Traditional Power Generation Research Articles

Multi-objective genetic algorithm-based wind turbines control

With the development of science and technology, new energy technology has increasingly become a hot spot of academic research. Today, solar, nuclear, tidal and wind power generation is gradually replacing thermal power generation. Aiming at the configuration problem of traditional wind power generation, this research will optimize the control of wind turbine generator sets, so as to realize the configuration optimization of the entire distribution network. In this study, a MOG (MOG) algorithm control optimization method was introduced. First, each scenario is constructed, and components such as centralized wind power plants are configured and controlled through the internal power flow program; then, decision variables are defined, and three objective functions with trade-off relationships are constructed. Finally, the MOG algorithm is used for iterative solution, and the point with the lowest violation cost is selected as the optimal solution. Simulation experiments including three different configuration scenarios were conducted in MATLAB environment for comparing the influence of different connection modes on distribution network. According to experimental results, the effectiveness of the proposed method is verified and it is proved that the proposed method can be applied to complex distribution networks.

Study on the application of photovoltaic power generation systems in field observatories under the zero carbon goal

Emissions of greenhouse gases such as CO2 are rapidly increasing, causing the greenhouse effect, leading to global warming and serious impacts on the ecosystem, while photovoltaic power generation can reduce the large amount of pollutants and carbon emissions produced in the traditional power generation process. The article takes the photovoltaic power generation system of the national field observatory as the research object, and uses PVsyst software to design the system of two distributed photovoltaic power generation systems. After calculating and analysing the total power of the experimental equipment, the efficiency of photovoltaic power generation and the system cost of the field observatory, the scheme and scale of the photovoltaic power generation system are determined, and the construction of the photovoltaic power generation test system is completed. Finally, through the comparison and analysis of the one-year PV power generation data with the simulated data, the feasibility of the system design method is verified, which can meet the requirements of the field experimental station and create a zero-carbon field observatory to achieve green energy conservation and sustainable energy use, which is in line with the strategic development plan of carbon peaking and carbon neutrality promoted by the state and provides a design basis and useful reference for the construction of zero-carbon field experimental stations.

Material Property Characterization and Parameter Estimation of Thermoelectric Generator by Using a Master–Slave Strategy Based on Metaheuristics Techniques

Thermoelectric generators (TEGs) have gained significant interest as a sustainable energy source, due to their ability to convert thermal energy into electrical energy through the Seebeck effect. However, the power output of TEGs is highly dependent on the thermoelectric material properties and operational conditions. Accurate modeling and parameter estimation are essential for optimizing and designing TEGs, as well as for integrating them into smart grids to meet fluctuating energy demands. This work examines the challenges of accurate modeling and parameter estimation of TEGs and explores various optimization metaheuristics techniques to find TEGs parameters in real applications from experimental conditions. The paper stresses the importance of determining the properties of TEGs with precision and using parameter estimation as a technique for determining the optimal values for parameters in a TEG mathematical model that represent the actual behavior of a thermoelectric module. This methodological approach can improve TEG performance and aid in efficient energy supply and demand management, thus reducing the reliance on traditional fossil fuel-based power generation.

Optimization of multi-temporal generation scheduling in power system under elevated renewable penetrations: A review

The traditional power generation mix and the geographical distribution of units have faced structural reform with the increasing renewables. The existing scheduling schemes confront the optimization challenges of multi-source collaborative and multi-temporal coordination. This paper reviews the optimization of generation scheduling in power systems with renewables integration in different time scales, which are medium- and long-term, short-term and real-time, respectively. First, the scheduling model and method are summarized. The connections and differences of the multi-source mathematic model with uncertainty, as well as the market mechanism, including thermal power, hydroelectric power, wind power, solar energy, and energy storage, are also indicated. Second, the scheduling algorithm and approach are sorted out from the two dimensions of certainty and uncertainty. The innovation and difference in algorithm between the traditional scheduling and the scheduling problem with renewables are presented. Meanwhile, the interaction and coupling relationship among the different time scales are pointed out in each section. The challenges and shortcomings of current research and references future directions are also provided for dispatchers.

Multi-Objective Optimization of Economy, Safety and Emissions for the Startup Scheduling of a Steam Turbine

Abstract With the rapid development of renewable energy, traditional power generation such as combined cycle unit needs to startup or change load quickly to balance the variability brought by renewable resources. In the combined cycle unit, the startup of steam turbine has a great influence on the whole set, and it is necessary to ensure safety, shorten the startup time, and reduce pollutant emissions. These optimization objectives are inter-related and have complex contradiction, which is the difficulty for multi-objective optimization. Costs function consisting of startup costs, fatigue life damage costs, pollutant emissions costs, and revenue gained from electricity is proposed to balance the contradiction more objectively. Finite exhaustive method (FEM) combining thermal-structural finite element calculation and multi-objective optimization is proposed for multi-objective startup process optimization, and its results are compared with those of minimum startup time and minimum fatigue life damage optimization. It can automatically search for a unique global optimal solution for engineering practice, rather than solution sets obtained by Pareto Optimality, which is beneficial for application in different combined cycle steam turbines and startup process. Multi-objective optimization scheduling shortens startup time from 105 to 93 min, reduces maximum stress from 493 MPa to 440 MPa, and reduces costs function by 66.6%. The comparison with the multi-objective optimization results of the response surface method (RSM) proves the reliability and validity of this method. The practical inspection results prove that the optimal scheduling is safe and effective.

Development Status and Carbon Dioxide Emission Forecast of Power Industry in Guangxi

This paper uses the Long-range Energy Alternatives Planning(leap) system model to predict the carbon dioxide emissions of the power industry and calculate the carbon peak platform period of the power industry in Guangxi, which is based on the development status of the power industry and the energy and power development plan in Guangxi during the "14th Five-Year Plan". The results show that the power structure of Guangxi is at an excellent level in the country. In 2021, the installed proportion of clean energy is 55.1% (the national average level of 44.7% in 2020), and the proportion of thermal power generation is 55.7% (the national average level of 68.5% in 2020). With the strong economic recovery , the demand for electricity in Guangxi will continue to increase. By 2025 and 2030, the electricity consumption of the whole society will reach 2.9 ×1012 kW•h and 3.7×1012 kW•h, respectively, with an average annual growth rate of about 7.1% and 5.3%. The installed power generation capacity will reach 9.4 ×107 kW and 1.5 ×108 kW, respectively. The power generation will reach 3.0 ×1012 kW•h and 4.2×1012 kW•h electricity, respectively. However, Due to the influence of resource endowment, meteorological conditions, energy storage technology and other constraints on non-fossil energy power generation, it is difficult to achieve large-scale replacement of traditional energy power generation in a short term. It is estimated that coal power production will account for 45% and 30% of the total power generation respectively in Guangxi by 2025 and 2030. Coal power will continue to be the main provider of power security for a long time in the future. Therefore, according to the current situation of power development planning, it is estimated that the carbon emission of Guangxi will reach 9.2×107 t by 2030. The carbon peak platform period of power generation industry will appear in 2029 to 2032, with a peak value of 1.1 × 108 t.

Comparison and Analysis of Three Different Power Generation Methods under the Context of Dual Carbon Goals

Traditional thermal power generation is characterized by mature technology, wide application, and high carbon emissions. Under the context of dual carbon goals, there is an urgent need for technological innovation in decarbonization. This paper aims to systematically analyze and compare three power generation methods, namely coal-fired power generation by steam turbines, power generation by natural gas turbines and fuel cell power generation. An outlook for future power generation technologies is presented at the end.

Performance investigation of double input enhanced converter for hybrid renewable energy

Nowadays, power shortage turned into a massive issue in numerous countries because of increasing load demand, which cannot be solved by traditional energy power generation. These problematic circumstances motivate analysts to extract the most extreme electric power by concentrating on non-conventional energy sources. In order to extract the electric power, new extendable multi-input DC-DC converters are utilized to maximize the Power Conversion efficiency. The efficient power conversion is accomplished with minimum voltage stress across the semiconductor devices. This topology gives maximum voltage gains by expanding the quantity of input sources, thereby being appropriate for various requirements from minimum to maximum voltage/power in Hybrid Energy Systems (HES). This paper evaluates the present and the future trend of non-isolated DC-DC converters with different parameters investigated utilizing MATLAB Software. Because of the simulation result, the performances of various non-isolated converters are assessed and help to establish the appropriate converter with a specific power rating for hybrid renewable energy-based requirements.

Optimization Scheduling Method of Solar Photovoltaic and Fuel Cell Combined Power Generation System

The performance of the battery used in the traditional solar photovoltaic power generation system is poor, and the solar energy has a certain volatility, which makes the performance of the solar photovoltaic power generation system decline significantly. In order to improve the performance of the solar photovoltaic power generation system, a solar photovoltaic fuel cell combined power generation system has been developed. However, there are some problems in the process of traditional combined generation system optimal scheduling, such as high investment cost and total cost, and short generation time of optimal scheduling scheme. This paper takes solving the problems of traditional system optimal scheduling as the research goal, a new optimization scheduling method of solar photovoltaic and fuel cell combined power generation system was designed. The composition and topological structure of the solar photovoltaic fuel cell combined power generation system are analyzed. The system is composed of photovoltaic array, fuel cell, electrolytic cell, short-term energy storage unit and energy control unit. It can mainly convert sunlight radiation into electric energy, and convert it into DC or AC used by people through multiple links, so as to ensure the stability and security of power supply in our country. A scheduling model is established according to the photovoltaic cell power generation model, the fuel cell power generation model, the electrolytic hydrogen production model, the battery model, the power conversion model and the combined power generation system model. And the multi-objective cuckoo algorithm is used to solve the model, the optimization scheduling results of solar photovoltaic fuel cell combined power generation system are obtained. The experimental results show that the total investment cost of this method is reduced by 123678.4 yuan and 175858.7 yuan compared with the experimental comparison method, and the total cost is reduced by 301195.5 yuan and 414991.8 yuan compared with the experimental comparison method. It shows that compared with the experimental comparison method, the total investment cost and total cost of this method are lower, and the generation time of the optimal scheduling scheme is between 0.19s and 0.25s, and the practical application effect is good. It fully solves the problems existing in the traditional methods and has certain application significance.

The Impact of Tax Policy on Performance of Green Energy in Power Industry

This paper estimates the impact of industrial policy on firms performance. We exploit the VAT preferential policies for enterprises in the power industry issued by the Chinese government since 2013. We find that, compared with traditional power generation technology-oriented enterprises, the enterprises in the power industry dominated by green energy technology improved performance after the launch of the tax incentive policy, which is reflected in significant increase in the gross profits and net profits. The findings of this paper suggest that tax incentives are helpful to reduce the burden of green energy enterprises, and shed light on the industrial structure transformation of power industry enterprises.

Enhanced Dynamic Performance in Hybrid Power System Using a Designed ALTS-PFPNN Controller

The large-scale, nonlinear and uncertain factors of hybrid power systems (HPS) have always been difficult problems in dynamic stability control. This research mainly focuses on the dynamic and transient stability performance of large HPS under various operating conditions. In addition to the traditional synchronous power generator, wind-driven generator and ocean wave generator, the hybrid system also adds battery energy storage system and unified power flow controller (UPFC), making the system more diversified and more consistent with the current actual operation mode of the complex power grid. The purpose of this study is to propose an adaptive least squares Petri fuzzy probabilistic neural network (ALTS-PFPNN) for UPFC installed in the power grid to enhance the behavior of HPS operation. The proposed scheme improves the active power adjustment and dynamic performance of the integrated wave power generation and offshore wind system under a large range of operating conditions. Through various case studies, the practicability and robustness of ALTS-PFPNN method are verifying it by comparison and analysis with the damping controller based on the designed proportional integral differential (PID) and the control scheme without UPFC. Time-domain simulations were performed using Matlab-Simulink to validate the optimal damping behavior and efficiency of the suggested scheme under various disturbance conditions.

Energy and Exergy Analysis on a Blast Furnace Gas-Driven Cascade Power Cycle

Blast furnace gas is the major combustible by-product produced in the steel industry, where iron ore is reduced by coke into iron. Direct combustion of blast furnace gas after simple treatment for power generation is a common utilization method nowadays. However, this method suffers from low efficiency and high carbon intensity. The use of gas-steam combined cycle is an excellent method to improve the efficiency of blast furnace gas for power generation. However, there is a problem of insufficient utilization of low product heat, and the addition of CCS system can further reduce the power efficiency. To solve these issues, a new blast furnace gas power generation system with a Brayton cycle with supercritical CO2 and a Rankine cycle with transcritical CO2 is proposed in this work. The new system is then thermodynamically simulated by Aspen Plus, after the sub-modules are validated. The effects of molar ratio of steam to carbon, selexol/CO2 mass ratio, compression ratio, turbine import temperature and turbine inlet pressure on the system are investigated. A comparison is also performed between the new combined cycle system and the traditional combined cycle power generation system. The results show that in the new power generation system, net power efficiency of 53.29%, carbon capture efficiency of 95.78% and sulfur capture rate of 94.46% can be achieved, which is significantly better than the performance of the conventional combined cycle.

A Hybrid Algorithm Based on Simplified Swarm Optimization for Multi-Objective Optimizing on Combined Cooling, Heating and Power System

Energy demand is rising sharply due to the technological development and progress of modern times. Neverthless, traditional thermal power generation has several diadvantages including its low energy usage and emitting a lot of polluting gases, resulting in the energy depletion crisis and the increasingly serious greenhouse effect. In response to environmental issues and energy depletion, the Combined Cooling, Heating and Power system (CCHP) combined with the power-generation system of renewable energy, which this work studied, has the advantages of high energy usage and low environmental pollution compared with traditional thermal power generation, and has been gradually promoted in recent years. This system needs to cooperate with the instability of renewable energy and the dispatch of the energy-saving system; the optimization of the system has been researched recently for this purpose. This study took Xikou village, Lieyu township, Kinmen county, Taiwan as the experimental region to solve the optimization problem of CCHP combined with renewable energy and aimed to optimize the multi-objective system including minimizing the operation cost, minimizing the carbon emissions, and maximizing the energy utilization rate. This study converted the original multi-objective optimization problem into a single-objective optimization problem by using the Technique for Order Preference by Similarity to and Ideal Solution (TOPSIS) approach. In addition, a hybrid of the simplified swarm optimization (SSO) and differential evolution (DE) algorithm, called SSO-DE, was proposed in this research to solve the studied problem. SSO-DE is based on SSO as the core of the algorithm and is combined with DE as the local search strategy. The contributions and innovations of the manuscript are clarified as follows: 1. a larger scale of CCHP was studied; 2. the parallel connection of the mains, allowing the exchange of power with the main grid, was considered; 3. the TOPSIS was adopted in this study to convert the original multi-objective optimization problem into a single-objective optimization problem; and 4. the hybrid of the DE algorithm with the improved SSO algorithm was adopted to improve the efficiency of the solution. The proposed SSO-DE in this study has an excellent ability to solve the optimization problem of CCHP combined with renewable energy according to the Friedman test of experimental results obtained by the proposed SSO-DE compared with POS-DE, iSSO-DE, and ABC-DE. In addition, SSO-DE had the lowest running time compared with POS-DE, iSSO-DE, and ABC-DE in all experiments.

Bidding Strategy of Two-Layer Optimization Model for Electricity Market Considering Renewable Energy Based on Deep Reinforcement Learning

In the future, the large-scale participation of renewable energy in electricity market bidding is an inevitable trend. In order to describe the Nash equilibrium effect and market power between renewable energy and traditional power generators in the tacit competition in the electricity market, a bidding strategy based on deep reinforcement learning is proposed. The strategy is divided into two layers; the inner layer is the electricity market clearing model, and the outer layer is the deep reinforcement learning optimization algorithm. Taking the equilibrium supply function as the clearing model of the electricity market, considering the green certificate trading mechanism and the carbon emission mechanism, and taking the maximization of social welfare as the objective function, the optimal bidding on the best electricity price is solved. Finally, the calculation examples of the 3-node system and the 30-node system show that compared with other algorithms, more stable convergence results can be obtained, the Nash equilibrium in game theory can be reached, social welfare can be maximized, renewable energy has more market power in the market. The market efficiency evaluation index is introduced to analyze the market efficiency of the two case systems. The final result is one of great significance and value to the reasonable electricity price declaration, the optimization of market resources, and the policy orientation of the electricity market with renewable energy.

Short-Term Prediction Method of Solar Photovoltaic Power Generation Based on Machine Learning in Smart Grid

In order to improve the accuracy of ultra short-term power prediction of the photovoltaic power generation system, a short-term photovoltaic power prediction method based on an adaptive k-means and Gru machine learning model is proposed. This method first introduces the construction process of the model and then builds a short-term photovoltaic power generation prediction model based on an adaptive k-means and Gru machine learning models. Then, the network structure and key parameters are determined through experiments, and the initial training set of the prediction model is selected according to the short-term photovoltaic power generation characteristics. And the adaptive k-means is used to cluster the initial training set and the photovoltaic power on the forecast day. The Gru model is trained on the initial training set data of each category, and the generated power is predicted in combination with the trained Gru model. Finally, considering three typical weather types, the proposed method is used for simulation analysis and compared with the other three traditional photovoltaic power generation single prediction models. The comparison results show that the proposed short-term photovoltaic power generation prediction method based on an adaptive k-means and Gru network has better effect, better robustness, and less error.

Study on the necessity and strategy of popularization of "near-zero emission" technology for thermal power generation.

With the increasingly severe environmental problems, cleaner production has become one of the necessary means to alleviate the problem. The "near-zero emission" technology of thermal power generation is an active attempt of traditional power generation enterprises to further improve the level of clean coal utilization and promote the flue gas emission from conventional management to lean management. The analysis and evaluation of "near-zero emission" technology of thermal power generation in China are conducive to enhancing the scientific understanding of the overall situation of air pollution in China and thus further analyzing the current situation and future development path of thermal power emissions. In this paper, the intelligent prediction algorithm is used to predict and evaluate the emission scale of soot, sulfur dioxide, nitrogen oxides, and other air pollutants in China and the thermal power industry in the future, which reflects the urgency of the promotion of "near-zero emission" of thermal power from the macro level. In addition, based on Evolutionary Game Theory (EGT) and coevolutionary algorithm, the strategy selection of several virtual power generation groups under different policy conditions is simulated around the promotion of "near-zero emission" of thermal power. The results show that the policy subsidy plays an important role in promoting technology popularization.

Influence of LiBr concentration in the generation of superheated vapor for a Hygroscopic Cycle

The novel proprietary Hygroscopic Cycle Technology (HCT) is a power cycle distinguished for using hygroscopic compoundswater mixtures as the working fluid to optimize the condensation process of the turbine exhaust steam by absorption phenomena. Previous works focused on the performance of the condensation/cooling section of the cycle as well as in the main improvements achieved by HCT in comparison to traditional thermal power generation cycles in terms of efficiency and profitability. Nevertheless, the thermodynamic characteristics of the steam generated from hygroscopic-water mixtures in the HCT boiler are yet to be studied. In this article, a theoretical analysis of temperature and pressure conditions of pure water steam generated from different concentrations of LiBr-water mixtures is carried out by using the Engineering Equation Solver (EES) software. Results show that LiBr-H2O mixtures display higher saturation temperatures than pure water, which enables the cycle to directly generate superheated vapor with no need for superheating equipment inside the boiler. This fact further improves the economic advantages previously shown by HCT, since erection, operation and maintenance costs related to the superheating process are avoided.

Model parameters identification of the PEMFCs using an improved design of Crow Search Algorithm

There is an increasing trend for fuel cell systems applications in electricity generation systems instead of traditional power generation systems because of their advantages such as high efficiency and almost no environmental pollution, desirable dynamic response, and reliability. Due to this reason, herein, a new method has been presented for optimum identification of the model of the proton exchange membrane fuel cell (PEMFC) model. The major concept is to lessen the sum of squared error (SSE) amount of the observed output voltage and the output voltage of the PEMFC stack by an improved version of Crow Search optimizer (ICSO). To validate the suggested technique, it is applied to two studied cases and the achievements are put in comparison with several newest optimizers, which are Genetic algorithm (GA), Grasshopper Optimizer (GHO), and Salp Swarm Optimizer (SSO). The achievements show that the suggested ICSO gives a better superiority to the other comparative algorithms for optimum estimation of the PEMFC model.

Optimal Dispatching of New Energy Power Grid with High Penetration Rate under the Background of Big Data

After decades of development, electricity has become an important driving force for national economic construction, and countries’ dependence on electricity is also increasing. In order to solve the problems of environmental pollution and nonrenewable resources in the process of traditional energy power generation and further improve the utilization efficiency of electricity, countries have vigorously developed new energy grids. As a rapidly advancing developing country, my country has a huge demand for electricity. In addition, in recent years, my country’s new energy power generation technology has advanced by leaps and bounds, and it has become an extremely important source of electricity, which is necessary. The purpose of this study is to improve the power grid dispatching and facilitate the people through the understanding of new energy sources with high penetration rate. This paper mainly uses the experimental method, the comparison method, and the investigation method and uses the big data technology to analyze the use of new energy and the optimal dispatch of the new energy grid. The experimental data show that the load changes in each time period are within 1. Through the continuous testing and optimization of the objective function, the dispatching of the high-penetration new energy grid has initially achieved scientific overall planning. The development of this research will help to realize the optimal configuration between traditional energy power generation and new energy power generation and realize the sustainable development of new energy power generation while reducing the loss of grid connection.

Asynchronous and Decoupled HIL Simulation of a DC Nanogrid

In this paper, an asynchronous and decoupled Hardware-In-the-Loop simulation of a DC nanogrid is presented. The DC nanogrid is a recent way to solve problems presented in traditional power generation, such as low efficiency, pollution, and cost increase. The complexity of this kind of system is high due to the interconnection of all the composing elements, making the use of HIL simulation attractive due to its advantages regarding computational power and low solution time. However, when a nanogrid is simulated in commercial and personalized platforms, all the elements presented are solved at the same integration time, even if some elements could be solved at smaller integration times, causing a slowdown of the system solution. The results of the asynchronous HIL simulation are compared with a synchronous HIL simulation with an integration time of 425 ns, and also with an offline simulation performed in PSIM software. The proposal achieves an integration time of 200 ns for the fastest element and 425 ns for the slowest, with an error of less than 0.2 A for current signals and less than 2 V for voltage signals. These results prove that the asynchronous and decoupled solution of an HIL simulation for nanogrid is possible, allowing each element to be solved as fast as possible without affecting the accuracy of the result, as well as simplifying programming.