Cost Of Power Generation Research Articles

Study on a new reed-coal coupled power generation system in Wuliangsuhai Lake wetland —Life cycle assessment and economic and technical analysis

As a major hydrophyte in wetlands, reeds purify water by reducing water eutrophication and have high value-added potential as biomass fuel for power generation. Based on pyrolysis experiments, reed has the maximum bio-oil yield of 57.11% at 550 °C. In this study, the optimal temperature conditions were used for the pyrolysis of reeds coupled with thermal power generation (BP-TPG). The BP-TPG process is relatively independent and avoids the deposition of alkali metals from biomass on the surface of the steam generator directly. The BP-TPG and biomass gasification coupled thermal power Generation (BG-TPG) were further investigated by process simulation and life cycle assessment. The GWP values of BP-TPG systems could be reduced by at least 4.8% than that of TPG. As the growth of reeds absorbed total nitrogen (TN) and total phosphorus (TP) from the wetland water, the eutrophication potential was reduced by 0.333 kg PO4/MWh less than that of the use of corn straw feedstocks. Economic and technical analysis showed that the BP-TPG-G system had the lowest cost of electricity generation at 386 ¥/MWh. Sensitivity analysis showed that the impact of coal prices on power generation costs cannot be ignored.

Economic comparison of floating photovoltaic systems with tracking systems and active cooling in a Mediterranean water basin

Floating Photovoltaic (FPV) modules are installed on water surface to reduce land use. This original solution, potentially deployable on hydropower and aquaculture basins as well, can benefit of enhanced cooling due to the proximity to water. Thanks to this natural effect, FPV modules can work at higher operating efficiencies than ground-based (GPV) modules. However, because of the relatively young age, FPV still requires higher installation costs than GPV. This study investigates the economic competitiveness of GPV and FPV in terms of energy performance and total costs. Different PV system solutions are economically evaluated on the basis of three key figures, namely the capital costs (CAPEX), the operation and maintenance costs (OPEX) and the power generation costs (LCOE). An economic ranking is created based on the comparative analysis of these three key figures.The crucial point in the proposed economic model is that the revenues resulting from the reduced evaporations are considered as well. Every year, indeed, a significant volume of water can be preserved thanks to the shading effect of FPV modules. This water can be used for various purposes, increasing the overall revenues of the FPV system. In addition, the present LCOE calculations also take into account the performance enhancements that could be achieved through the installation of active cooling systems.In light of the expected economy of scale, a sensitivity analysis of the LCOE is carried out to account potential reductions in the capital cost of FPVs. This is done by analyzing the energy and economic performance of various FPV designs on a water basin in Southern Italy. The results demonstrate that, reducing the CAPEX of the FPV by 30 %, a nearly 20 % reduction in LCOE can be obtained compared to the reference GPV system.

Coordinated Dispatch Optimization between the Main Grid and Virtual Power Plants Based on Multi-Parametric Quadratic Programming

Virtual power plants (VPPs) are a critical technology for distribution systems that can integrate various renewable energy resourcescontrollable loads and energy storage systems into one specific power plant through a distributed energy management system. This paper proposes a coordinated dispatch optimization model between the main grid and VPPs aiming to minimize both the power generation cost and total system active loss. When the time of the equivalent dispatching model is not divisible due to the existence of a time coupling constraint inside the VPPs, this model can obtain the global optimal solution through iteration between the main grid and the VPPs. By employing multi-parametric quadratic programming to obtain accurate critical domains and optimal cost functions, the convergence speed and stability are significantly improved. Additionally, a reactive power and voltage optimization technique leveraging the generalized Benders decomposition is presented for the coordination of the main grid and the VPPs. Moreover, the impact of distributed energy resource (DER) clusters on the main grid was studied, from which we proved that the proposed approach can expeditiously abate energy production expenditure and system active dissipation whilst enhancing the system equilibrium.

Dynamic thermal line rating model of conductor based on prediction of meteorological parameters

This paper developed a new algorithm to predict dynamic thermal line rating to increase the capacity of transmission lines, which can enhance the capacity of wind power integrated to the grid and reduce the curtailment. The proposed dynamic thermal line rating prediction model was trained by analyzing historical meteorological data and conductor physical parameters, and used deep learning with parameters optimized by an optimized algorithm. The prediction accuracy of the model is verified by Mean Absolute Error, R2 and comparison with other models. The simulation results show that the proposed prediction model has a good performance. The suggested dynamic thermal line rating algorithm, which bears resemblance to the actual value, boosts the static thermal line rating by varying degrees of 23% to 75% at different instances throughout the sample. At the same time, this paper designs an optimal power flow economic dispatch objective function. By comparing the economic dispatch of the power grid calculated by adding static thermal line rating and the prediction models, the method proposed in this paper can effectively increase the amount of wind power integration and reduce power generation costs.

Multi-objective optimal dispatch strategy for power systems with Spatio-temporal distribution of air pollutants

Accurate and effective pollutant emission control models are key in mitigating the environmental impact of energy supply systems. This paper proposes a novel high-dimensional multi-objective optimal dispatching strategy for power systems considering the spatial and temporal distribution of multiple pollutants. Firstly, a spatial and temporal distribution model of pollutants in thermal power plants is constructed, which is truly applicable to power dispatch. For modeling the pollution situation, the daily changes in the atmospheric boundary layer are taken into account, fully reflecting the pollutant dispersion characteristics of thermal power plants and improving the result accuracy. Next, a high-dimensional multi-objective optimization model is developed to simultaneously reduce the cost of power generation, carbon emission and the impact air quality from VOCs (volatile organic compounds), SO2 and NO2. This model combines the spatial and temporal distribution characteristics of various pollutants and environmental capacity. Finally, a representative high-dimensional multi-objective optimization algorithm is employed to obtain an approximate Pareto-optimal solution set. And a multi-objective decision method is proposed to filter the compromise solution. The results show that the APCs of VOCs, SO2 and NO2 are reduced by 27.5%, 10.13% and 23.16%, respectively, in the pollution day model. On non-pollution days, the proposed scheduling method not only effectively improves air quality, but also achieves cost savings of $1276.59 and reduces CO2 emission by 0.168 × 104t compared to the emission limitation method. The proposed scheduling method can not only effectively improve air quality, but also make corresponding adjustments according to the spatial and temporal changes of environmental capacity, which can truly realize economic and environmental-friendly power scheduling.

Multi-objective Optimal Scheduling Analysis of Power System Based on Improved Particle Swarm Algorithm

Economic Environmental Dispatching (EED) in power systems is a multi-variable, strongly constrained, non-convex, multi-objective optimization problem that is difficult to properly handle using traditional methods. However, the application of particle swarm optimization algorithms may result in insufficient population diversity and easy to fall into local optimization problems. Therefore, this paper proposes an adaptive backbone multi-objective particle swarm optimization (ABBMOPSO) method to solve the economic and environmental scheduling problems of power systems. This paper first analyzes the topology and computational flow of particle swarm optimization algorithms, and then constructs a multi-objective optimization research framework that integrates Pareto optimization principles for the scheduling of power generation units. The execution algorithm is the improved multi-objective particle swarm optimization algorithm (MOPSO). This paper establishes a mathematical model for the economic and environmental scheduling of power systems, which optimizes conflicting fuel cost functions and pollutant emission functions simultaneously, taking into account nonlinear constraints such as load balance constraints and unit operation constraints. The improved ABBMOPSO algorithm is used to optimize the solution to improve the global search ability of the EED model. The simulation data of seven units show that the ABBMOPSO algorithm has a minimum power generation cost of 588.1 $/h and a minimum pollutant emission of 0.192 t/h, which is significantly superior to other algorithms and reduces the number of iterations, with good feasibility.

Power production competition under different regulations: a social welfare perspective

With the prevalence of low-carbon transmission, traditional energy power generation enterprises (TGEs) are facing increasing pressure. By comparison, renewable energy power generation (REPG) holds the advantage of low carbon. To promote the development of REPG, various regulation policies have been implemented. This paper aims at investigating the interplays between renewable energy power generation enterprise (RGE) and TGE’s production decisions under different regulation policies from the social welfare perspective. We consider the feed-in tariff (FIT), renewable portfolio standard (RPS), and carbon tax (CT) policies. Our analyses reveal several major findings. First, the influence of the regulation policy on RGE and TGE’s power generation quantities depends on the gap between REPG cost and traditional energy power generation (TEPG) cost. Second, the regulator’s optimal regulation policy hinges on his emphasis on the economic benefit and the environmental protection. Third, as the REPG cost decreases and the regulator’s emphasis on environmental protection increases, the regulator’s optimal policy from the social welfare perspective exhibits a trend of ‘FIT-CT-RPS.' Fourth, TGE’s preferred policy focuses on FIT or CT, and RGE’s preferred policy is always RPS regardless of how the regulator’s attention on environmental protection changes. On this basis, managerial implications for promoting REPG are provided.

Cost-inclusive heuristic power management of hybrid power generation plant with ANN-regulated biogas plant

To mitigate the negative effects of fossil fuels, renewable energy sources are being promoted for power generation. Biomass, a renewable energy source, is also being promoted to reduce the negative impact of organic waste generated in societies and industries. Waste biomass is used to produce biogas fuel in a biogas plant via an anaerobic digestion process, which is typically used to generate electricity as well as fuel for cooking and vehicles. However, the high local electrical load in biogas plants reduces profit for plant owner. Biogas-based power generation is hybridised with Photovoltaics (PV) and Battery energy storage systems (BESS), to meet power demand, to increase profit and power generation capacity while reducing the burden of local electrical load on biogas plants. A Proton-exchange membrane fuel cell (PEMFC) stack has been used for power generation from the hydrogen produced from the biogas obtained from the anaerobic digestion process. A cost-inclusive heuristic power management system has been designed for power management in hybrid power generation, considering the availability of sources and their dynamic tariffs. An artificial neural network (ANN) has also been designed to determine the digester operating temperature according to methane yield requirements. The designed system has been investigated for hydrogen consumption, carbon emission, power generation cost, operating cost, power factor, frequency, distortion, efficiency, and voltage regulation. The proposed system effectively meets the load demand at a lesser cost in different scenarios. Cost saving of 28.42%, carbon emission reduction by 96% and 4.53% Total harmonic distortion (THD) at a power factor of 0.99 has been observed. The complete proposed hybrid power plant is ideal for implementation in societies and industries.

Novel Heuristic Optimization Technique to Solve Economic Load Dispatch and Economic Emission Load Dispatch Problems

The fundamental objective of economic load dispatch is to operate the available generating units such that the needed load demand satisfies the lowest generation cost and also complies with the various constraints. With proper power system operation planning using optimized generation limits, it is possible to reduce the cost of power generation. To fulfill the needs of such objectives, proper planning and economic load dispatch can help to plan the operation of the electrical power system. To optimize the economic load dispatch problems, various classical and new evolutionary optimization approaches have been used in research articles. Classical optimization techniques are outdated due to many limitations and are also unable to provide a global solution to the ELD problem. This work uses a new variant of particle swarm optimization techniques called modified particle swarm optimization, which is effective and efficient at finding optimum solutions for single as well as multi-objective economic load dispatch problems. The proposed MPSO is used to solve single and multi-objective problems. This work considers constraints like power balance and power generation limits. The proposed techniques are tested for three different case studies of ELD and EELD problems. (1) The first case is tested using the data of 13 generating unit systems along with the valve point loading effect; (2) the second case is tested using 15 generating unit systems along with the ramp rate limits; and (3) the third case is tested using the economic emission dispatch (EELD) as a multi-objective problem for 6 generating unit systems. The outcomes of the suggested procedures are contrasted with those of alternative optimization methods. The results show that the suggested strategy is efficient and produces superior optimization outcomes than existing optimization techniques.

Modelling the impacts of carbon pricing in India’s power system using a dynamic optimization approach

India has a significant importance in achieving the global Paris agreement’s long-term goal as it is the world's third-largest consumer of energy. This paper aims to improve the understanding of the opportunities and constraints of decarbonizing India’s electricity sector and adds to the growing body of literature by modelling the electricity sector in India with a dynamic optimization approach that assesses options for effective greenhouse reductions through carbon pricing. Attaining significant variable renewable energy penetration in India by 2050 is consistent with the lowest cost power generation options identified through our simulations, where solar and wind can provide 66 % of the electricity needs by 2050, even without a carbon price. With a carbon price of US$50/tCO2e, the simulations project that wind and power generation share in the electricity mix will rise to 81 %, leading to significant emission reductions. In this scenario, power sector emissions will be 164 million tCO2e by 2050, which is only about 14 % of the emissions levels in 2019. The analysis also indicates that fossil fuels remain part of India’s electricity mix in 2050, but their role and operation would dramatically change. When a carbon price of US$50/tCO2e is introduced, coal capacity remains valuable to fill demand when solar and wind are not available. This would require modifications in operation schedules and repurposing coal power plants for frequent ramping up and down.

Optimal Scheduling of the Wind-Photovoltaic-Energy Storage Multi-Energy Complementary System Considering Battery Service Life

Under the background of “peak carbon dioxide emissions by 2030 and carbon neutrality by 2060 strategies” and grid-connected large-scale renewables, the grid usually adopts a method of optimal scheduling to improve its ability to cope with the stochastic and volatile nature of renewable energy and to increase economic efficiency. This article proposes a short-term optimal scheduling model for wind–solar storage combined-power generation systems in high-penetration renewable energy areas. After the comprehensive consideration of battery life, energy storage units, and load characteristics, a hybrid energy storage operation strategy was developed. The model uses the remaining energy in the system after deducting wind PV and energy storage output as the “generalized load”. An improved particle swarm optimization (PSO) is used to solve the scheduling schemes of different running strategies under different objectives. The optimization strategy optimizes the battery life-loss coefficient from 0.073% to 0.055% under the target of minimizing the mean squared deviation of “generalized load”, which was optimized from 0.088% to 0.053% under the minimized fluctuation of combined system output and optimized from 0.092% to 0.081% under the minimized generation costs of the combined system. The results show that the model can ensure a stable operation of the combined system, and the operation strategy proposed in this article effectively reduces battery life loss while reducing the total power generation cost of the system. Finally, the superiority of the improved PSO algorithm was verified.

Advanced Gas Turbine Cooling for the Carbon-Neutral Era

In the coming carbon-neutral era, industrial gas turbines (GT) will continue to play an important role as energy conversion equipment with high thermal efficiency and as stabilizers of the electric power grid. Because of the transition to a clean fuel, such as hydrogen or ammonia, the main modifications will lie with the combustor. It can be expected that small and medium-sized gas turbines will burn fewer inferior fuels, and the scope of cogeneration activities they are used for will be expanded. Industrial gas turbine cycles including CCGT appropriate for the carbon-neutral era are surveyed from the viewpoint of thermodynamics. The use of clean fuels and carbon capture and storage (CCS) will inevitably increase the unit cost of power generation. Therefore, the first objective is to present thermodynamic cycles that fulfil these requirements, as well as their verification tests. One conclusion is that it is necessary to realize the oxy-fuel cycle as a method to utilize carbon-heavy fuels and biomass and not generate NOx from hydrogen combustion at high temperatures. The second objective of the authors is to show the required morphology of the cooling structures in airfoils, which enable industrial gas turbines with a higher efficiency. In order to achieve this, a survey of the historical development of the existing cooling methods is presented first. CastCool® and wafer and diffusion bonding blades are discussed as turbine cooling technologies applicable to future GTs. Based on these, new designs already under development are shown. Most of the impetus comes from the development of aviation airfoils, which can be more readily applied to industrial gas turbines because the operation will become more similar. Double-wall cooling (DWC) blades can be considered for these future industrial gas turbines. It will be possible in the near future to fabricate the DWC structures desired by turbine cooling designers using additive manufacturing (AM). Another conclusion is that additively manufactured DWC is the best cooling technique for these future gas turbines. However, at present, research in this field and the data generated are scattered, and it is not yet possible for heat transfer designers to fabricate cooling structures with the desired accuracy.

A Linear Programming Method Based Optimal Power Flow Problem for Iraqi Extra High Voltage Grid (EHV)

The objective of an Optimal Power Flow (OPF) algorithm is to find steady state operation point which minimizes generation cost, loss etc. while maintaining an acceptable system performance in terms of limits on generators real and reactive powers, line flow limits etc. The OPF solution includes an objective function. A common objective function concerns the active power generation cost. A Linear programming method is proposed to solve the OPF problem. The Linear Programming (LP) approach transforms the nonlinear optimization problem into an iterative algorithm that in each iteration solves a linear optimization problem resulting from linearization both the objective function and constrains. A computer program, written in MATLAB environment, is developed to represent the proposed method. The adopted program is applied for the first time on Iraqi 24 bus Extra High Voltage (EHV) network (400 kV). The required are data taken from the operation and control office, which belongs to the ministry of electricity.

Design a high-performance thermoelectric generator by analyzing industrial heat transfer

Industrial production will release a large amount of waste heat into the environment and cause serious energy waste. Currently, most waste heat recovery technologies cannot directly convert waste heat into energy required for industrial production, and there is a problem of low energy conversion efficiency problem. Thermoelectric conversion technology can convert industrial waste heat into electrical energy and directly use it in industrial production. This study uses a numerical method to analyze the heat transfer behavior between an industrial thermal environment and a thermoelectric generator (TEG). According to the numerical calculation results, the deficiencies of the original TEG are optimized, and a new TEG with high power generation performance and stability is designed. Finally, the accuracy of numerical calculation results and the industrial application feasibility of new TEG are verified through on-site tests. The results show that the power generation performance of the new TEG has been greatly improved, and it can operate stably for a long time in an industrial environment. The best economic benefit can be obtained by installing new TEGs in the x = 20 m-21 m, the power generation cost is 3.45$/W, and the total output power is 0.55 kW. The cost of power generation for new TEGs will increase as the installation area increases. When new TEGs are installed in the x = 20 m-24 m, the power generation cost is 3.97$/W, and the total output power is 1.96 kW. It can be known from the on-site testing results that the new TEG has high practical value and can be widely used in industrial waste heat recovery.

Energy, economic and environmental performance evaluation of co-gasification of coal and biomass negative-carbon emission system

This study evaluated the energy, economic, and environmental performance of co-gasification coal and biomass negative-carbon emission systems. All coal and all biomass systems were analyzed in detail for performance differences. The effects of the blending ratio and carbon capture rate (CCR) on the system were explored at the technical level. In addition, the impacts of carbon trading and fuel prices were considered at the market level. The results showed that the co-gasification system effectively improved the heat value of syngas and reduced the power generation cost. The efficiency and levelized cost of energy (LCOE) increase with the blending ratio. Compared with the 100 % biomass system, the power generation cost was reduced by 19 % at most. For the certain blending ratio, the energy and economic performance deteriorated as the CCR increased. A higher CCR corresponds to a higher cost of capturing unit CO2. The economic advantage decreased significantly with increasing CCR for the system with lower blending ratio. The LCOE was most sensitive to the carbon trading price at the market level. The LCOE was reduced by $ 1.93 as the carbon trading price increased by $ 1. This study demonstrates the potential and promotion value of negative-carbon emission power generation systems.

Analysis of Hydrogen Production Potential from Water Electrolysis in China

Currently, the cost of electricity for electrolysis of producing hydrogen is about 70–90% of the total cost. Scientists predict that the share of renewable energy in total energy is expected to reach about 70% in 2050, as the cost of wind photovoltaic power generation in China is as low as 0.13¥/(kW·h), and that the use of renewable energy sources, mainly photovoltaic and wind power, of producing hydrogen can effectively reduce the cost of electricity. Meanwhile, with the improvement of technology, the cost of an electrolyzer used in proton exchange membrane hydrogen production technology can be reduced by about 50%, which will reduce the cost of hydrogen production by more than 20%. According to the distribution of electricity resources in each region of China, the project construction of renewable energy power generation and proton exchange membrane hydrogen production can be reasonably carried out. A long-distance hydrogen transmission system can be established to provide guaranty for the establishment and operation of urban hydrogen power stations using a combination of renewable energy and long-distance hydrogen transmission systems. This paper provides a reference for the industrial development and policy direction of hydrogen production by water electrolysis.

Economic/Environmental Optimal Power Flow Using a Multiobjective Convex Formulation

This paper addresses the problem of economic/environmental optimal power flow with a multiobjective formulation using a second-order conic programming (SOCP) optimization model. This problem formulation considers renewable energy sources (RES), fossil-fuel-based power generation units, and voltage control. The proposed SOCP model is a stochastic scenario-based approach to deal with RES and load behavior uncertainties. An ε-constrained algorithm is used to handle the following three objective functions: (1) the costs of power generation, (2) active power losses in the branches, and (3) the emission of pollutant gases produced by fossil-fuel-based power generation units. For comparative purposes, the SOCP model is also presented using a linearized formulation, and numerical results are presented using a 118-bus system. The results confirm that changing the energy matrices directly affects the cost of objective functions. Additionally, using a linearized SOCP model significantly reduces reactive power violation in the generation units when compared to the nonlinearized SOCP model, but also increases the computational time consumed.

Economic and thermal analysis of a tubular thermoelectric power generator equipped with a novel fin-pin-porous based heat exchanger; comparative case study with conventional smooth channel

Wasted hot exhaust gas in thermodynamic engineering systems is always a potential source of power generation via different techniques. Passing the hot gas through the inner area of a tubular thermoelectric module can directly convert the hot gas into electricity. In this research, it is shown how the performance of tubular thermoelectric heat recovery is significantly boosted using a novel fin-pin-porous based heat exchanger. Generally, the results show that the proposed integrated structure can enhance the output power of the recovery system up to 45%–110% compared to the conventional smooth channel-based tubular thermoelectric generator. A comparative case study is carried out between the new proposed system and conventional smooth structure from different viewpoints including power generation, conversion efficiency, pumping power and economic cost.

Transmission expansion planning considering solar photovoltaic using novel binary particle swarm optimization

Due to the demand growth and an increase in uncertain generation from solar photovoltaics which are promoted instead of fossil fuel, the transmission expansion planner needs to take this uncertain generation into account. This paper proposes Transmission Expansion Planning (TEP) considering solar PhotoVoltaic (PV) using Novel Binary Particle Swarm Optimization (NBPSO) algorithm to define the location and number of the Transmission Lines Candidate (TLC) that will be installed into the existing power system. The goal is to minimize the total cost consisting of the investment cost of the newly installed TL and the operation cost of power generation. The case study consists of two cases. The TEP when solar PV is disregarded is solved and analyzed in the first case. For the second case, the TEP when considering solar PV generation is studied. The aforementioned cases were analyzed on the IEEE 24 bus test system. The optimization program is written by MATLAB software with the MATPOWER tool to solve optimal power flow. From the simulation result, the NBPSO can find the TEP solution with the lowest investment cost and operating cost while the obtained results do not violate the power system constraints.

Multi-objective reactive power optimization strategy of power system considering large-scale renewable integration

To fully tap the abilities of renewables in reactive power optimization, this paper develops a detailed model for the power regulation capabilities of wind turbines and photovoltaic units and studies their impact on the power system’s operation. First, the power system model with renewables integration is established using AC power flow. The wind turbines and photovoltaic units are modeled in detail according to their topologies and operating characteristics, and then further simplified according to the feasible region. An improved DC power flow model is adopted to handle the non-linear characteristics of the power system. On this basis, a multi-objective reactive power optimization model is constructed to minimize the power generation cost, wind and solar power curtailment, and voltage offset. Finally, comparisons between two types of models in different scenarios are designed. Numerical simulations demonstrate that the participation of renewables in reactive power regulation can improve the operational economy and voltage stability of power systems.