Thermal Generation Research Articles

Experimental analysis of a pilot plant in Organic Rankine Cycle configuration with regenerator and thermal energy storage (TES-RORC)

Applying thermal energy storage in a Regenerative Organic Rankine Cycle system is a possible combination of energy storage for renewable and waste energy sources. This research presents an experimental analysis of a regenerative organic Rankine cycle pilot plant, incorporating a thermal energy storage chamber based on a phase change materials to obtain thermal inertia in the system. The primary heat source is the residual heat of combustion gases at a temperature of approximately 320 °C coming from a generating set that operates within a thermal power generation plant. The prototype uses a twin-screw expander modified to work as a turbine. Novec 649 was used as the working fluid which is an organic compound. The experimental evaluation of the regenerative organic Rankine cycle with thermal energy storage has been carried out through energy and exergetic efficiency analysis to verify the operation of the pilot plant. Values of 13.2 % were obtained for the exergetic efficiency and 2.67 % for the global efficiency of the cycle, with a maximum expander work of 11.8 kW. It was demonstrated that the thermal energy storage system provides energy in response to the plant's operating inertia for 17.5 min, sufficient time for the gradual shutdown of the plant equipment, maintaining the working power of the regenerative organic Rankine cycle.

Preparation and erosion resistance of SiC-based ceramic-PCM sensible-latent heat composite heat storage materials

Alloy phase change materials (PCM) are key materials in latent heat storage systems for solar thermal power generation, however, their application is limited by the high corrosion and oxidation problems of alloys. To reduce the erosion problem of alloy PCM, SiC-based ceramic encapsulated flake graphite (EG) modified Al-Cu28-Si6 alloy was used to successfully prepare composite PCM with high compatibility and oxidation resistance. Adopting static erosion test method, the composite PCM was subjected to 50 thermal cycles (25–1000 °C holding time of 10 h was recorded as 1 cycle). The results showed that SiC-based ceramics have excellent corrosion resistance to composite PCM with 10 wt% EG added, which due to the excellent reticulation and encapsulation of EG. Based on the Young-Laplace equation and the contact angle, the erosion resistance mechanism of silicon carbide ceramic alloys has been revealed. SiC-based ceramic-PCM sensible heat-latent heat composite heat storage material was expected to be used for high-temperature heat storage in solar thermal power generation.

Thermal energy storage, heat transfer, and thermodynamic behaviors of nano phase change material in a concentric double tube unit with triple tree fins

The contribution of this study is the proposal of a synergistic composite enhancement strategy involving tree fins and nanomaterials to improve the low thermal performance of phase change material (lauric acid) in a typical double tube latent heat thermal energy storage unit. The effects of nanoparticle concentrations and tree fin branching angles on the fluid dynamics, melting time, heat transfer, energy storage, and entropy generation characteristics were investigated. By employing tree fins, the melting time was respectively reduced by up to 60.20% and 36.05% compared to the finless case and the rectangular fins, while the energy storage power was respectively boosted by up to 143.36% and 37.45%. The increase of nanoparticle concentration was beneficial for melting heat transfer, and the dendritic fins with a bottom angle of 90° and a top angle of 30° yielded the highest energy storage performance. The total entropy generation of the melting process was mainly governed by the thermal entropy generation, and it showed a decreasing trend with the increase of time. In contrast to the finless configuration, the incorporation of fins diminished the integrated entropy generation value. Similarly, an elevation in nanoparticle concentration also led to a reduction of the integrated entropy generation value.

Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation

AbstractThe economic‐environmental power dispatch (EEPD) problem, a widely studied bi‐objective non‐linear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This article addresses these concerns by formulating and solving the economic environmental and stable power dispatch (EESPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modelled using random variable generation techniques, applying Gaussian, Weibull, and log‐normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic EESPD problem extends to multiple periods by replicating the single‐period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi‐objective evolutionary algorithms (MOEAs) have gained prominence for solving complex non‐linear problems involving multi‐objective functions. This article applies the latest MOEAs to tackle the proposed EESPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp‐up and ramp‐down constraints for thermal generators. A bidirectional coevolutionary‐based multi‐objective evolutionary algorithm is employed, integrating an advanced constraint‐handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade‐off between various conflicting objective functions compared to other state‐of‐the‐art MOEAs.

Featuring the stagnation point flow of Casson nanofluid with thermal radiation and swimming microorganisms over a stretching sheet: A computational modeling via analytical method

Here, the investigation examines the flow of a nanomaterial toward stagnation point past stretching sheet having microorganism. The thermal radiative and heat generation have been accounted as well as convective boundary conditions. The influence of thermophoretic and Brownian movment has been considered. It has been proposed that the suitable transformation be utilized in order to convert the system of PDEs into ODEs. The analytical solutions are created through the Homotopy Analysis Method (HAM). The physical parameters with modelled equations have been graphically explained with related physical results. The numeric magnitude of drag friction, heat, mass transport and motile microorganism dendity were also computed and described for numerous emerging parameters. The result shows that the flow decreases with the rinsing value of Casson parameter. The impact of enhancing fluid parameters is too depressed the momentum and thermal boundary layer. The augment in the radiation boost the thermal layer whereas an enlargement in the input of the Lb tends to depreciation the motile density. Validation of the work is existed by validating the current outcomes with previous publishing results. The existing and potential applications of bioconvection have generated a new fascination in the theoretical investigation of the synthesis and process control in biofuel cells and bioreactors.

Integrated machine learning models for predictive analysis of thermal and electrical power generation of a photo-thermal system at Catania, Italy

The Photovoltaic-Thermal (PV/T) system is designed for producing both electrical and thermal energy. Its efficiency is improved when temperature-sensitive PV cells are cooled using nanofluid coolants. However, the current models for predicting PV/T system performance with nanofluid cooling are limited. In order to fill this gap, this study aims to develop machine learning models to predict the electrical and thermal efficiencies of a water-based PV/T system. Three types of machine learning algorithms from the supervised learning method were selected in this study because of their ability to handle complex data and provide accurate predictions: the Multi-layer Perceptron (MLP), the Gradient Boosting Regressor (GBR) and the Light Gradient-Boosting Machine (LightGBM). Initially, the models are trained and validated using data consisting of 15,540 samples from a water-based PV/T system. On the basis of the results obtained, the MLP algorithm proved to be the best for the prediction of electrical energy, with an R2 value = 0.9906, against 0.9709 and 0.99 respectively for the other GBR and LGB algorithms, while the LGB algorithm proved effective for the prediction of thermal energy, with an R2 value = 0.983, against 0.983, 0.97 and 0.988 respectively for the GBR and LGB algorithms. Secondly, the best MLP and LGB models previously identified for the water-based PV/T system were adapted to predict, this time, the energy performance of an Ag/water nanofluid-based PV/T system. The results obtained show that these models are highly effective in predicting the electrical and thermal performance of the Ag/water nanofluid PV/T system, even in the absence of real data, making them very useful for real applications.

Thermal magnetization transport analysis featuring darcian and multi-physical rheological characteristics in chemically reactive dual convective tangent-hyperbolic nanomaterial confined by vertical cone

The Soret-Dufour characteristics elaborate the phenomena of cross-diffusion where solutal gradients yield heat flux (Dufour aspect) and thermal gradients engender mass flux (Soret effect). Such consideration finds significance in multi-component fluid systems, influencing both heat-mass transportation processes. This study evaluates the porous medium characteristics in convectively heated tangent-hyperbolic nanomaterial driven by a magnetized convected cone. The analysis features Buongiorno nanomaterial model which accounts Brownian motion together with thermophoresis. Thermal transport expression which reports heat transfer characteristics is modeled by considering thermal radiation, convective thermal conditions and heat generation while mass transfer considers the chemical reaction effects. Dimensional expressions are converted into dimensionless forms deploying similarity transformations ensuing in a set of ODEs (ordinary differential expressions) which are computed by deploying bvp4c algorithm. Graphical and tabular behavior of velocity, nanoparticles concentration and temperature fields is inspected corresponding to related variables. This research highlights key findings, revealing a decrease in Nusselt number with increasing heat generation, Dufour parameter and thermophoresis parameter values while opposite trends are verified for radiation parameter values. The velocity function declines for escalating Weissenberg number, magnetic field and permeability parameters but it upsurges with material and buoyancy parameters.

A note on the uniqueness of Nash–Cournot equilibria in an oligopolistic energy market with renewable generation and demand uncertainty

This work explores the uniqueness of Nash–Cournot equilibria in wholesale electricity markets with both thermal and renewable generation in the presence of demand uncertainty. Criteria guaranteeing a unique equilibrium are derived for an energy market where each firm owns a share of renewable generation. This work expands upon the uniqueness results proven by Lagerlöf (2006) to incorporate the presence of renewable generation into the market in the manner of Acemoglu et al. (2017). A numerical case study is used to illustrate the potential impacts of increasing renewable generation on equilibrium uniqueness and directions for future research are discussed.

Optimized the locations and sizes of FACTS devices on electrical network involving wind power using a new hybrid stochastic algorithm

This article focuses on utilizing a new hybrid optimization algorithm called the Fitness-Distance Balance-based Archimedes Optimization Algorithm (FDB-AOA) to solve the Optimal Power Flow (OPF) problems within a recently adopted electrical transmission grid, specifically the modified IEEE-30 bus system. This system integrates thermal and wind -based generating units, including various types of Flexible AC Transmission System (FACTS) devices. Several tests are performed where the stochastic wind energy is modeled using probability density functions. The optimization goal takes into account the cost of thermal generation, the direct cost of scheduled wind power, and the penalty cost for underestimating wind power. The locations and sizing of FACTS devices are optimized with aim of reducing several fitness functions. The optimization results achieved by the proposed method in solving single objective functions were more effective in finding the optimal solution compared to several well-known algorithms. The results show the superiority of the proposed method in the majority of case studies, as it achieved a better optimum solution with a total generation cost ( Cgen ) value of 806.9817 $/h, and a real power loss ( Ploss ) value 1.7619 MW, also yields a competitive gross cost ( Cgross ) value of 1104.6652 $/h compared to those obtained by the other algorithms. In contrast, the statistical analysis proven the superiority of this algorithm where the standard deviations (SD) required in solving the single objective problem ( Cgen ) is 0.0996, which are better compared to other techniques. the simulation results demonstrate that the FDB-AOA optimizer is robust than other approaches, like the Success history based-adaptive differential evolution (SHADE) algorithm, MSA (Moth Swarm Algorithm), and ABC (artificial bee colony) integrated with the SF (superiority of feasible solutions) approach, in solving OPF problems involving the integration of both thermal and wind power plants along with FACTS devices.

Advanced Energy Modeling and Prediction of Integrated Micro-Generator System for Useful Heat Harvesting

Theoretical modeling and numerical simulation of an integrated micro-thermoelectric generator system for thermal power generation are carried out. The system measures 4.2 × 4.2 × 5 mm and consists of a micro-thermoelectric module (bismuth telluride) and two finned heat sinks (aluminum). The system can be used to convert thermal energy to electricity in Seebeck effect-based micro-applications. This work aims to improve an advanced model to effectively predict the thermal performance of the system and to develop thermal and flow simulations to accurately evaluate real micro-thermoelectric generator systems. The advanced model solves the thermoelectric module’s energy equations, incorporating heat balance in the heat transfer calculations. The thermal and flow simulations take into account the dynamic calculations under the thermal loads occurring in the system. This innovative aspect can considered separately for the different materials (ceramics, semiconductors and copper strips) of the micro-thermoelectric module for heat transfer enhancement. The results predicted that when the temperature difference of the thermoelectric module was increased from 18 K to 58 K, the power output and the conversion efficiency of the system increased by about 0.5 W and 50%, respectively. Also, the transfer of useful heat to electrical power was achieved at 83%, with 11% saved heat and thermal losses of 6% W at maximum temperature difference of the module. In terms of overall energy consumption, the integrated micro-thermoelectric generator system has a little environmental impacts. Validation of the model with particular experimental works was accomplished for dependability. Comparisons with different modeling strategies demonstrate that the accuracy and performance of the advanced model can be used to reliably study the thermal performance of real micro-thermoelectric generator systems.

Comparative study of Yamada-Ota and Xue models for MHD hybrid nanofluid flow past a rotating stretchable disk: stability analysis

PurposeThe hybrid nanofluid flow due to a rotating disk has numerous applications, including centrifugal pumps, paper production, polymers dying, air filtration systems, automobile cooling and solar collectors. This study aims to investigate the convective heat transport and magnetohydrodynamics (MHD) hybrid nanofluid flow past a stretchable rotating surface using the Yamada-Ota and Xue models with the impacts of heat generation and thermal radiation.Design/methodology/approachThe carbon nanotubes such as single-wall carbon nanotubes and multi-wall carbon nanotubes are suspended in a base fluid like water to make the hybrid nanofluid. The problem’s governing partial differential equations are transformed into a system of ordinary differential equations using similarity transformations. Then, the numerical solutions are found with a bvp4c function in MATLAB software. The impacts of pertinent parameters on the flow and temperature fields are depicted in tables and graphs.FindingsTwo solution branches are discovered in a certain range of unsteadiness parameters. The fluid temperature and the rate of heat transport are enhanced when the thermal radiation and heat generation effects are increased. The Yamada-Ota model has a higher temperature than the Xue model. Furthermore, it is observed that only the first solution remains stable when the stability analysis is implemented.Originality/valueTo the best of the authors’ knowledge, the results stated are original and new with the investigation of MHD hybrid nanofluid flow with convective heat transfer using the extended version of Yamada-Ota and Xue models. Moreover, the novelty of the present study is improved by taking the impacts of heat generation and thermal radiation.

Cellulose-reinforced foam-based phase change composites for multi-source driven energy storage and EMI shielding

To address the increasingly serious environmental pollution and energy crisis, there is an urgent need to develop multi-source-driven energy storage materials, the field of new energy sources, such as solar thermal power generation, but electromagnetic pollution has become a primary problem that needs urgent resolution. Therefore, the development of multifunctional phase change composites (CPCMs) with multi-source drive capabilities and excellent electromagnetic shielding integration is crucial. In this study, polypyrrole (PPy)-coated conductive fibers were crosslinked with polyvinyl alcohol (PVA) to form a three-dimensional network, which was then combined with metal foam to prepare Ni–F/PPy@CNF-PVA (PCN) dual-network carriers with high electrical conductivity by vacuum-assisted adsorption and freeze-drying methods. Polyethylene glycol (PEG) was subsequently encapsulated via vacuum impregnation to get the shape-stable PEG/Ni–F/PPy@CNF-PVA (PPCN) phase change composites. The PPCN exhibited good stability and high energy storage density (melt enthalpy up to 126.18 J/g, relative enthalpy efficiency over 99 %). Benefiting from its outstanding electrical conductivity (186680 S/m for PPCN-3), light-absorbing properties, and magnetism, the PPCN also exhibits highly efficient photothermal, electrothermal, and magnetothermal conversion capabilities. The electromagnetic interference shielding efficiency reaches up to 110.37 dB within the X-band frequency range (8.2–12.4 GHz). In conclusion, PPCNs are significant for multi-source-driven energy storage and electromagnetic shielding.

Theoretical investigation of height and width tapered microchannel cooling systems for ultra-high concentrator photovoltaic thermal hybrids

This paper explores the utilisation of horizontally and vertically converging microchannel heat sinks for cooling concentrator photovoltaic systems. These are compared to a straight microchannel, which is used as the control test. The primary performance parameters considered are the system's energy and exergetic efficiency and the thermal stresses generated in the solar cell. Horizontally converging channels yield more uniform temperature distributions, but the peak and average thermal stress generation increased due to the higher cell temperature. Conversely, vertically converging channels demonstrated a reduction in thermal stress generation. However, channels which converge more sharply require a substantial increase in pumping power for the same cooling-fluid flow rate which negatively impacts energy and exergetic efficiency. Notably, the control design for straight microchannels is specified to have the same manufacture difficulty across configurations, which is an often-overlooked aspect.The evaluation incorporates two irradiance profiles from concentrator optics to enhance the applicability of results. The straight microchannel structure and the vertically converging channel with a taper ratio of 0.75 are identified as the most applicable for concentrator photovoltaic application. The outcomes provide valuable insights on heat sink design and sheds light on the trade-offs between thermal performance, energy efficiency and manufacturability.

A data-driven model for the operation and management of prosumer markets in electric smart grids

The digital transformation of electric power systems requires forecasts and planning for optimal management, as well as real-time data streaming for the ongoing optimization of the system during operation. Recent research efforts have developed models for power system capacity planning, real-time monitoring and control, fault analysis, and energy efficiency assessment. However, those models are usually not integrated and do not combine operational data with management information and real-time decision-making. This paper conceives a data-driven model that integrates optimization and machine learning techniques for optimal operation and management of prosumer markets in electric smart grids. While classical optimization is used during day-ahead mode for operation planning, Gaussian Processes are used to predict demand forecasts for day-ahead and pre-dispatch modes while assimilating real-time measurements. The proposed approach is applied in a case study comprising a community manager coordinating a smart grid with prosumers operating thermal and renewable generators. Results highlight that the data-driven model helps achieve near-optimal operation of the smart grid in normal conditions while guaranteeing its reliability under disruptive events.

Optimal reactive power dispatch with renewable energy sources using hybrid whale and sine cosine optimization algorithm

The optimization of reactive power dispatch entails the complex challenge of controlling and managing the flow of reactive power in power networks to maintain desired voltage levels across many buses. Nowadays, there is a rising preference for employing renewable energy sources rather than traditional thermal generators. This change presents both challenges and possibilities for power system operators and managers. This paper addresses the Optimal Reactive Power Dispatch (ORPD) problem by presenting a novel approach that incorporates solar and wind power plants into existing power networks using the Hybrid Whale and Sine Cosine Optimisation Algorithm (HWSCOA). Solar and wind power plants are established at bus 5 and bus 8 respectively to replace traditional thermal generators in a specific case study using the IEEE 30-bus system. To handle uncertainties associated with load demand changes and the intermittent nature of renewable energy generation, the study employs probability density functions and a variety of scenarios. The primary goal is to minimize power losses in transmission cables while also lowering voltage changes throughout the network. To address uncertainty in load demands and renewable energy output, a scenario-based methodology is used, generating 30 different scenarios to cover all conceivable outcomes. By presenting the ORPD challenge as an optimization problem, the study hopes to achieve considerable reductions in power losses and voltage variations from nominal levels. The findings of this study reveal encouraging results, including significant reductions in power losses and optimized voltage stability even under shifting conditions.

Impact of renewables on the Peruvian electricity system

Peru is committed by international agreements such as the Paris Agreement and the UN 2030 Agenda for Sustainable Development to reduce its Green House Gas (GHG) emissions. Although Peru began promoting power plant projects based on renewable energies in 2008, the institutional impulse seems to have ceased today. Nonetheless, adopting renewable energy sources not only aids in fulfilling these commitments but also reduces electricity prices, fostering a more competitive economy. This study aims to provide a thorough analysis and evaluation of the impact that integrating Non-Conventional Renewable Energy Resources (NCRER) has on Peru's wholesale electricity market. Specifically, it focuses on the net total savings for the system, calculated as the difference between the reduction in energy costs traded and the cost of the renewable premium. To reach that goal a simplified yet analytical tool has been developed based on a multivariable regression of the real data of the market. The volume of natural gas not consumed and the volume of GHG emissions avoided will also be evaluated. The results show that based on the 2021 scenario, a NCRER share of 28.26 % would lead to a net saving for the final users of 316.04 MUSD in the annual energy traded in the market. The reduction of conventional thermal generation prevents the annual burning of 2.04 normal km3 of natural gas and the emission of 4.99 million tons of CO2-eq. To the authors' knowledge, this is the first time that the impact of NCRER production in the Peruvian electricity system has been quantitatively evaluated.

Optimized Genetic Algorithms: Study of the Impact on Load Distribution in the Power System

In order to balance the power flow, voltage and current between renewable energy power generation and thermal power generation, this paper takes renewable energy as the research object, analyzes the power flow and voltage after grid connection, and proposes an improved genetic algorithm for research. Firstly, the log function is used to analyze the power flow and voltage data before and after grid connection, reduce the complexity of the data, and calculate the proportion of photovoltaic energy. Then, the load analysis of the power system was carried out by genetic iteration to identify the overloaded nodes, and the identification standard was greater than 80% of the rated load. Finally, a load set is formed, and information such as changes, power flow, and voltage of different power generation nodes is recorded. The results show that the genetic algorithm can maximize the proportion of renewable energy to 50% and reduce the power flow change rate of the power system to 10%. At the same time, improve the load efficiency of the power system, so that the node load is between 75~80%, average the load of each node, especially the photovoltaic matrix and fan, and shorten the load deployment time, so that it is 10s smaller. Compared with the previous algorithms, the genetic algorithm proposed in this paper is better, which can meet the load distribution requirements of the power system and expand the energy proportion of renewable energy.

Intelligent construction and optimization based on the heatsep method of a supercritical CO2 brayton cycle driven by a solar power tower system

The current progress in tower-based solar thermal concentrators and receivers enables achieving temperatures within the required range of 500–700 °C for the S–CO2 Brayton cycle. Within this temperature range, the efficiency of the S–CO2 Brayton cycle exceeds that of conventional steam power cycles. This study applies the S–CO2 Brayton cycle to concentrated power generation systems, emphasizing structural and parameter optimization. By utilizing the operational parameters of tower-based solar thermal power generation as boundary conditions and maximizing specific power as the optimization objective, various configurations derived from a maximum of two fundamental cycles are systematically enumerated. Subsequently, an intelligent algorithm optimizes the power cycle flow and parameters for each structure, further optimizing the heat exchanger network. The findings reveal that the optimized configuration shares a common heating and cooling process. This optimization yields a specific power increase of 14.62 kJ/kg compared to the reference case, indicating a 13.26 % enhancement. Additionally, the cycle efficiency improves to 47.45 %, marking a 28.52 % increase. The overall system efficiency for the solar power tower system reaches 32.03 %, with an exergy efficiency of 32.19 %.

Improving the Long-Term Stability of PbTe-Based Thermoelectric Modules: From Nanostructures to Packaged Module Architecture.

Nanostructured lead telluride PbTe is among the best-performing thermoelectric materials, for both p- and n-types, for intermediate temperature applications. However, the fabrication of power-generating modules based on nanostructured PbTe still faces challenges related to the stability of the materials, especially nanoprecipitates, and the bonding of electric contacts. In this study, in situ high-temperature transmission electron microscopy observation confirmed the stability of nanoprecipitates in p-type Pb0.973Na0.02Ge0.007Te up to at least ∼786 K. Then, a new architecture for a packaged module was developed for improving durability, preventing unwanted interaction between thermoelectric materials and electrodes, and for reducing thermal stress-induced crack formation. Finite element method simulations of thermal stresses and power generation characteristics were utilized to optimize the new module architecture. Legs of nanostructured p-type Pb0.973Na0.02Ge0.007Te (maximum zT ∼ 2.2 at 795 K) and nanostructured n-type Pb0.98Ga0.02Te (maximum zT ∼ 1.5 at 748 K) were stacked with flexible Fe-foil diffusion barrier layers and Ag-foil-interconnecting electrodes forming stable interfaces between electrodes and PbTe in the packaged module. For the bare module, a maximum conversion efficiency of ∼6.8% was obtained for a temperature difference of ∼480 K. Only ∼3% reduction in output power and efficiency was found after long-term operation of the bare module for ∼740 h (∼31 days) at a hot-side temperature of ∼673 K, demonstrating good long-term stability.

Investigating the Effects of Increased Thermal Generation by Unit Commitment Optimization in Hydrothermal Power Systems Using Lagrange Algorithm

Investigating the Effects of Increased Thermal Generation by Unit Commitment Optimization in Hydrothermal Power Systems Using Lagrange Algorithm