Renewable Power Plants Research Articles

Forecasting the power generation at renewable power plants in Sri Lanka using regression trees

This paper presents the application of regression trees as a versatile alternative to other machine learning and statistical modelling techniques to forecast the power generation at five renewable power plants: one large hydropower plant, two mini hydropower plants, and two wind farms in Sri Lanka. The prediction models for each power station were developed by varying the depth of the regression tree. The regression tree model with the lowest depth that forecasts the output (power) in terms of all the predictor variables was selected for each power station and the accuracy of the models was evaluated by means of the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2). According to the degree of the above performance indicators, i.e. very low values of MAE, MAPE, and RMSE supplemented by R2 of 0.95 or more, the regression tree method proved to be a convenient forecasting technique to predict the power generation at both hydro and wind power plants. Further, it could be found that a good correlation between the input and output variables paves the way for a smaller depth in the regression tree. Moreover, regression trees presented here could accurately identify the relationship between the power generated and the most influential weather factors, without being affected by potential outliers or missing values while managing collinearity too. Extension of this study would enable to generalize the prediction of renewable power generation based on the regression tree method, leading towards minimizing the use of fossil fuel.

HyDesign: a tool for sizing optimization of grid-connected hybrid power plants including wind, solar photovoltaic, and lithium-ion batteries

Abstract. Hybrid renewable power plants consisting of collocated wind, solar photovoltaic (PV), and lithium-ion battery storage connected behind a single grid connection can provide additional value to the owners and society in comparison to individual technology plants, such as those that are only wind or only PV. The hybrid power plants considered in this article are connected to the grid and share electrical infrastructure costs across different generation and storing technologies. In this article, we propose a methodology for sizing hybrid power plants as a nested-optimization problem: with an outer sizing optimization and an internal operation optimization. The outer sizing optimization maximizes the net present values over capital expenditures and compares it with standard designs that minimize the levelized cost of energy. The sizing problem formulation includes turbine selection (in terms of rated power, specific power, and hub height), a wind plant wake loss surrogate, simplified wind and PV degradation models, battery degradation, and operation optimization of an internal energy management system. The problem of outer sizing optimization is solved using a new parallel “efficient global optimization” algorithm. This new algorithm is a surrogate-based optimization method that ensures a minimal number of model evaluations but ensures a global scope in the optimization. The methodology presented in this article is available in an open-source tool called HyDesign. The hybrid sizing algorithm is applied for a peak power plant use case at different locations in India where renewable energy auctions impose a monetary penalty when energy is not supplied at peak hours. We compare the hybrid power plant sizing results when using two different objective functions: the levelized cost of energy (LCoE) or the relative net present value with respect to the total capital expenditure costs (NPV/CH). Battery storage is installed only on NPV/CH-based designs, while the hybrid design, including wind, solar, and battery, only occurs on the site with good wind resources. Wind turbine selection on this site prioritizes cheaper turbines with a lower hub height and lower rated power. The number of batteries replaced changes at the different sites, ranging between two or three units over the lifetime. A significant oversizing of the generation in comparison to the grid connection occurs on all NPV/CH-based designs. As expected LCoE-based designs are a single technology with no batteries.

Modeling the impact of extreme summer drought on conventional and renewable generation capacity: Methods and a case study on the Eastern U.S. power system

Across recent years, there has been a growing prevalence of extreme weather events throughout the United States, posing significant challenges to the reliable and resilient operation of power systems. Specifically, summer droughts threaten to severely reduce available generation capacity to meet regional electricity demand, potentially leading to power outages. This underscores the importance of accurate resource adequacy (RA) assessment to ensure the reliable operation of the nation’s energy infrastructure. Accurately evaluating the usable capacity of regional generation fleets is a challenging undertaking due to the intricate interactions between power systems and hydro-climatic systems. This paper proposes a systematic and analytical framework to evaluate the impacts of extreme summer drought events on the available capacity of various generating technologies, incorporating both meteorological and hydrologic factors. The framework provides detailed plant-level capacity derating models for hydroelectric, thermoelectric, and renewable power plants, facilitating evaluations with high temporal and spatial resolution. The application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system within the PJM and SERC regions of the United States yields insightful results. By analyzing the daily usable capacity of 6,055 at-risk generators across the study region, it shows that the summer capacity deration is most significant for hydroelectric and once-through thermal power plants, followed by recirculating thermal power plants and combustion turbines. In the event of the recurrence of the 2007 southeastern summer drought event in the near future, the generation fleet could experience a substantial reduction in available capacity, estimated at approximately 8.5 GW, compared to typical summer conditions. The sensitivity analysis reveals that the usable capacity of the generation fleet would suffer an even more significant decrease under conditions of increasingly severe summer droughts. The proposed approach and the findings of this study provide valuable methodologies and insights, empowering stakeholders to bolster the resilience of power systems against the potentially devastating effects of future extreme drought events.

Resilient and sustainable energy supply chains: insights on sourcing and pricing strategies in a non-collaborative and collaborative environment

Energy supply chain resilience and sustainability are essential for dependable, ecologically responsible energy availability, reducing disruptions, and creating a sustainable future. In this regard, this study explores the resilience and sustainability of a single energy supply chain through two distinct scenarios. The focus is on a manufacturer capable of sourcing products from renewable and non-renewable power plants. Although the renewable power plant is the preferred clean option, disruptions may occur. On the other hand, the fossil fuel power plant offers reliability at the cost of environmental impact. The manufacturer employs two sourcing strategies based on disruption probabilities: single sourcing from just the renewable power plant and dual sourcing from fossil and renewable power plants. In the second scenario, a collaboration between the power plants is introduced. If a disruption occurs at the renewable power plant, it orders the fossil fuel power plant to generate electricity on its behalf. Using a game-theoretic approach, the research examines the effects of dual-sourcing techniques, and the efficacy of cooperation in minimising disruptions. It also examines the trade-offs between energy supply chain resilience and environmental sustainability. The results shed light on sourcing strategy decision-making and add to the literature on sustainable and resilient energy supply chains.

Research on Multi-Objective Energy Management of Renewable Energy Power Plant with Electrolytic Hydrogen Production

This study focuses on a renewable energy power plant equipped with electrolytic hydrogen production system, aiming to optimize energy management to smooth renewable energy generation fluctuations, participate in peak shaving auxiliary services, and increase the absorption space for renewable energy. A multi-objective energy management model and corresponding algorithms were developed, incorporating considerations of cost, pricing, and the operational constraints of a renewable energy generating unit and electrolytic hydrogen production system. By introducing uncertain programming, the uncertainty issues associated with renewable energy output were successfully addressed and an improved particle swarm optimization algorithm was employed for solving. A simulation system established on the Matlab platform verified the effectiveness of the model and algorithms, demonstrating that this approach can effectively meet the demands of the electricity market while enhancing the utilization rate of renewable energies.

Bi-level configuration and operation collaborative optimization of shared hydrogen energy storage system for a wind farm cluster

Energy storage is indispensable to achieve dispatchable and reliable power generation through renewable sources. As a kind of long-duration energy storage, hydrogen energy storage systems are expected to play a key role in supporting the net zero energy transition. However, the high cost has become an obstacle to hydrogen energy storage systems. The shared hydrogen energy storage (SHES) for multiple renewable energy power plants is an emerging mode to mitigate costs. This study presents a bi-level configuration and operation collaborative optimization model of a SHES, which applies to a wind farm cluster. Different operation modes, including the ‘electricity‑hydrogen-electricity (E-H-E)’ mode and the ‘electricity‑hydrogen (E-H)’ mode, are considered. Results show that SHES system offers substantial advantages over individual hydrogen energy storage solutions. It achieves a notable reduction in annual operation and maintenance (O&M) costs by 9.5 % and a significant increase in annual profit by 10.49 %. Moreover, the SHES system demonstrates a lower wind curtailment rate of 0.24 %, in contrast to the 0.37 % observed in individual cases. SHES will play a more significant role in larger wind farm clusters. The aforementioned advantages of SHES systems become even more pronounced in larger wind farm clusters, where their impact on cost reduction and efficiency is significantly amplified.

Performance evaluation and operation optimization of a combined heat and power plant integrated with molten salt heat storage system

Combined heat and power is an effective way to enhance the energy efficiency of thermal power plants. The solar and wind power are intermittent, and dispatchable power sources are critical to balance the supply and demand sides. To facilitate the high penetration of renewable energy, combined heat and power (CHP) plants should provide more and more peak shaving services for the power grid. The thermal energy can be stored in and released from the molten salt heat storage system (MSHSS). The integration of MSHSS can enlarge the adjustable range of the power load of CHP plants, so it is a potential way to enhance the operational flexibility of CHP plants supplying industrial steam and power. In this study, the MSHSS integrated within CHP plants are proposed, which store a part of sensible heat energy carried by extraction steam before supplying to industrial users. The heat storage capacity dominates the flexibility enhancement, and indicators to evaluate energy performances of the proposed system are defined. Moreover, models for the operation optimization of CHP plants integrated with the MSHSS are developed, and a case study of a 350 MW CHP unit is conducted. When the reheat steam is extracted to supply to industrial steam, the maximum and minimum adjustable power loads can be increased and decreased by 17.13 and 12.01 MW, respectively with the integration of the MSHSS. Moreover, during the peak, flat, valley time of reference day, the heat supply coal consumption rates are 73.94, 74.19 and 81.03 kg t−1,respectively. With the operation scheduling of CHP plant integrated with MSHSS, the MSHSS with reheat steam as heat source show the lowest coal consumption rate as 1553.79 ton/day.

Minimize Total Cost and Maximize Total Profit for Power Systems with Pumped Storage Hydro and Renewable Power Plants Using Improved Self-Organizing Migration Algorithm

This study presents the application of an improved self-organizing migration algorithm (ISOMA) for minimizing the total electricity production expenditure (TEPE) and maximizing the total electricity sale profit (TPRF) for hydrothermal power systems (HTPS) without and with renewable energies. Two power system configurations were employed to test the real efficiency of ISOMA while dealing with two objective functions. In the first configuration, there was one thermal power plant and one hydropower plant, while in the second configuration, wind and solar energy were both connected to the first system. The results achieved in the first configuration with the first objective function indicated that ISOMA not only outperformed SOMA according to all comparison criteria but was also superior to other methods such as evolutionary programming (EP), acceleration factor-based particle swarm optimization (AFPSO), and accelerated particle swarm optimization (APSO). The evaluation of the results achieved by ISOMA in the second configuration with the objective function of maximizing the TPRF revealed that ISOMA could reach better profits than SOMA in terms of maximum, mean and minimum TPRF values over fifty trial runs. As a result, it was concluded that pumped storage hydropower plants are very useful in integrating with renewable power plants to cut total cost for thermal power plants and in reaching the highest profit for the whole system. Also, ISOMA is a suitable algorithm for the considered problem.

Pump-stopping-induced hydraulic oscillations in long-distance district heating system: Modelling and a comprehensive analysis of critical factors

To achieve low-carbon goals, long-distance district heating (LDH) systems are widely applied, which utilize the waste heats from renewable energy power plants and waste incineration plants. The hydraulic oscillations induced by pump-stopping in LDH systems may give rise to critical incidents, such as pipeline ruptures and heating disruptions. The systematic analysis of hydraulic oscillations induced by pump-stopping and the subsequent revelation of critical factors that mitigate their intensity are fundamental prerequisites for ensuring the safety and reliability of LDH system. In this paper, firstly, a pump-stopping hydraulic transient model of the LDH system is established based on the distributed parameter method. Then, combining the method of characteristics (MOC) and the Newton iteration method, the numerical solution scheme for this model is proposed. Finally, taking a 20 km LDH system as an example, the characteristics of hydraulic oscillations induced by pump-stopping is analyzed, and the key factors influencing the intensity of these hydraulic oscillations are investigated. In addition, the differences between hydraulic oscillations induced by pump-stopping and those induced by sudden faults on pipelines are also analyzed. The results indicate that an optimal pump inertia moment value and a properly sized bypass pipe at the pump significantly mitigate hydraulic oscillations, while the influence of constant pressure at the heat source inlet on hydraulic oscillations can be negligible. Our study has important reference value for improving the safe and reliable operation of LDH systems.

Kajian Pemanfaatan Debit pada Saluran Irigasi untuk Pembangkit Listrik Tenaga Mikrohidro (Studi Kasus Saluran Induk Sawito Daerah Irigasi Sadang) Kabupaten Pinrang, Propinsi Sulawesi Selatan

Utilization of water as a new power plant reaches about 6.9% of the existing potential and most of it is built in rivers with very large costs and quite a long time. The demand for electricity is increasing more and more, but the government cannot add power plants from fossil fuels. In fact, they will gradually close all of them and replace them with new renewable energy plants, such as hydropower, solar power, PLTB and other renewable energy power plants. In general, hydroelectric power plants (PLTA) are built on river bodies in the form of weirs or weirs so that they require considerable cost, time and risk and are greatly influenced by fluctuations in discharge, sediment and so on. While there are many irrigation areas that have operated with discharge capacity and fall height that can be utilized for small-scale hydropower, one of them is the Sadang Irrigation Area with an area of ±64,000ha. Using topographic mapping methods, measuring surveys of existing channels, and analyzing the availability of discharge and irrigation water requirements, the research results showed that the height of falls in several irrigation buildings was obtained. By analyzing water availability and demand (water balance), a water supply plan for each existing irrigation canal is obtained. so that the potential power and electrical energy produced is obtained. The Sadang irrigation area is supplied with water from the Benteng weir through two intake buildings. The right intake building supplies the Pekkabata main channel, while the Sawito and Rappang Ha main channels are supplied from the left intake building. The Pekkabata sub-network has a potential power of 338 kW, the Rappang sub-network has a potential power of 158 kW and the Sawito sub-network has a power of 1,939 kW. So the total potential electrical power in the Sadang irrigation network is 2,436 Kw. The potential that has been developed in the Sadang irrigation area for electricity generation is 1,235 kW so there is still a potential of 1,200 kW. Especially in the Sawito main channel, in the building for tapping BSa. 9 has an electrical potential with a power of 492 kW, and annual energy production of 3,511,880 kWh, and the suitable turbine type is the kaplan type.

INVESTIGATING THE EFFECTIVITY OF PROMOTING ELECTRIC VEHICLE TO REDUCE AIR POLLUTION: AN ANALYSIS OF INDONESIA POWER PLANTS

Purpose: Fossil fuels such as coal, oil, and natural gas, release carbon dioxide and other greenhouse gases when burned for energy production. Transitioning from fossil fuels to renewable energy sources is essential for addressing climate change, ensuring energy security, protecting the environment, and fostering sustainable economic development. The combination of environmental benefits, technological advancements, and supportive policies makes electric vehicles (EV) a promising and sustainable solution for the future of transportation. In Indonesia, the use of EV is still in its earliest stage, and the popularity of EV in Indonesia is yet very low due to its price and charging problems. The government has introduced some incentives to promote EV to the people, however, the results still yet to come. This paper investigated the effectiveness of EV transition to reduce pollution based on existing power plants in Indonesia. Methodology: We computed the proportion of renewable power plants from all power plants and used the data of CO2 emissions per kWh of various power plants to analyze the CO2 emission of conventional and EVs using the data of CO2 emissions from fossils. Findings: We found that 86.8 percent of the electricity used in Indonesia is generated from fossil fuels. However, based on the power consumption rate per km of travel, we found that EVs emit 46 to 49 percent lower CO2 than fossil-powered vehicles for the same travel distance. Originality/value: Despite the domination of fossil-fueled power plants, promoting EVs can help to reduce greenhouse gas emissions in Indonesia.

A current injection method for converter power loss representation in electromagnetic transient simulation

The power-electronics converters are increasingly applied in modern power system, which contains various sub-systems, such as renewable energy power plants and high voltage direct current transmission systems, etc. The electromagnetic transient simulation has been one of the important tools for the study of complex system containing large numbers of power converters. The coordination control of the power converters and corresponding power generation and load units is one of the challenges ensuring stability and obtaining optimal efficiency. However, the detailed modeling of the power converters considering the conduction and switching losses can cost a great number of computation resources, which makes it difficult to simulate a large system. This work proposed a current injection method for converter power loss representation, which can accurately consider the conduction and switching losses of the power-electronics switches with low computation complexity. A look-up table (LUT) of the power loss ratio is calculated before the simulation, and a current source representing the power loss is connected in parallel with the converter to track the reference loss generated by the look-up table. The proposed method can be combined with multiple converter modeling schemes, such as the two-resistance model, the switching-function model, and the average value model, etc. The topologies of the two-level converter and the modular multi-level converter (MMC) were used in the case studies to demonstrate the effectiveness of the proposed method. Compared to the device-level model, the simulation power loss deviation is 0.64% and 4.13% applying the current injection method model in the case studies of the two-level converter and the MMC.

Turkey’s Municipal Solid Waste and Urban Waste Water Treatment Sludge Electrical Energy Potential

The role of indigenous and renewable energy resources is crucial for countries that want to be less dependent on foreign countries. Renewable energy resource power plants have many advantages when compared with other resources. They make countries less energy dependent and they also have environmentally friendly technologies when compared with other resources. Turkey is a country that has an abundance of renewable energy resources and biomass is one of the most promising energy resources in renewables. The goal of this paper is to determine Turkey’s electricity potential based on industrial wastes i.e. municipal solid waste (MSW) and urban wastewater treatment sludge potential, using applicable electricity generation methods. In this attempt, electrical energy potential of these resources has been found by using applicable waste to energy methods, technical and economical parameters. Electrical energy potential of Biogas (Landfill gas included) based on these resources is an amount of 6.73 billion kWh/year. Municipal solid waste (MSW) and dried municipal wastewater treatment sludge electrical energy potential is an amount of 23.81 billion kWh/year. Based on the resources stated; using the biogas and biomass energy values, calculations resulting in primary energy potentials; the installed capacity for gas-motor systems, gas turbine systems (Simple cycle), gas & steam turbine systems (combined) and boiler – steam turbine systems have been determined.

Capacity planning for fossil fuel and renewable energy resources power plants

Electricity industry is changing by organizational and legislative evolutions dealing with economical and environmental affairs. Besides rapid development in energy generation decreased the related variable costs, considering environmental issues. In this study energy generation technologies are evaluated both from economical and environmental views. Applying Analytical Hierarchy Process (AHP) which is extremely applicable and known as one of the efficient methods to analyze complicated multi criteria problems it is achieved interesting results from evaluation. The main goal of this evaluation which is presented in this essay is to extract total preference weights for generation technology. Total preference weights are determined considering four criteria in fossil fuel power plants and renewable energy resources power plant using this evaluation pattern. For three power plant of gas, steam and combine cycle the total preference weights are calculated by considering seven criteria. For the next eight power plants of wind, small hydro, photovoltaic, solar thermal, micro turbine, biomass, geothermal and fuel cell total preference weights is considered according to seven criteria. Optimum capacity is calculated and determined. Sensitivity analysis is made on two most important criteria of this model named 1- operation and investment cost, and 2-fossil resources scarcity for power plants. 1- Multiple criteria of objective function, 2-possibility of sensitivity analysis on criteria weights and 3-concurrent inspection of qualitative and quantitative criteria in optimization of objective function are three advantages of the proposed model comparing common applying method such as (mono- objective software WASP)

Identification of surface defects on solar PV panels and wind turbine blades using attention based deep learning model

The global generation of renewable energy has rapidly increased, primarily due to the installation of large-scale renewable energy power plants. However, monitoring renewable energy assets in these large plants remains challenging due to environmental factors that could result in reduced power generation, malfunctioning, and degradation of asset life. Therefore, the detection of surface defects on renewable energy assets is crucial for maintaining the performance and efficiency of these plants. This paper proposes an innovative detection framework to achieve an economical surface monitoring system for renewable energy assets. High-resolution images of the assets are captured regularly and inspected to identify surface or structural damages on solar panels and wind turbine blades. Vision transformer (ViT), one of the latest attention-based deep learning (DL) models in computer vision, is proposed in this work to classify surface defects. The ViT model outperforms other DL models, including MobileNet, VGG16, Xception, EfficientNetB7, and ResNet50, achieving high accuracy scores above 97% for both wind and solar plant assets. From the results, our proposed model demonstrates its potential for monitoring and detecting damages in renewable energy assets for efficient and reliable operation of renewable power plants.

Off-grid vs. grid-based: Techno-economic assessment of a power-to-liquid plant combining solid-oxide electrolysis and Fischer-Tropsch synthesis

The economic performance of Power-to-Liquid processes depends substantially on the power source’s features, i.e., electricity costs and full load hours. Off-grid solutions can ensure cheap, green electricity without being exposed to fluctuating electricity markets. A techno-economic assessment of a Power-to-Liquid plant combining solid-oxide electrolysis and Fischer-Tropsch synthesis has been conducted. Off-grid and grid-based scenarios of three process configurations at plant scales from 1 to 1000 MWel. rated electrolyzer power were evaluated. Net production costs of Fischer-Tropsch products ranging from 2.42 to 4.56 €2022/kg were obtained for the grid-based scenarios. In contrast, values of 1.28 to 2.40 €2022/kg were determined for the evaluated off-grid scenarios. Scaling up the plant showed a weakened decrease in net production costs after surpassing a threshold of 100 MWel. due to substantial relative electricity costs of up to 88 %. Thus, future Power-to-Liquid projects should be designed at a scale of 100MWel. rated electrolyzer power. In addition, an availability exceeding 4000 h/a is recommended for off-grid plants, e.g., by implementing hybrid renewable power plants as well as electricity and syngas storage technologies.

Transient Stability of Synchronous Condenser Co-located with Renewable Power Plants under High-Resistance Faults and Risk Mitigation

Transient Stability of Synchronous Condenser Co-located with Renewable Power Plants under High-Resistance Faults and Risk Mitigation

Transient Stability of Synchronous Condenser Co-Located With Renewable Power Plants

Transient Stability of Synchronous Condenser Co-Located With Renewable Power Plants

Prototype Design and Construction of 450 VA Permanent Magnetic Sync Generator for Vertical Axis Wind Power Generator House Scale with Low Wind Speed

Permanent magnet synchronous generator (PMSG) is a mechanical electrical machine that can convert motion energy into electrical energy which has two main parts, namely a rotor using permanent magnets and a stator without an iron core which the machine uses for low and low rotations. load torque used as a generator. The driving force for Wind Power Plants (PLTB) which we know is alternative and renewable power plants, the energy source is wind which continues to exist and is easily available, another reason for the need to develop permanent magnet generators for PLTB is the reduction of petroleum, other fossil energy, subsidies continue to cut materials fuel oil for power generation which increases the price of electricity per KWH as well as a global commitment to reduce greenhouse gas emissions. the need for electricity for the community and the high price of electricity as well as the difficulty of installing electricity with a capacity of 450 VA, this study aims to make a prototype of a laboratory-scale permanent magnet generator for wind power plants for residential homes that allows it to be placed close to the ground, no need to be directed towards the wind. blowing, can generate electricity with wind speed from 2.5 m/s, easy operation and low cost. The research method in this study is to conduct a literature study, conduct a wind source mapping survey from several sources to obtain wind speed data nationally, design a prototype and design a permanent magnet generator prototype and finally simulation with calculation capacity of the prototype. The design of the permanent magnet generator prototype for the Wind Power Plant has 12 poles with a rotor speed of 500 rpm (revolution per minute) with capacity of 450 VA.

MODELI PREVENCIJE KRIMINALA U PROJEKTIMA OBNOVLJIVIH IZVORA ENERGIJE

The aim of this paper is to identify models for preventive risk recognition as tool for crine prevention in the renewable energy sector through a systematic review of the available literature. These models should represent a formalized set of steps or protocols that can be used at any stage of renewable energy projects, particularly in the construction of renewable energy power plants, to mitigate and minimize the risk of criminal offenses. Models of preventive risk recognition ensure that the power plant construction project progresses smoothly from start to finish without unnecessary financial losses due to their ability to identify all weak points in the entire process. With the help of the models described in this paper, the transition to green energy is accelerated and sustainable development is fully supported,