Power Plant Management Research Articles

Adaptive multi-agent reinforcement learning for dynamic pricing and distributed energy management in virtual power plant networks

This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant (VPP) networks using multi-agent reinforcement learning (MARL). As the energy landscape evolves towards greater decentralization and renewable integration, traditional optimization methods struggle to address the inherent complexities and uncertainties. Our proposed MARL framework enables adaptive, decentralized decision-making for both the distribution system operator (DSO) and individual VPPs, optimizing economic efficiency while maintaining grid stability. We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay. Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods, including Stackelberg game models and model predictive control, achieving an 18.73% reduction in costs and a 22.46% increase in VPP profits. The MARL framework shows particular strength in scenarios with high renewable energy penetration, where it improves system performance by 11.95% compared with traditional methods. Furthermore, our approach demonstrates superior adaptability to unexpected events and mis-predictions, highlighting its potential for real-world implementation.

Hardware and Software Complex for Power Plant Management

Today, the issues of transferring private households to independent energy sources, including solar panels and solar collectors, are relevant. The article examines software options for hardware systems that can be used to manage energy supply systems for private homes. Technologies for using independent energy sources in the energy consumption system are presented, as well as the mathematical model for their optimization for remote country houses.

Advancing photovoltaic panel temperature forecasting: A comparative study of numerical simulation and machine learning in two types of PV power plant

Photovoltaic (PV) panel temperature dynamic monitoring and forecasting is important for managing and maintaining of PV power plant. However, it is uncommon to use a variety of methods to predict and evaluate the panel temperature of different types of PV power plants. Therefore, this study aims to advance PV panel temperature forecasting through a comparative analysis of numerical simulation and machine learning models in two types of PV power plants: land- and water-mounted. The results indicate that PV panel temperature condition for two types of PV power plants can be well captured by the numerical simulation (NS) and machine learning, except for the NS in water-mounted PV power plant (R2 with 0.66). Models perform better in land-mounted PV power plants, with Random Forest Regression (RFR) and ResNet models demonstrating superior accuracy across both environments. While all models exhibit some degree of deviation from normal distribution in residuals, RFR and ResNet show the least deviation and lowest prediction errors, highlighting their robustness in forecasting PV panel temperatures. This study can provide valuable insights for predicting PV panel temperatures and for the management and maintenance of PV power plants.

Dynamic Pricing and Energy Management of Virtual Power Plant Based on Source-Charge Uncertainty

With the intermittent, random and volatile distributed renewable energy mass influx into the grid and power market, the power system will face greater challenges. As a small energy autonomous system that integrates distributed generation, energy storage and load, virtual power plant connects the power market and end users, and it is also an important subject of power market reform. Its operating efficiency directly determines the effectiveness of market reform. However, virtual power plant faces the market risk of double uncertainty of internal renewable energy output and load demand during operation, so it needs to optimize the market behavior to protect its own interests. Therefore, the paper takes virtual power plant as the research object, and discusses the dynamic pricing strategy of virtual power plant considering the double uncertainty of source and charge and real-time market linkage from the aspects of theoretical analysis, modeling construction and simulation analysis, to provide reference for decision pricing of virtual power plant and formulation of electricity market reform policy.

Virtual power plant management with hybrid energy storage system

The transition to renewable energy sources and distributed energy generation (DG) has spurred the global evolution of energy production methods. However, virtual power plants (VPPs) face challenges due to fluctuations in renewable energy sources (RES) production, such as those from photovoltaics and wind turbines. Factors like temperature, solar radiation, wind speed, and high-frequency interference contribute to unstable output power, potentially affecting power supply quality with voltage fluctuations and frequency changes. To address these challenges, it is crucial to smooth alternating current before grid transmission.This paper proposes a solution involving a smart grid with decentralized generators and controllable loads forming a VPP. The approach introduces a Hybrid Energy Storage System (HESS) comprising batteries, supercapacitors, and fuel cells. Equipped with proportional-integral (PI) and model predictive control (MPC) regulators, the HESS aims to regulate inverter voltage for renewable energy. By converting fluctuating electricity into high-quality power, the system enables seamless integration into the VPP, thereby preventing disruptions in generation processes and reducing potential costs associated with damage caused by power fluctuations to grid-connected devices.In the context of the HESS, a photovoltaic system and a wind turbine have been developed, with the proposed system connected to an RLC series load through an IGBT inverter. To evaluate the effectiveness of the HESS within the proposed VPP, two different scenarios were examined by varying the location of these systems in a 14-bus microgrid.

Real-time detection and discrimination of radioactive gas mixtures using nanoporous inorganic scintillators

The nuclear industry’s expansion to encompass carbon-free electricity generation from small modular reactors and nuclear fuel reprocessing necessitates enhanced detection and monitoring of pure beta-emitting radioactive elements such as 3H and 85Kr; this endeavour is crucial for nuclear safety authorities tasked with environmental monitoring. However, the short range of electrons emitted by these gases makes detection challenging. Current methods, such as ionization chambers and liquid scintillation, do not offer at the same time good sensitivity, real-time analysis and ease of implementation. We demonstrate an approach using a gas–solid mixture to overcome these limitations. We synthetized a transparent and scintillating nanoporous material, an aerogel of Y3Al5O12:Ce4+, and achieved real-time detection with an efficiency of 96% for 85Kr and 18% for 3H. The method reaches a sensitivity below 100 mBq per cm3 over 100 s measurement time. We are able to measure simultaneously as mixtures containing both 3H and 85Kr a capability not possible previously. Our results demonstrate a compact and robust detection system for inline measurement of strategic radioactive gases. This combination of concept and method enhances nuclear power plant management and contributes to environmental safeguarding. Beyond the detection issues, this concept opens a wide field of new methods for radionuclide metrology.

Photovoltaics rooftop regulations and their connection to pro-environment consumer behavior in Indonesia

To address environmental issues, Indonesia aims to achieve a 23% renewable energy share by 2025 and 31% by 2050, according to the National Energy Plan. This article examines the regulations governing the management of rooftop solar power plant (rooftop-PV) in Indonesia and their connection to pro-environment consumer behavior. Despite Indonesia’s ambitious targets for Renewable Energy (RE) growth, progress in the field still needs improvement. Pro-environmental behavior (PEB) within the community is crucial for adopting rooftop PV as a more environmentally friendly energy alternative. This research employs a qualitative descriptive methodology with content analysis to evaluate the current situation. The research findings indicate that rooftop PV offers economic benefits through cost savings for consumers and is supported by existing regulations. However, some businesses feel that the government needs to expedite revising relevant regulations to address investment stagnation and installed solar panel capacity. Concerns arise that proposed policy changes may hinder the growth of rooftop PV businesses and the achievement of national capacity targets. The role of pro-environment consumer behavior, which has the potential to drive rooftop PV adoption, will be influenced by psychological and social factors, as well as changes in applicable regulations.

Predictive modeling of combined cycle power plant performance using a digital twin-based neural ODE approach

This study presents a novel digital twin-based predictive modeling approach to enhance the operation, maintenance, and management of combined cycle power plants. The research aimed to accurately forecast the combined cycle power (CCP) output, a critical performance indicator, to support intelligent decision-making for plant operators and engineers. The proposed framework leverages a neural ordinary differential equation (NODE) model integrated within a digital twin architecture to capture the complex, nonlinear dynamics of combined cycle gas turbine unit (CCGU) system. The predictive model was trained and validated using multivariate time-series data collected from a CCGU, achieving high accuracy with an R2_score of 0.993, a mean absolute percentage error of 0.325 %, and a root mean squared error of 1.518. Importantly, the NODE model demonstrated superior forecasting capabilities compared to traditional machine learning techniques. The digital twin (DT) concept enables seamless real-time data integration, physics-based models, and advanced data analytics to facilitate comprehensive CCGU lifecycle management. This novel approach provides operators with reliable, real-time insights to optimize plant performance, reduce maintenance costs, and improve overall sustainability. The generalization of the NODE model to an independent CCGU unit validated its applicability across diverse power plant configurations, highlighting the originality and practical value of the research. The key contribution of this work is the development of a digital twin-based predictive modeling framework centered on the innovative use of neural ordinary differential equations to enhance the operation and maintenance of critical energy infrastructure. This research advances the state-of-the-art in intelligent asset management for the built environment.

Simultaneous sizing and energy management of multi-energy Virtual Power Plants operating in regulated energy markets

This research analyses the case of a Virtual Power Plant (VPP) in regulated electricity markets, trading energy with the consumers and the grid under a Power Purchase Agreement (PPA). The VPP propagates the deployment of solar PVs while balancing its intermittency with a dispatchable power plant, which is assumed in this research to be a CCHP, supplying cooling, heating, and power. The VPP also integrates energy storage systems for a comprehensive assessment. Traditionally, the VPP concept has not been introduced in regulated markets, but it is widely researched in deregulated markets where VPPs trade energy with the electricity grid for profit maximisation. In regulated markets, a special architecture is proposed for a VPP that mediates between residential compounds and electricity grids for profit maximization and energy demand coverage, thus converting the compound into a power generator with minimum dependence on the grid for its energy demand. In the literature on aggregated energy systems in regulated markets, it is usually overlooked to perform detailed energy modelling and optimisation on an hourly level. Only basic rule-based frameworks for energy management are proposed. In this research, it is initially assumed that since the VPP integrates multi-energy components supplying heating, cooling and electricity, optimization of the output of each component for a common profit maximization, is necessary. However, in VPP-related literature, the capacity of each component, which is a main input for energy modelling, is traditionally assumed and not assessed. Therefore, the research aims to explore how to find the optimal capacity configuration of the residential VPP that achieves optimal profit. The paper analyses an iterative exhaustive search framework, integrating the 2-levels of energy optimisation (hourly profit maximisation objective) and capacities optimisation (Life cycle CAPEX & OPEX minimisation). Compared to baseline cases, where only energy optimisation is performed, and capacities are assumed and not assessed in terms of capital investment, the proposed framework achieved a higher annual profit by 3.1 % and a payback period of 11 years. The results also provide comprehensive 3D charts drawing the relations between the achieved profit against capacities configurations, thus allowing high-level decision-making. The results also prove the hypothesis that hourly energy optimisation should not be performed without investment cost assessment and that targeting the minimization of investment costs will indirectly benefit the achieved profit.

Decarbonization management of solvent-storage carbon capture power plant

Decarbonization management of solvent-storage carbon capture power plant

Distributed differentially private energy management of virtual power plants

Prosumers with flexible distributed energy resources (DERs) can be aggregated as a virtual power plant (VPP) to participate in the electricity market. However, the VPP’s energy management requires the prosumers to share individual data, which poses privacy concerns. This paper proposes a distributed differentially private energy management strategy for the VPP to maximize its profit by coordinating the prosumers and at the same time mitigate the privacy risks of local prosumers. Specifically, the coordination of prosumers is first formulated as an optimization problem, which includes the operation constraints of each individual prosumer and the VPP coordination process. On this basis, this paper proposes to solve the optimization problem with a two-level privacy protection strategy. For the individual level, a distributed solution framework is proposed to keep the private information of each prosumer preserved locally. For the communication level, a differential privacy mechanism is integrated into the information exchange process thus reducing the privacy leakage risk. Both theoretical and practical results are provided to verify the performance of the proposed method in terms of optimality and privacy protection levels.

Environmental risk assessment and management of nuclear power plants based on big data analysis

In traditional research, monitoring data and samples are limited, and it is difficult to achieve ideal results in real-time monitoring and rapid response to environmental risks. By leveraging extensive environmental data gathered from nuclear power plants, the research employed machine learning methodologies for accurate feature selection and extraction of environmental parameters. An efficient environmental risk assessment model was successfully established by using a random forest algorithm. The 95% confidence interval for the area under the curve value spanned from 0.6894 to 0.9292. This provided a more dynamic and effective means for assessing and managing the environmental risks of nuclear power plants.

A Study on One-Time Inspection for Aging Management of Operating Nuclear Power Plants

One-time inspection is used to confirm the effectiveness of nuclear power plant aging management programs such as water chemistry, fuel oil chemistry, and selective leaching program that is designed to prevent or minimize aging degradation during the plant operation. This paper represents one-time inspection experience of selected components and susceptible locations within the scope of water chemistry and fuel oil chemistry aging management programs. Prior to performing one-time inspection, sample sizes of components to be inspected were determined based on combination of materials, environment and aging effect. Examination techniques and relevant operating experience were analysed to obtain technical basis for further implementation of one-time inspection aging management programs. Existing correlation curves between sample size and total population number were reviewed to prepare further inspections for entire operating plants and ensure safety level of components subject to one-time inspection program.

Heat Transfer Management of Solar Power Plant for Dryer

Heat Transfer Management of Solar Power Plant for Dryer

Assessing Solar Radiation Forecasting: a WRF-Solar Model Evaluation in Kupang, Indonesia

In response to the challenges faced by solar power plants in Kupang, Indonesia – particularly during the peak of the rainy season – this study aims to enhance the WRF-Solar (Weather Research and Forecasting–Solar) numerical weather prediction model. Accurate short-term solar radiation forecasting is crucial for managing electricity supply disruptions and controlling operational costs in the City of Kupang and its surroundings. The unpredictable weather patterns – characterized by intense cloud activity during these seasons – pose significant challenges for reliable solar energy generation, thereby making precise weather modeling an essential tool for effective power plant management. Spanning 2020–2022, the research focused on the peak rainy seasons, utilizing a specifically configured WRF-Solar model to simulate solar radiation. Validation of the WRF-Solar model using observational data from the Automatic Solar Radiation Stations at the Kupang Climatological Station showed correlation values consistently above 0.50. However, the model exhibited a tendency to overestimate radiation intensity for GHI and DNI parameters (correlation 0.78 and 0.54) and underestimate for DHI (correlation 0.80), with RMSE values ranging from 47–320 W/m² and MBE values from –41.84 to 116.34 W/m². The model's accuracy was notably higher under clear conditions, with lower RMSE and MBE values but decreased significantly under overcast conditions, hence indicating sensitivity to cloud cover. This study highlights the WRF-Solar model's capability in capturing the diurnal pattern and hourly fluctuations of solar radiation while also underscoring the need for improved cloud representation to enhance accuracy under diverse climatic conditions.

Study on Fault Monitoring Technology of Photovoltaic Panel Based on Thermal Infrared and Optical Remote Sensing

Abstract. Rapid access to the operating status of Photovoltaic (PV) panels and troubleshooting can save management and maintenance costs for the development of PV power plants, which is important for PV power plant management and power generation capacity assurance. The use of remote sensing technology to identify the faults of photovoltaic panels has developed rapidly, however, current research usually relies only on a single optical data source to identify and count the area of PV panels in a PV electric field, although there are literature on PV panel fault detection, only the surface fault identification of PV panels is tested, while the internal faults (such as panel bad points or bad lines) cannot be identified because of the limitations of optical remote sensing. In this paper, a photovoltaic panel fault monitoring technology based on multi-source remote sensing is proposed. The optical and thermal infrared hybrid data combined with deep learning technology are used to achieve rapid and accurate fault identification and localization of PV panel arrays. It can not only automatically identify PV panels that are obscured by dust and foreign objects, but also locate PV panels that have bad dots or bad lines, which greatly improves the ability and effectiveness of remote sensing PV panel fault monitoring. The high-resolution unmanned air vehicle (UAV) optical image and thermal infrared image are applied in this experiment. The Mask RCNN algorithm is used to accurately locate and number the photovoltaic panel of the optical image. Then, the fault scene classification model is established for the multi-type fault characteristics of the optical image and thermal infrared image within the panel range, so as to identify five types of faults, such as dust cover, branch cover, bird droppings cover, internal bad points and bad lines of PV panel, which effectively solves the problem that the single optical remote sensing image cannot identify the internal component faults of the photovoltaic panel under normal conditions.

Physics-informed deep learning and linear programming for efficient optimization of combined cycle power plants

Efficient operation of combined cycle power plants (CCPP) involves solving mixed-integer nonlinear optimization problems, constrained by thermodynamic differential equations, thermal constraints, and temporal dependencies between variables. The optimization problem aims to maximize profit for day-ahead operation by minimizing short-term and long-term costs, and accounting for volatility in ambient conditions and energy price. In operational terms, the challenge lies in developing an efficient and robust optimization algorithm capable of determining optimal values for thousands of temporal variables. It must also have the capacity to evaluate a multitude of scenarios encompassing various weather and market conditions. To address this challenge, this paper proposes a hybrid optimizer that utilizes deep learning models trained on historical operational data of a real CCPP, along with a surrogate physics-driven piecewise-linear model of CCPP, to determine the optimal values of thousands of control setpoints, with strong temporal co-dependencies, within a two-minute runtime required by industry constraints. A multi-resolution architecture is proposed to efficiently implement this optimizer. The algorithm sequentially solves the problem in coarse and fine resolutions to accelerate convergence. Final results are checked for feasibility using pre-trained deep learning models, ensuring accuracy and validity. The study shows that the proposed model provides comparable results to the exact solution while reducing computational costs, up to 95%. This research has practical implications for power plant system operators, as well as traders and dispatchers, enabling them to make optimal decisions for day-ahead contracting purposes and CCPP operation in a reasonable time, ultimately leading to more efficient and cost-effective power plant management. Furthermore, the methodology introduced in this work could potentially be extended to other industrial applications that require large-scale, complex optimizations, where the precise governing principles of a process can be approximated by piecewise-linear functions.

REVIEW ON PRODUCTION AND UTILIZATION OF FLY ASH: AN INDIAN PERSPECTIVE

A rapidly developing economy creates surge in energy demand and forced to find various alternative energy sources. Though many non-conventional energy sources have come up in the energy landscape, the t dependency on coal-based energy has not been reduced. Continuous coal burning generates huge quantity of Fly Ash and creates problems of storage, transportation, and management for power plants. If the fly ash generated is not handled properly, it poses a serious threat to ecology and the environment. The present study begins with focusing on the importance of coal in the Indian energy sector and discuss the status of fly ash generation and utilization throughout the years. It also focuses on how enforced laws and regulations help for management of fly ash. It emphasizes fly ash classification and physical-chemical characteristics, then, elaborates on the different sectors that have adopted fly ash as resource material and proven its worth over a time. This review attempts to highlight the current status of fly ash management in India which contributes to environmental and natural resources conservation.

Research on Multi-Objective Energy Management of Renewable Energy Power Plant with Electrolytic Hydrogen Production

This study focuses on a renewable energy power plant equipped with electrolytic hydrogen production system, aiming to optimize energy management to smooth renewable energy generation fluctuations, participate in peak shaving auxiliary services, and increase the absorption space for renewable energy. A multi-objective energy management model and corresponding algorithms were developed, incorporating considerations of cost, pricing, and the operational constraints of a renewable energy generating unit and electrolytic hydrogen production system. By introducing uncertain programming, the uncertainty issues associated with renewable energy output were successfully addressed and an improved particle swarm optimization algorithm was employed for solving. A simulation system established on the Matlab platform verified the effectiveness of the model and algorithms, demonstrating that this approach can effectively meet the demands of the electricity market while enhancing the utilization rate of renewable energies.

Quantum and quantum-inspired optimization for an in-core fuel management problem

Operation management of nuclear power plants consists of several computationally hard problems. Searching for an in-core fuel loading pattern is among them. The main challenge of this combinatorial optimization problem is the exponential growth of the search space with a number of loading elements. Here we study a reloading problem in a Quadratic Unconstrained Binary Optimization (QUBO) form. Such a form allows us to apply various techniques, including quantum annealing, classical simulated annealing, and quantum-inspired algorithm in order to find fuel reloading patterns for several realistic configurations of nuclear reactors. We present the results of benchmarking the in-core fuel management problem in the QUBO form using the aforementioned computational techniques. This work demonstrates potential applications of quantum computers and quantum-inspired algorithms in the energy industry.