Quantifying social costs of coal-fired power plant generation

Coal has been dominating the electricity supply in Indonesia,especially in long-term power generation from fossil energy.This dominance is due to lower production costs in coal-fired power plant generation.However,this low price is only based on monetary costs and ignores the social costs.Therefore,this study aims to quantify the social costs of coal-fired generation.Using QUERI-AirPacts modeling,the present study quantifies the social costs resulting from the Tenayan Raya coal-fired generation in Riau,Indonesia.It includes the levelized cost of electricity and health costs into the generation costs.After that,this study calculates the net present value,internal rate return,and project payback period.The study found that as much as$50.22/MWh was the levelized cost of electricity.While$15.978/MWh or$0.015978/kWh was the social cost that was not included in the generating cost.At the electricity production level of 1,380,171.69 MWh per year,there is an expected extra cost of$22,052,383.30 uncounted when externalities are included.For instance,the net present value(NPV)is lower and even negative when external costs are included(-$24,062,274.19)compared to$176,108,091.52 when externalities are not considered.The internal rate of return(IRR)is much higher when the social costs are not considered.The payback period is also shorter when the social costs are excluded than when the externalities are included.This global number indicates that the inclusion of external costs would impact NPV,IRR,and the payback period.This result implies that the government should internalize the external cost to stimulate the electricity producers to conduct cost-benefit analyses.The cost-benefit analysis mechanism would lead the producers to be more efficient.

Investigating Load Regulation Characteristics of a Wind-PV-Coal Storage Multi-Power Generation System

There is a growing need to explore the potential of coal-fired power plants(CFPPs)to enhance the utilization rate of wind power(wind)and photovoltaic power(PV)in the green energy field.This study developed a load regulation model for a multi-power generation system comprising wind,PV,and coal energy storage using realworld data.The power supply process was divided into eight fundamental load regulation scenarios,elucidating the influence of each scenario on load regulation.Within the framework of the multi-power generation system with the wind(50 MW)and PV(50 MW)alongside a CFPP(330 MW),a lithium-iron phosphate energy storage system(LIPBESS)was integrated to improve the system’s load regulation flexibility.The energy storage operation strategy was formulated based on the charging and discharging priority of the LIPBESS for each basic scenario and the charging and discharging load calculation method of LIPBESS auxiliary regulation.Through optimization using the particle swarm algorithm,the optimal capacity of LIPBESS was determined to be within the 5.24-4.88 MWh range.From an economic perspective,the LIPBESS operating with CFPP as the regulating power source was 49.1% lower in capacity compared to the renewable energy-based storage mode.

Research on the MPPT of Photovoltaic Power Generation Based on Improved WOA and P&O under Partial Shading Conditions

Partial shading conditions(PSCs)caused by uneven illumination become one of the most common problems in photovoltaic(PV)systems,which can make the PV power-voltage(P-V)characteristics curve show multi-peaks.Traditional maximum power point tracking(MPPT)methods have shortcomings in tracking to the global maximum power point(GMPP),resulting in a dramatic decrease in output power.In order to solve the above problems,intelligent algorithms are used in MPPT.However,the existing intelligent algorithms have some disadvantages,such as slow convergence speed and large search oscillation.Therefore,an improved whale algorithm(IWOA)combined with the P&O(IWOA-P&O)is proposed for the MPPT of PV power generation in this paper.Firstly,IWOA is used to track the range interval of the GMPP,and then P&O is used to accurately find the MPP in that interval.Compared with other algorithms,simulation results show that this method has an average tracking efficiency of 99.79%and an average tracking time of 0.16 s when tracking GMPP.Finally,experimental verification is conducted,and the results show that the proposed algorithm has better MPPT performance compared to popular particle swarm optimization(PSO)and PSO-P&O algorithms.

Multi-objective optimization and evaluation of supercritical CO_(2) Brayton cycle for nuclear power generation

The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.

Economic Power Dispatching from Distributed Generations: Review of Optimization Techniques

In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop control,predictive control,and fuzzy-based methods.These methods can deal with large-scale systems with many objectives and non-linear,non-convex optimization issues.The most popular approaches are the LP and QP models,while more difficult problems are handled using metaheuristic optimization techniques.In summary,in order to increase efficiency,reduce costs,and ensure a consistent supply of electricity,optimization techniques are essential tools used in economic power dispatching from DGs.

Comprehensive Benefit Evaluation of SZ Distributed Photovoltaic Power Generation Project Based on AHP-Matter-Element Extension Model

With the introduction of the“dual carbon goals,”there has been a robust development of distributed photovoltaic power generation projects in the promotion of their construction.As part of this initiative,a comprehensive and systematic analysis has been conducted to study the overall benefits of photovoltaic power generation projects.The evaluation process encompasses economic,technical,environmental,and social aspects,providing corresponding analysis methods and data references.Furthermore,targeted countermeasures and suggestions are proposed,signifying the research’s importance for the construction and development of subsequent distributed photovoltaic power generation projects.

Research on solar aided coal-fired power generation system and performance analysis

Integrating solar power utilization systems with coal-fired power units, the solar aided coal-fired power generation (SACPG) shows a significant prospect for the large-scale utilization of solar energy and energy saving of thermal power units. The methods and mechanism of system integration were studied. The parabolic trough solar collectors were used to collect solar energy and the integration scheme of SACPG system was determined considering the matching of working fluid flows and energy flows. The thermodynamic characteristics of solar thermal power generation and their effects on the performance of thermal power units were studied, and based on this the integration and optimization model of system structure and parameters were built up. The integration rules and coupling mecha- nism of SACPG systems were summarized in accordance with simulation results. The economic analysis of this SACPG system showed that the solar LEC of a typical SACPG system, considering CO2 avoidance, is 0.098 $/kW·h, lower than that of SEGS, 0.14 $/kW·h.

Analysis and Power Quality Improvement in Hybrid Distributed Generation System with Utilization of Unified Power Quality Conditioner

This paper presents a comprehensive study that includes the sizing and power flow by series and parallel inverters in a distributed generation system(DGs)that integrates the system of hybrid wind photovoltaic with a unified power quality conditioner(UPQC).In addition to supplying active power to the utility grid,the system of hybrid wind photovoltaic functions as a UPQC,compensating reactive power and suppressing the harmonic load currents.Additionally,the load is supplied with harmonic-free,balanced and regulated output voltages.Since PVWind-UPQC is established on a dual compensation scheme,the series inverter works like a sinusoidal current source,while the parallel inverter works like a sinusoidal voltage source.Consequently,a smooth alteration from interconnected operating modes to island operating modes and vice versa can be achieved without load voltage transients.Since PV-Wind-UPQC inverters handle the energy generated through the hybrid wind photovoltaic system and the energy demanded through the load,the converters should be sized cautiously.A detailed study of the flow of power via the PV-Wind-UPQC is imperative to gain a complete understanding of the system operation and the proper design of the converters.Thus,curves that allow the sizing of the power converters according to the power flow via the converters are presented and discussed.Simulation results are presented to assess both steady state and dynamic performances of the grid connected hybrid system of PV-Wind-UPQC.This investigation is verified by simulating and analyzing the results with Matlab/Simulink.

Dynamic and Power Generation Features of A Wind-Wave Hybrid System Consisting of A Spar-Type Wind Turbine and An Annular Wave Energy Converter in Irregular Waves

Combining wave energy converters(WECs)with floating offshore wind turbines proves a potential strategy to achieve better use of marine renewable energy.The full coupling investigation on the dynamic and power generation features of the hybrid systems under operational sea states is necessary but limited by numerical simulation tools.Here an aero-hydro-servo-elastic coupling numerical tool is developed and applied to investigate the motion,mooring tension,and energy conversion performance of a hybrid system consisting of a spar-type floating wind turbine and an annular wave energy converter.Results show that the addition of the WEC has no significant negative effect on the dynamic performance of the platform and even enhances the rotational stability of the platform.For surge and pitch motion,the peak of the spectra is originated from the dominating wave component,whereas for the heave motion,the peak of the spectrum is the superposed effect of the dominating wave component and the resonance of the system.The addition of the annular WEC can slightly improve the wind power by making the rotor to be in a better position to face the incoming wind and provide considerable wave energy production,which can compensate for the downtime of the offshore wind.

Effect of Photovoltaic Power Generation on Carbon Dioxide Emission Reduction under Double Carbon Background

Increasing the efficiency and proportion of photovoltaic power generation installations is one of the best ways to reduce both CO_(2) emissions and reliance on fossil-fuel-based power supplies.Solar energy is a clean and renewable power source with excellent potential for further development and utilization.In 2021,the global solar installed capacity was about 749.7 GW.Establishing correlations between solar power generation,standard coal equivalent,carbon sinks,and green sinks is crucial.However,there have been few reports about correlations between the efficiency of tracking solar photovoltaic panels and the above parameters.This paper calculates the increased power generation achievable through the use of tracking photovoltaic panels compared with traditional fixed panels and establishes relationships between power generation,standard coal equivalent,and carbon sinks,providing a basis for attempts to reduce reliance on carbon-based fuels.The calculations show that power generation efficiency can be improved by about 26.12%by enabling solar panels to track the sun's rays during the day and from season to season.Through the use of this improved technology,global CO_(2) emissions can be reduced by 183.63 Mt,and the standard coal equivalent can be reduced by 73.67 Mt yearly.Carbon capture is worth approximately EUR 15.48 billion,and carbon accounting analysis plays a vital role in carbon trading.

PSO-BP-Based Optimal Allocation Model for Complementary Generation Capacity of the Photovoltaic Power Station

To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed.Particle Swarm Optimization and BP neural network are used to establish the forecasting model,the Markov chain model is used to correct the forecasting error of the model,and the weighted fitting method is used to forecast the annual load curve,to complete the optimal allocation of complementary generating capacity of photovoltaic power stations.The experimental results show that thismethod reduces the average loss of photovoltaic output prediction,improves the prediction accuracy and recall rate of photovoltaic output prediction,and ensures the effective operation of the power system.

Problems of Hazardous Waste Storage Facilities in Coal-fired Power Plants and Countermeasures

The hazardous waste produced by coal-fired power plants are large in quantity and variety. It is important for ecological environment protection to properly store hazardous waste in coal-fired power plants. The environmental management of hazardous waste in coal-fired power plants started late, and there are many problems in the construction and management of their storage facilities. In this paper, taking eight typical coal-fired power plants as examples, the present problems of hazardous waste storage facilities in coal-fired power plants are analyzed, and corresponding countermeasures are put forward to solve the main common problems.

Short-Term Prediction of Photovoltaic Power Generation Based on LMD Permutation Entropy and Singular Spectrum Analysis

The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.

Research on Equivalent Modeling Method of AC-DC Power Networks Integrating with Renewable Energy Generation

Along with the increasing integration of renewable energy generation in AC-DC power networks,investigating the dynamic behaviors of this complex system with a proper equivalent model is significant.This paper presents an equivalent modeling method for the AC-DC power networks with doubly-fed induction generator(DFIG)based wind farms to decrease the simulation scale and computational burden.For the AC-DC power networks,the equivalent modeling strategy in accordance with the physical structure simplification is stated.Regarding the DFIG-based wind farms,the equivalent modeling based on the sequential identification of multi-machine parameters using the improved chaotic cuckoo search algorithm(ICCSA)is conducted.In light of the MATLAB simulation platform,a two-zone four-DC interconnected power grid with wind farms is built to check the efficacy of the proposed equivalentmodelingmethod.Fromthe simulation analyses and comparative validation in different algorithms and cases,the proposed method can precisely reflect the steady and dynamic performance of the demonstrated system under N-1 and N-2 fault scenarios,and it can efficiently achieve the parameter identification of the wind farms and fulfill the equivalent modeling.Consequently,the proposed approach’s effectiveness and suitability are confirmed.

Optimal Placement and Sizing of Distributed Generations for Power Losses Minimization Using PSO-Based Deep Learning Techniques

The integration of distributed generations (DGs) into distribution systems (DSs) is increasingly becoming a solution for compensating for isolated local energy systems (ILESs). Additionally, distributed generations are used for self-consumption with excess energy injected into centralized grids (CGs). However, the improper sizing of renewable energy systems (RESs) exposes the entire system to power losses. This work presents an optimization of a system consisting of distributed generations. Firstly, PSO algorithms evaluate the size of the entire system on the IEEE bus 14 test standard. Secondly, the size of the system is allocated using improved Particles Swarm Optimization (IPSO). The convergence speed of the objective function enables a conjecture to be made about the robustness of the proposed system. The power and voltage profile on the IEEE 14-bus standard displays a decrease in power losses and an appropriate response to energy demands (EDs), validating the proposed method.

Application of Multivariate Reinforcement Learning Engine in Optimizing the Power Generation Process of Domestic Waste Incineration

Garbage incineration is an ideal method for the harmless and resource-oriented treatment of urban domestic waste.However,current domestic waste incineration power plants often face challenges related to maintaining consistent steam production and high operational costs.This article capitalizes on the technical advantages of big data artificial intelligence,optimizing the power generation process of domestic waste incineration as the entry point,and adopts four main engine modules of Alibaba Cloud reinforcement learning algorithm engine,operating parameter prediction engine,anomaly recognition engine,and video visual recognition algorithm engine.The reinforcement learning algorithm extracts the operational parameters of each incinerator to obtain a control benchmark.Through the operating parameter prediction algorithm,prediction models for drum pressure,primary steam flow,NOx,SO2,and HCl are constructed to achieve short-term prediction of operational parameters,ultimately improving control performance.The anomaly recognition algorithm develops a thickness identification model for the material layer in the drying section,allowing for rapid and effective assessment of feed material thickness to ensure uniformity control.Meanwhile,the visual recognition algorithm identifies flame images and assesses the combustion status and location of the combustion fire line within the furnace.This real-time understanding of furnace flame combustion conditions guides adjustments to the grate and air volume.Integrating AI technology into the waste incineration sector empowers the environmental protection industry with the potential to leverage big data.This development holds practical significance in optimizing the harmless and resource-oriented treatment of urban domestic waste,reducing operational costs,and increasing efficiency.

Influence of flue gas cleaning system on characteristics of PM_(2.5)emission from coal-fired power plants

This study investigated the influence of precipitators and wet flue gas desulfurization equipment on characteristics of PM_(2.5)emission from coal-fired power stations.We measured size distribution and removal efficiencies,including hybrid electrostatic precipitator/bag filters(ESP/BAGs)which have rarely been studied.A bimodal distribution of particle concentrations was observed at the inlet of each precipitator.After the precipitators,particle concentrations were significantly reduced.Although a bimodal distribution was still observed,all peak positions shifted to the smaller end.The removal efficiencies of hybrid ESP/BAGs reached 99%for PM_(2.5),which is considerably higher than those for other types of precipitators.In particular,the influence of hybrid ESP/BAG operating conditions on the performance of dust removal was explored.The efficiency of hybrid ESP/BAGs decreased by 1.9%when the first electrostatic field was shut down.The concentrations and distributions of particulate matter were also measured in three coal-fired power plants before and after desulfurization devices.The results showed diverse removal efficiencies for different desulfurization towers.The reason for the difference requires further research.We estimated the influence of removal technology for particulate matter on total emissions in China.Substituting ESPs with hybrid ESP/BAGs could reduce the total emissions to 104.3 thousand tons,with 47.48 thousand tons of PM_(2.5).

Lignocellulosic biomass as sustainable feedstock and materials for power generation and energy storage

Lignocellulosic biomass has attracted great interest in recent years for energy production due to its renewability and carbon-neutral nature.There are various ways to convert lignocellulose to gaseous,liquid and solid fuels via thermochemical,chemical or biological approaches.Typical biomass derived fuels include syngas,bio-gas,bio-oil,bioethanol and biochar,all of which could be used as fuels for furnace,engine,turbine or fuel cells.Direct biomass fuel cells mediated by various electron carriers provide a new direction of lignocellulose conversion.Various metal and non-metal based carriers have been screened for mediating the electron transfer from biomass to oxygen thus generating electricity.The power density of direct biomass fuel cells can be over 100 mW cm^(-2),which shows promise for practical applications.Lignocellulose and its isolated components,primarily cellulose and lignin,have also been paid considerable attention as sustainable carbonaceous materials for preparation of electrodes for supercapacitors,lithium-ion batteries and lithium-sulfur batteries.In this paper,we have provided a state-of-the-art review on the research progress of lignocellulosic biomass as feedstock and materials for power generation and energy storage focusing on the chemistry aspects of the processes.It was recommended that process integration should be performed to reduce the cost for thermochemical and biological conversion of lignocellulose to biofuels,while efforts should be made to increase efficiency and improve the properties for biomass fuelled fuel cells and biomass derived electrodes for energy storage.

Progress and prospects of innovative coal-fired power plants within the energy internet

The development of electrical engineering and electronic, communications, smart power grid, and ultra-high voltage transmission technologies have driven the energy system revolution to the next generation: the energy internet. Progressive penetration of intermittent renewable energy sources into the energy system has led to unprecedented challenges to the currently wide use of coal-fired power generation technologies. Here, the applications and prospects of advanced coal-fired power generation technologies are analyzed. These technologies can be summarized into three categories:(1) large-scale and higher parameters coal-fired power generation technologies, including 620/650/700 oC ultra-supercritical thermal power and double reheat ultra-supercritical coal-fired power generation technologies;(2) system innovation and specific, highefficiency thermal cycles, which consist of renewable energy-aided coal-fired power generation technologies, a supercritical CO_2 Brayton cycle for coal-fired power plants, large-scale air-cooling coal-fired power plant technologies, and innovative layouts for waste heat utilization and enhanced energy cascade utilization;(3) coal-fired power generation combined with poly-generation technologies, which are represented by integrated gasification combined cycle(IGCC) and integrated gasification fuel cell(IGFC) technologies. Concerning the existing coal-fired power units, which are responsible for peak shaving, possible strategies for enhancing flexibility and operational stability are discussed. Furthermore, future trends for coal-fired power plants coupled with cyber-physical system(CPS) technologies are introduced. The development of advanced, coal-fired power generation technologies demonstrates the progress of science and is suitable for the sustainable development of human society.