System Power Generation Research Articles

A study of automatic output power control methods for stand-alone photovoltaic power generation systems

Abstract The conventional power generation system’s output power automatic control structure is generally unidirectional, and the control coverage is small, leading to the increase in the random error obtained in the final test. Therefore, the design and research of this article are proposed. According to the current control demand, the generation system shall be stabilized first, and the power compensation shall be calculated. The multi-step mode shall be adopted to improve the control coverage and design the multi-step automatic control structure. According to this, it is constructed, and the automatic control processing is realized using jump balance. The test results show that compared with the traditional DFIG-DRWT system power output automatic control method and the traditional three-phase asynchronous generation system power output automatic control method, the random error finally obtained by the designed independent photovoltaic power station output power automatic control method is relatively effective. This indicates that the application effect and coverage of the designed power automatic control method are high, that controllability has been significantly improved, and that control flexibility has also been strengthened.

Hydrocarbonoclastic Biofilm-Based Microbial Fuel Cells: Exploiting Biofilms at Water-Oil Interface for Renewable Energy and Wastewater Remediation.

Microbial fuel cells (MFCs) represent a promising technology for sustainable energy generation, which leverages the metabolic activities of microorganisms to convert organic substrates into electrical energy. In oil spill scenarios, hydrocarbonoclastic biofilms naturally form at the water-oil interface, creating a distinct environment for microbial activity. In this work, we engineered a novel MFC that harnesses these biofilms by strategically positioning the positive electrode at this critical junction, integrating the biofilm's natural properties into the MFC design. These biofilms, composed of specialized hydrocarbon-degrading bacteria, are vital in supporting electron transfer, significantly enhancing the system's power generation. Next-generation sequencing and scanning electron microscopy were used to characterize the microbial community, revealing a significant enrichment of hydrocarbonoclastic Gammaproteobacteria within the biofilm. Notably, key genera such as Paenalcaligenes, Providencia, and Pseudomonas were identified as dominant members, each contributing to the degradation of complex hydrocarbons and supporting the electrogenic activity of the MFCs. An electrochemical analysis demonstrated that the MFC achieved a stable power output of 51.5 μW under static conditions, with an internal resistance of about 1.05 kΩ. The system showed remarkable long-term stability, which maintained consistent performance over a 5-day testing period, with an average daily energy storage of approximately 216 mJ. Additionally, the MFC effectively recovered after deep discharge cycles, sustaining power output for up to 7.5 h before requiring a recovery period. Overall, the study indicates that MFCs based on hydrocarbonoclastic biofilms provide a dual-functionality system, combining renewable energy generation with environmental remediation, particularly in wastewater treatment. Despite lower power output compared to other hydrocarbon-degrading MFCs, the results highlight the potential of this technology for autonomous sensor networks and other low-power applications, which required sustainable energy sources. Moreover, the hydrocarbonoclastic biofilm-based MFC presented here offer significant potential as a biosensor for real-time monitoring of hydrocarbons and other contaminants in water. The biofilm's electrogenic properties enable the detection of organic compound degradation, positioning this system as ideal for environmental biosensing applications.

Rolling planning model for high proportion renewable energy generation power system considering frequency security constraints

In this paper, a rolling planning model for high proportion renewable energy generation power systems is proposed, considering frequency security constraints, to address the frequency stability challenges posed by increased integration of wind and solar energy into the power grid under the “double carbon” goal. First, this study establishes a frequency response model for a thermal generation unit (TGU) and analyzes the impact of the high proportion of renewable energy on system frequency stability. Subsequently, a dynamic frequency security constraint is formulated, which can be used in generation planning to address the frequency issues. Second, a bi-level rolling planning model for high proportion renewable energy generation power systems considering dynamic frequency security constraints is established. The upper level focuses on investment decisions. The lower level incorporates frequency security constraints into the operational simulation to guarantee the frequency stability of the power system when making decisions on the size of newly built TGUs annually. Finally, case studies are conducted to demonstrate the validity and effectiveness of the proposed rolling planning model.

The Information Security Issues of Distributed Economic Dispatch for New Generation Power Systems—Present Situation and Forecast

With the large scale and high proportion of distributed generation connected to the power grid, the distributed economic dispatch system has attracted more attention because of its significant advantages. However, the distributed economic dispatch system faces more serious information security issues due to the variation of communication topology. Therefore, the purpose of this paper is to review the information security issues that may occur in the distributed economic dispatch system and the defense measures. By summarizing the existing literature on information security issues and defense measures, it can be found that the current research focuses on the cyber-side defense for information security, and lacks consideration of the cyber-physical coupling characteristics. Additionally, the separate cyber-side defense measures still have a defense blind spot and cannot respond in a timely manner to the physical-side actions caused by information security issues. Finally, the establishment of the information security issues model and the construction of the integrated security defense system are discussed from the perspective of the power cyber-physical system. This research will be helpful in the construction of the security defense system for information issues in distributed economic dispatch systems.

Short-term stochastic multi-objective optimization scheduling of wind-solar-hydro hybrid system considering source-load uncertainties

Uncertainties in energy outputs (source side) and load side are simultaneously present and cannot be ignored in the actual operation of multi-energy systems. Adopting a reasonable uncertainty modeling method and understanding the impact of source-load uncertainties on optimization scheduling are key to formulating accurate and effective multi-energy scheduling plans. To address the uncertainties on both the source side and the load side in wind-solar-hydro hybrid systems, this paper proposes a multi-objective optimization scheduling model based on stochastic programming theory. The model aims to maximize the net profit of the system's power generation and minimize the fluctuation of the remaining load. It employs Vine-Copula coupled with Monte Carlo simulation and the deep learning method TimeGAN to generate joint wind and solar power output and load scenario sets. The generated source-load uncertainty scenarios are then reduced to representative scenarios using the K-Means clustering method, which are used as inputs for the scheduling model. The proposed model is applied to a wind-solar-hydro energy base in China, and the results show that: 1) The Vine-Copula-based source-side scenario generation method can quantitatively consider the correlations among meteorological factors. The relative errors of the generated scenarios' statistics compared to the original data are all less than 5%, and the relative errors of the correlation coefficients are less than 10%. 2) The TimeGAN-based load-side scenario generation method avoids the presupposition of the load probability distribution. Compared to the original data, the generated scenarios have R2 and Pearson correlation coefficients of 0.77 and 0.87, respectively. Additionally, TimeGAN shows significant advantages over traditional random sampling methods in simulating extreme scenarios. 3) Both source-side and load-side uncertainties significantly impact the optimization scheduling results of multi-energy systems, leading to increased fluctuation of the remaining load and decreased net profit. 4) The combined source-load uncertainties have a synergistic negative impact on the multi-objective optimization scheduling results. 5) The Pareto front of the optimization results is a concave function with low marginal benefits. Decision-makers should adopt a compromise solution as a guide for the operation of multi-energy systems.

Research on Optimization Design of Heliostat Field Based on Ray Tracing

In tower-type solar thermal power generation systems, the optical efficiency of the mirror field is the primary factor determining the overall system's power generation efficiency. In order to meet the requirement of a rated power of 60MW with different heliostat sizes and installation heights, optimize the various parameters of the heliostat field, establish a tower-type solar thermal power generation system model, and evaluate the model. Taking the maximum annual average output power per unit mirror area as the optimization objective, through simulation, it is found that the heliostat with a side length of 6 meters, a gradient design of installation height, a regular triangular layout, and the use of ray tracing technology can efficiently capture solar energy. The average optical efficiency, average cosine efficiency, average shadow blocking efficiency, average truncation efficiency, and average thermal power factor per unit area mirror are analyzed. The research results show that there is an optimal combination of absorber northward movement, heliostat size, equilateral triangle arrangement, installation height gradient design, and solar trajectory, which enables the annual average output thermal power to reach or even exceed the rated power of 60MW.This model and algorithm compared and analyzed genetic, greedy, and simulated annealing algorithms on the same dataset. The runtime is approximately 8-10 minutes, with CPU utilization around 60%.

On representation of energy storage in electricity planning models

This paper considers the representation of energy storage in electricity sector capacity planning models. The incorporation of storage in long-term systems models of this type is increasingly relevant as the costs of storage technologies, particularly batteries, and of complementary variable renewable technologies decline. To value energy storage technologies appropriately in optimization models, a representation of linkages between time periods is required, breaking classical temporal aggregation strategies that greatly improve computation time. Our paper reviews approaches to address the problem of compressing chronology for large-scale electricity planning models and provides a generalized conceptual model, conditions for lossless aggregation, and key principles to evaluate aggregation methods. We propose a novel approach, which we call the “expected value” method, to maintain key economic characteristics of energy storage, variable renewables, dispatchable generation, and other power system resources at a relatively low computational cost and conduct numerical experiments to compare its accuracy and computational performance with other temporal aggregation methods.

An Approach to Improve Power System Resiliency in Grid-Connected Wind Turbine Generator Power System

An Approach to Improve Power System Resiliency in Grid-Connected Wind Turbine Generator Power System

Co-optimization of power and water distribution systems for simultaneous hydropower generation and water pressure regulation

Water Distribution Systems (WDSs) are gravitational systems supplying water, mainly. Little research has been conducted on harnessing the great amount of energy that is currently dissipated during water pressure regulation, usually performed with “passive” devices like Pressure Reduction Valves (PRVs). This paper develops a co-optimization framework between Water and Power Distribution Systems (PDS) and introduces the concept of Integrated Power-Water Distribution System (IPWDS). Under this framework, PRVs are replaced by hydro-turbines, which form the interconnecting links between the two systems. They regulate pressure at the WDS while injecting the power resulting from their action to PDS. The complete WDS and PDS mathematical models are adopted and the detailed turbine design and performance model that accounts for turbines feasible operational range is considered. The suggested method optimizes the power generation of the hydro-turbines subject to the hydraulic restrictions and power flow constraints imposed by each network. Optimal Power Flow (OPF) has been extended in order to accommodate turbine’s model and constraints imposed by the varying daily flows of the WDS and PDS. Results taken under the suggested framework from a typical WDS, demonstrated that more than 393 MWh (approximately $40 K) of clean power could be produced per year. However, the violation of the unified set of constraints from the IPWDS may reduce the amount of renewable power generation by a significant 7%. This paper focuses mainly on the technical aspect of such an approach. Future work will focus on the economic assessment and viability of such joint projects, resulting from co-optimization and on social-technical aspects that aim on maximizing the penetration of turbines within existing PDS.

Using the MCDM Method Distributed Generation (DG) System

Distributed Generation (DG) system These two major categories of DG optimization methodologies are different from the components of the examined studies. Distributed generation (DG) power systems are the most popular technique for extending the power network to rural areas and, more recently, as a sustainable electrification technique The consequences of seasonal load variation and distributed hybrid system architecture without load shedding generation (TG) are explored in light of the dwindling availability of traditional fossil fuels, the fluctuating cost of fuel, and the decrease of environmental pollutants owing to increased demand. Numerous DGs connected to integrated power quality system conditioners. Today, a lot of distributed generation (DG) technology for renewable energy is interface-based. In grid-connected converters, these harmonic functions are taken into account by sensing control, enhancing converter versatility when local controllers use assessment techniques for harmonic distribution system adjustment. As a result, systems ought to implement common current-regulated and voltage-regulated DG harmonics correction functions. A wind solar hybrid system produces electricity by combining the two renewable energy sources, wind and sunlight. The system is made to produce electricity utilizing both modest wind generators and solar panels. The task of supplying the engine with fuel falls on the fuel system, which consists of a fuel tank, pump, filter, and injectors or a carburetor. Each part of the car needs to be faultless in order for it to function and be as dependable as anticipated. A photovoltaic (PV) system combines one or more solar panels with an inverter and other electrical and mechanical components to generate power from the sun. There are many different sizes available for PV systems, ranging from small rooftop or portable devices to massive utility-scale power plants. In isolated (cold or more temperate) places with no other electrical supply, PV offers a suitable energy source. Photovoltaic systems, for instance, can be used to power: water pipes, communications repeater stations, and more. The components of a typical system include a building sewer, a septic tank, a standard trench, a shallow trench, a chamber trench, a deep wall trench, and an absorbent bed for seepage pits. EDAS approach is proposed for their role category. The top advantage of EDAS compared to other methods for classification is that it has high accuracy performance and less mathematical calculations. In EDAS, each evaluation of substitutions appreciates size and a form standard solution introduces a durable EDAS technique for finding providers depending on the location of character substitution. Strong waste for disposal in site determination suggested a purely intuitive fuzzy model based on EDAS. In this study, EDAS was integrated into analyzer boundaries for RE development Application of EDAS technique in MCDM. First, a basic definition of projects and a distance method are briefly suggested. Next, the augmented EDAS approach is traditional under the real context inspired by the EDAS method. Results: The final result is done by using the EDAS method. Fuel system is highest Value and PV system is lowest value. resulting in Fuel system ranked first, there Fuel system has low rank.

Distributed Generation (DG) system using COPRAS method

Distributed Generation (DG) system. Distributed Generation (DG) power systems, Expansion of power systems in remote areas, and Very recently as a sustainable way of electrification popular. Depletion of conventional fossil fuels, Fuel price volatility, and emissions Awareness environment about reduction, Due to this its demand is increasing. DG systems have additional power quality challenges. There are many types of DG power sources that produce electrical power with different voltages at various frequencies. RES-Based Distributed Generation (DG) Systems are conventional energy systems In finding new modern solutions for planning have contributed Widespread use of DG is old and Defines new views, Current delivery system The only way to deal with the project Calling for more complex defenses. COPRAS (Complex Proportionality Assessment) is very Multiple criteria used in decision-making methods is one. This technique is different Decision making by researchers Used to solve problems. Investment cost, operating cost, Primary energy consumption, CO2 emissions. Energy storage system, fuel system, Wind Solar Hybrid System, Conventional System, PV energy storage system. Involvement of Distributed Generation (DG) system PV energy storage system ranked 1st, and the Conventional system also got 5th position.

Self-powered powerline monitor with strong anti-interference based on Maxwell’s displacement current-driven LED

A smart grid is a new generation power system, which realizes the interconnection of information in intelligent, efficient, and stable operation modes. Thus, high-voltage monitoring is an important part of ensuring efficient power transmission in a smart grid. However, traditional voltage monitors that rely on wired connections have limitations in meeting the adaptability requirement for smart grids. In this work, inspired by the double-TENG structure, we propose a self-powered high-voltage monitoring device with strong anti-interference, namely, the Maxwell’s displacement current-driven lighting emitting diode (MDC-LED), and proved that the MDC-LED with four electrodes can reorganize the electric field near the powerline, leading to an approximately uniform electric field. Therefore, the light output from the MDC-LED is highly sensitive to the electrical signal of the powerline and has a strong anti-device vibration. Because of the MDC-LED’s high sensitivity to alternating electric fields, it can detect the electric pollution in the powerline, as well as the powerline’s vibration frequency and amplitude. We believe the MDC-LED can provide a novel technical approach for the development of detection systems for use in smart grids.

PRINCIPLES OF FORMING THE OPTIMAL STRUCTURE OF THE ELECTRIC POWER SYSTEM WITH THE INTEGRATED USE OF DISTRIBUTED GENERATION SOURCES

Abstract. The article presents the results of developing the optimal configuration of a hybrid electric power system of distributed generation, taking into account the energy potential of alternative energy in Ukraine. The issues of further development of distributed energy based on the introduction of hybrid systems with renewable sources of distributed energy generation are considered. The potential of alternative energy technologies in Ukraine is analyzed. A comparison of the efficiency of implementation of different types of alternative energy sources by the method of hierarchy analysis is carried out in order to identify their level of prospects for use in complex systems of distributed generation. The general optimal structure of the electric power system of distributed generation is developed. A method for optimizing the composition of integrated distributed generation power systems with renewable energy sources based on expert analysis of energy efficiency and the potential for the development of modern alternative energy technologies in Ukraine is proposed. The application of the performed research will increase the reliability of distributed generation systems and their energy efficiency in the hybrid use of renewable energy sources, provided that the system control is fully automated and its structure is optimised.

Analysis of Grid-tied Solar Photovoltaic Energy Generation under Uncertain Atmospheric Conditions Using Adaptive Neuro-fuzzy Control System

The grid-tied photovoltaic (PV) power system has remained the most practical and sustainable configuration among renewable energy generation systems. Although uncertainties persist in solar irradiance and temperature, the grid-tied system faces transient instability issues during maximum power point tracking, adversely affecting power quality and resulting in substantial costs. To overcome this issue, we proposed analyzing the grid-tied system under uncertain atmospheric conditions based on an adaptive neuro-fuzzy control system (ANCS). This control scheme incorporates a hybrid learning algorithm and undergoes evaluation across various operating conditions. The obtained results demonstrate the effectiveness of the learning algorithm in maintaining a fast convergence speed. Consequently, this capability ensures the consistent preservation of sufficient power quality in the power system without any discernible transient impact. Furthermore, the investigation reveals the significant impact of solar radiation and temperature on the performance of the solar grid-tied PV system. Specifically, temperature alone contributes to over 15% power reduction when reaching 45 °C. As the temperature decreases to 5 °C at 1000 W/m2 irradiance, the ANCS influences an increase in the system's power generation from 100.72 kW at 25 °C to 103.01 kW.

Cloud Computing for Renewable Power Systems

Renewable energy systems are playing a main role in the future power systems. Therefore, the future electricity infrastructure must support the integration of the existing conventional power system with varies renewable energy resources, which are widely dispersed power sources. This interconnected power pools or grid needs a real-time monitoring, controlling and high computational power. There are many devices connected to the grid enable us to exchange information with power systems. This paper discussed the computation power and the performance of the cloud computing platform by implementing a lab demonstrator which is a load forecasting application in energy management field. Moreover, it announces an insight to the application of cloud computing in the next generation power systems.

Parameter evaluation for a hybrid marine system combining solid oxide fuel cells and overexpanded steam-injected gas turbine

The article focuses on the development of decarbonized marine power systems, consisting of solid oxide fuel cells (SOFC) and gas turbines (GT), aiming to provide high economic and environmental performance. Currently, the potential of such systems is not fully realized due to issues related to the insufficient reliability and durability of the fuel cells themselves, as well as suboptimal heat utilization processes in the gas turbine section of the power plant. In contrast to previous research, this study demonstrates the prospects of using the steam-injected gas turbine (STIG) cycle and overexpanded turbine to improve the heat recovery conditions of exhaust gasses from fuel cells, enhance the efficiency of the power system, and increase the longevity of its key components. To achieve this, a corresponding mathematical model was developed, and optimization calculations for the proposed system cycle were conducted. Adopting the proposed cycle with a steam-injected gas turbine resulted in a 4.4 % increase in efficiency compared to traditional schemes. Such a system could be utilized in the design of advanced marine power system for the next generation.

Research and analysis of the thermal and control characteristics of the plate-type fuel assembly for the supercritical CO2 reactor

Small nuclear reactors can be applied to micro grid power generation, island and space nuclear power systems. The plate-type fuel assembly has a compact design structure, low fuel core temperature, large heat exchange area, and high heat exchange efficiency, which is widely used in small nuclear reactors. To supplement the lack of research in S-CO2 small nuclear reactor, The Brayton cycle reactor system analysis program of S-CO2 plate-type fuel assemblies (BRESA-PFA) is developed, and the reactor control system program is developed to simulate the transient condition in this paper. This article establishes a fuel assembly flow and heat transfer model and a control rod control model using Modelica language, achieving physical and thermal coupling and power control of the reactor. The steady-state operating parameters of the developed program BRESA-PFA were studied, and the flow distribution, coolant and fuel temperature distribution of BRESA-PFA were obtained. The flow distribution conforms to the experimental parameters of CARR, and the fuel assembly radial temperature distribution is high at the center and low at the edge. After verification, the maximum fuel temperature did not exceed the limit value and met the design requirements. Transient characteristic analysis was conducted to study the rod lifting strategy during the startup process, the start-up and rod lifting process of BRESA-PFA is N2-N1-G2-G1, using the logic of step control. Research on reactor control system response under loss of coolant accident, after the accident, the coolant flow suddenly decreased to 65 % of the rated value, and the reactor power also decreased to 65 % FP, G2 and G1 control rods were inserted downwards, the coolant temperature fluctuates by 1 %∼2%. The reactor control system was able to respond quickly, ensuring the stability of coolant temperature, proving the safety and reliability of BRESA-PFA, and providing theoretical reference and scheme support for the research of S-CO2 small nuclear reactors.

Weakly Mixing Power System for Thermoelectric Generation of Electricity Using Engine Waste Heat in Vehicles

Weakly Mixing Power System for Thermoelectric Generation of Electricity Using Engine Waste Heat in Vehicles

Vulnerability analysis and modeling false data injection attack against voltage source converter in renewable generation power system

With the increasing penetration of renewable resources, more power electronic devices that need communication with control centers may bring a novel risk of cyber attacks. This paper investigates the vulnerability of the hierarchical control and proposes a false data injection attack (FDIA) constructing algorithm against voltage source converters. The attack can be accomplished via a physical attack generator or falsification via attacking supervisory control and data acquisition system. By developing the FDIA model against state estimation, the proposed attack model can circumvent bad data detection in the secondary control loop. The tests are carried out on a single converter infinite bus benchmark and an IEEE 34-bus system. The results show that the proposed attack model can mislead the system to produce threatening oscillation.

Islanding detection and classification of non-islanding disturbance in multi-distributed generation power system using deep neural networks

This paper proposes a statistical feature-based deep neural network (S-DNN) islanding detection technique and classification of non-islanding disturbances for hybrid systems with both synchronous and inverter-based distributed generators. The proposed technique extracts five statistical features from the point of common coupling (PCC)'s three-phase voltage. Several islanding and non-islanding scenarios for the test system were developed in the MATLAB environment. The S-DNN model is employed to effectively detect islanding and classify non-islanding disturbances using extracted features. In the detection phase of this study, the model is designed to identify events that occur in both islanded and non-islanding modes, providing the capability to accurately detect islanding conditions. In the classification phase, the model focuses specifically on non-islanding disturbances, such as line faults, load switching, and capacitor switching disturbances, enabling precise categorization of these distinct types of events. By leveraging the S-DNN model's capabilities, this study aims to enhance both islanding detection and non-islanding disturbance classification. By introducing white Gaussian noise with varying signal-to-noise ratios (30 dB and 40 dB) to the data, we aimed to evaluate the method's ability to handle and mitigate the effects of noisy and uncertain input signals. The inclusion of noise was intended to simulate realistic conditions where measurement inaccuracies or disturbances can be present. The decision tree (DT), multilayer perceptron (MLP), and support vector machine (SVM) are put up against the proposed technique, and it performs better than them in terms of accuracy, precision, recall, and F1 score.