The daily carbon dioxide emission in 2018 for five percent (5%) spinning reserve is 392,151,000 kg. The said amount is larger than in winter 2025 when it will be 311,314,000 kg indicating the importance of the full implementation of renewable energy projects in helping to reach Taiwan's goal to limit carbon dioxide emission by 2025. Pumped storage scheduling changes from conventional pumping in the early morning to pumping from around noon to late afternoon due to the high penetration of renewable energy. The construction of combined-cycle units will have a critical role in the achieving of the spinning reserve capacity goal set by the Taiwan government, to avoid the importation of energy and to maintain the energy security of the country.

Steady-State Power Quality Synthetic Evaluation Based on the Triangular Fuzzy BW Method and Interval VIKOR Method

With the increasing consumption of fossil fuels, renewable sources power generation has attracted more and more attention. However, with the integration of renewable energy and a large number of non-linear loads in power systems, several power quality problems are attracting the attention of researchers. At present, only national standards for an individual power quality index have been set in China. When evaluating power quality in practice, the individual standard cannot reflect a comprehensive level of power quality. In this paper, a synthetic evaluation method for steady-state power quality is proposed. Firstly, the traditional BW (best-worst) method is improved based on the triangular fuzzy number, to obtain the interval weight of each evaluation index. Then, the interval VIKOR method is used to evaluate the steady-state power quality monitoring data, and the final evaluation results are obtained. The validity of the proposed method is verified by the experimental data from the dynamic simulation laboratory of Tianjin University.

Step Up Dc Dc Converter for Pv Cell

Paper In modern trend renewable energy sources has taken vast place in power generation. out of all renewable energy sources solar power generation source has high efficiency . in our paper we have using a converter which step up the power generation through photo voltaic cell. Coupled inductor topology not step up input but also provides uninterrupted power supply to load. This type of system is used in island applications. Voltage developed by photo voltaic cell is 15v which is step up to 220V using our proposed converter with power of 100W, this converter provide output which is fifteen times of input. This converter is transformer less and has negligence losses therefore it is DC-DC converter used for DC applications and for Ac applications by connecting an inverter to it.