System Operation Cost Research Articles

Modeling smart rejuvenation on a series system with different failure modes

Abstract Software rejuvenation is a proactive fault management technique that is used to counteract aging phenomena in continuously running software systems. To mitigate such phenomena, rejuvenation includes preventive periodic stoppage of the running software, cleaning its internal state by garbage collection, flushing operating system kernel tables, defragmentation and reinitialization of internal data structures, and then restarting it. In this paper, a two-unit series software system is considered which can experience different failure modes. Each software component can experience both soft and hard failures. A hard failure is counteracted by a hardware reboot, though a soft failure is recovered by software rejuvenation. Additionally, rejuvenation is proactively initiated when a software component transitions into a degraded, failure-prone state. This paper introduces the innovative concept of smart rejuvenation, which strategically leverages system downtime caused by a hard failure in one component to simultaneously rejuvenate another component. To model the entire system’s evolution in time, a semi-Markov process is used. The aim of this work is twofold: firstly, to distinguish the rejuvenation policy for each software component that optimizes the entire system availability and operational cost, and secondly to examine if smart rejuvenation can improve these measures for the software system.

Optimization of Microgrid Dispatching by Integrating Photovoltaic Power Generation Forecast

In order to address the impact of the uncertainty and intermittency of a photovoltaic power generation system on the smooth operation of the power system, a microgrid scheduling model incorporating photovoltaic power generation forecast is proposed in this paper. Firstly, the factors affecting the accuracy of photovoltaic power generation prediction are analyzed by classifying the photovoltaic power generation data using cluster analysis, analyzing its important features using Pearson correlation coefficients, and downscaling the high-dimensional data using PCA. And based on the theories of the sparrow search algorithm, convolutional neural network, and bidirectional long- and short-term memory network, a combined SSA-CNN-BiLSTM prediction model is established, and the attention mechanism is used to improve the prediction accuracy. Secondly, a multi-temporal dispatch optimization model of the microgrid power system, which aims at the economic optimization of the system operation cost and the minimization of the environmental cost, is constructed based on the prediction results. Further, differential evolution is introduced into the QPSO algorithm and the model is solved using this improved quantum particle swarm optimization algorithm. Finally, the feasibility of the photovoltaic power generation forecasting model and the microgrid power system dispatch optimization model, as well as the validity of the solution algorithms, are verified through real case simulation experiments. The results show that the model in this paper has high prediction accuracy. In terms of scheduling strategy, the generation method with the lowest cost is selected to obtain an effective way to interact with the main grid and realize the stable and economically optimized scheduling of the microgrid system.

Economic Analysis of Red Tilapia (Oreochromis sp.) Production Under Different Solar Energy Alternatives in a Commercial Biofloc System in Colombia

The study investigates the economic aspects of red tilapia (Oreochromis sp.) production using biofloc technology under different electrical energy sources. Conducted at the El Vergel Fish Farming Association in Arauca, Colombia, the study examines four energy treatments: conventional energy (CE), combined conventional and photovoltaic energy (CPVE), full photovoltaic energy (PVE), and simulation of photovoltaic energy generating surplus for nighttime use (PVES). The water quality and zootechnical performance met the species requirements, with dissolved oxygen decreasing as fish size increased. The PVE treatment had the highest initial investment due to solar panels and battery costs, but it also had the lowest operating energy costs. However, the overall costs of the PVE treatment increased due to depreciation and maintenance. Feed was the largest production cost, followed by labor in most treatments, while depreciation was a major cost for the PVE treatment. The total operating cost (TOC) of the photovoltaic energy systems (PVE and PVES) was lower compared to that of conventional energy (CE), with PVES showing the highest cost savings. The reduction in energy costs highlights the potential for solar energy systems to enhance the economic viability of aquaculture production, making these systems a favorable option for sustainable production in the long term.

Risk‐Averse Distributionally Robust Environmental‐Economic Dispatch Strategy Based on Renewable Energy Operation: A New Improved Whale Optimization Algorithm

Although the use of optimization techniques for environmental and economic dispatch of integrated electricity and natural gas systems has been widely applied, there are still significant challenges in meeting the multiple energy demands of conventional and renewable energy sources, mainly wind‐solar. In this study, Wasserstein distance is introduced to measure the randomness of wind‐solar power generation to construct the uncertainty set. A multi‐objective distributionally robust optimization (MODRO) environmental‐economic scheduling model for risk aversion that minimizes the risk cost, the system operation cost, and the carbon emission cost is proposed to achieve the balance between the risk cost, the operation cost, and the pollutant emission. To solve the model efficiently, the multi‐objective whale optimization algorithm (IMOWOA) was improved and used the 4‐node power system and the 7‐node natural gas system as case studies. The results show that the MODRO environmental‐economic scheduling model can measure the operational risk due to the stochastic fluctuation of wind‐solar energy sources, and provide an effective decision‐making tool for policymakers. Considering P2G technology and gas turbines at the same time, it promotes the coupled operation of electric‐gas integrated systems and achieves good economic efficiency. Thus, the model provides an effective solution for the stability, economy, and cleanliness of the integrated electric gas system. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Safe deep reinforcement learning-assisted two-stage energy management for active power distribution networks with hydrogen fueling stations

In a power-hydrogen coupled integrated energy system (PHCIES), hydrogen fueling stations (HFSs) with solar photovoltaic (PV) systems are crucial devices to support hydrogen demand and maintain a stable and near-zero pollutant emission-based PHCIES operation by producing a pollutant-free hydrogen. However, optimal operation management of HFSs is challenging because the electrolyzers, compressor, hydrogen storage system (HSS), and fuel cell in HFSs are interconnected and change dynamically under various operational environments. To resolve this issue, this paper presents a safe deep reinforcement learning (DRL)-assisted two-stage framework that ensures a reliable and economical PHCIES operation. In the first stage, day-ahead operational schedules of stand-alone PV systems, PV-enabled HFSs, on-load tap changers, and capacitor banks with hourly resolution are coordinated to minimize the system operation cost, electricity arbitrage cost, PV curtailment cost, and real power loss by solving a Volt-VAR control (VVC) optimization problem. In the second stage, a safe DRL algorithm with a 15-min resolution is employed to minimize the real power loss and maximize the HFS profit by rescheduling the reactive power of stand-alone PV systems and real/reactive power and hydrogen of HFSs. The proposed self-tuning adaptive safety module integrated in the DRL method ensures no violations of HSS’ state-of-hydrogen (SOH) and voltage magnitude in the PHCIES during the training process. Numerical examples conducted on a PHCIES (IEEE 33-bus system coupled with two PV-enabled HFSs) demonstrate the effectiveness of the proposed framework in terms of training convergence, SOH/voltage violation, real power loss, and profit of HFSs.

A resilience-oriented approach to integrated energy management systems: Addressing energy conversion unit unavailability and cost efficiency

As integrated energy systems become increasingly complex, their vulnerability to energy conversion units unavailability poses a considerable threat to system resilience and cost efficiency. This research is motivated by the need to address the growing threat of energy conversion unit unavailability and its significant impact on both system performance and operational costs. The development of a comprehensive vulnerability assessment framework, coupled with the integration of machine learning techniques, is hypothesized to significantly enhance the resilience and cost-efficiency of integrated energy management systems when faced with various unavailability scenarios. A novel methodology is introduced to enhance integrated energy management systems, focusing on the critical role of diverse energy conversion units. The methodology includes a new vulnerability index to quantify the impact of energy conversion unit unavailability on system performance, alongside an energy conversion unavailability attack model from the malicious actor perspective. Results indicate that under worst-case energy conversion unavailability attack scenarios, integrated energy systems operational costs can increase by up to 26.19%. The proposed solution, incorporating a Deep Q-Network into the integrated energy management system, achieves an 85.24% F1-score and a 96.68% reduction in additional costs associated with energy conversion unavailability attacks. This research demonstrates the varied impacts of different energy conversion units on integrated energy systems and proposes an enhanced integrated energy management system framework that improves system resilience and cost efficiency.

Research on the Operation Optimization of Public Building Systems in Extremely Cold Areas Based on Flexible Loads

The heating energy consumption in public buildings in cold regions is notably significant, presenting substantial scope for energy savings and emission reductions. Flexible loads can actively participate in controlling the operation of the power grid, improving the energy utilization and the economy of the system. This study introduces flexible loads into the operation optimization of energy systems, establishing mathematical models for flexible thermal and electrical loads. A two-stage operation optimization method is proposed: the first stage simulates the starting and stopping control conditions of equipment at varying temperatures and times, selecting the optimal time period to regulate the thermal loads; the second stage employs a multi-objective particle swarm optimization algorithm to optimize the scheduling of the system’s electrical load. Finally, an empirical analysis is carried out in a public building in Shenyang City as an example, and the results indicate that optimal scheduling of flexible thermal and electrical loads reduces the daily operating cost of the energy supply system by RMB 124.12 and decreases carbon emissions by 22.7%.

Collaborative Optimization Scheduling of Source-Network-Load-Storage System Based on Ladder-Type Green Certificate–Carbon Joint Trading Mechanism and Integrated Demand Response

To fully leverage the potential flexibility resources of a source-network-load-storage (SNLS) system and achieve the green transformation of multi-source systems, this paper proposes an economic and low-carbon operation strategy for an SNLS system, considering the joint operation of ladder-type green certificate trading (GCT)–carbon emission trading (CET), and integrated demand response (IDR). Firstly, focusing on the load side of electricity–heat–cooling–gas multi-source coupling, this paper comprehensively considers three types of flexible loads: transferable, replaceable, and reducible. An IDR model is established to tap into the load-side scheduling potential. Secondly, improvements are made to the market mechanisms: as a result of the division into tiered intervals and introduction of reward–penalty coefficients, the traditional GCT mechanism was improved to a more constraining and flexible ladder-type GCT mechanism. Moreover, the carbon offset mechanism behind green certificates serves as a bridge, leading to a GCT-CET joint operation mechanism. Finally, an economic low-carbon operation model is formulated with the objective of minimizing the comprehensive cost consisting of GCT cost, CET cost, energy procurement cost, IDR cost, and system operation cost. Simulation results indicate that by effectively integrating market mechanisms and IDR, the system can enhance its capacity for renewable energy penetration, reduce carbon emissions, and achieve green and sustainable development.

Demand Response Strategy Considering Industrial Loads and Energy Storage with High Proportion of Wind-Power Integration.

To address the challenges of reduced grid stability and wind curtailment caused by high penetration of wind energy, this paper proposes a demand response strategy that considers industrial loads and energy storage under high wind-power integration. Firstly, the adjustable characteristics of controllable resources in the power system are analyzed, and a demand response scheduling framework based on energy storage systems and industrial loads is established. Building on this foundation, a multi-scenario stochastic programming approach is employed to develop a day-ahead and intra-day multi-time-scale optimization scheduling model, aimed at maximizing economic benefits. In response to the challenges of wind-power fluctuations with high temporal resolution, a strategy for smoothing intra-day wind-power variability is further proposed. Finally, simulations are conducted to derive optimal demand response strategies for different stages. As verified by the comparison of different scheduling strategies, the demand response strategy proposed in this paper can reduce wind curtailment when there is sufficient wind power and reduce load shedding when there is insufficient wind power, which effectively reduces the system operation cost.

Collaborative water-electricity operation optimization of a photovoltaic/pumped storage-based aquaculture energy system considering water evaporation effects

The increasing global population, economic development, and urbanization trends have led to a heightened demand for seafood, presenting a significant challenge to the growth trajectory of the food industry. As the most rapidly expanding segment within the food industry, aquaculture presents a sustainable solution to mitigate the overexploitation of marine resources and address the growing demand for food. Aquaculture's sufficient aquatic expanses offer considerable potential for PV deployment, thereby relieving the demand for limited land resources. However, existing research rarely addresses the collaborative operation of water and electricity in aquaculture systems. The effects of water evaporation from PV panel-covered water surfaces on the collaborative water-electricity operation are generally neglected. Hence, this work proposes a collaborative water-electricity operation of a photovoltaic (PV)-pumped storage-based aquaculture energy system considering the water evaporation effects. This optimization method accounts for the reduced water evaporation due to PV panel shading. Water surface PV and pumped storage are integrated into the system to fulfill electricity needs, with pumped storage serving as a dual-purpose device for water and electricity supply. Environmentally sustainable water-electricity generation and aquaculture energy system requirements are established. Water surface PV electricity generation is modeled based on climate and engineering conditions, while aquaculture pond electricity requirement includes oxygenation, feeding, and water replenishment. Finally, a collaborative water-electricity operation model is introduced to optimize operation strategies for various devices. A case study on a fish-light complementation project demonstrates that this optimization method can reduce reliance on the utility grid and overall system operational costs.

A value-of-information-based optimal sensor placement design framework for cost-effective structural health monitoring (with application to miter gate monitoring)

Structural health monitoring (SHM) involves collecting information to assess the health of a structure, typically to guide risk-informed maintenance decision-making or predict limit state behavior throughout its lifespan. Although the value of information (VoI) obtained from an SHM system can facilitate improved decision-making, it is important to estimate its overall utility by considering the costs involved in designing, developing, installing, maintaining, and operating the system. A feasible SHM system provides greater expected returns resulting from data-informed lifecycle management decisions than the cost of the SHM system design, fabrication, deployment, operation, and maintenance. That is, since data acquisition is a precursor to data-informed decision-making, the design of an SHM system governs its feasibility. Such cost-benefit analyses are a current topic of research in the SHM community. One approach that has been proposed for these analyses is a preposterior decision analysis using the VoI metric. In this paper, we propose a sensor optimization framework that maximizes the VoI over the structure’s lifecycle, constrained by a pre-decided maintenance policy. We use two different VoI metrics: the traditional expected VoI and a gambling-theory-inspired normalized expected-savings-to-investmentrisk ratio. We introduce three time-normalized, unitless VoI metrics that are valuable for evaluating the performance of an SHM design over an extended period. Furthermore, we consider the effect of different risk profiles on the overall optimal sensor design, recognizing that the cost of decision-making depends on the utility and risk perception of the decision-maker. We conduct a detailed analysis on the marginal utility gain of gathered information as we add more sensors, observing the utility to diminish in line with the law of diminishing returns as the information content increases. This framework is applied to the design of an SHM system used for monitoring miter gates.

Optimal Configuration of Electricity-Heat Integrated Energy Storage Supplier and Multi-Microgrid System Scheduling Strategy Considering Demand Response

Shared energy storage system provides an attractive solution to the high configuration cost and low utilization rate of multi-microgrid energy storage system. In this paper, an electricity-heat integrated energy storage supplier (EHIESS) containing electricity and heat storage devices is proposed to provide shared energy storage services for multi-microgrid system in order to realize mutual profits for different subjects. To this end, electric boiler (EB) is introduced into EHIESS to realize the electricity-heat coupling of EHIESS and improve the energy utilization rate of electricity and heat storage equipment. Secondly, due to the problem of the uncertainty in user-side operation of multi-microgrid system, a price-based demand response (DR) mechanism is proposed to further optimize the resource allocation of shared electricity and heat energy storage devices. On this basis, a bi-level optimization model considering the capacity configuration of EHIESS and the optimal scheduling of multi-microgrid system is proposed, with the objectives of maximizing the profits of energy storage suppliers in upper-level and minimizing the operation costs of the multi-microgrid system in lower-level, and solved based on the Karush-Kuhn-Tucker (KKT) condition and Big-M method. The simulation results show that in case of demand response, the total operation cost of multi-microgrid system and the total operation profit of EHIESS are 51,687.73 and 11,983.88 CNY, respectively; and the corresponding electricity storage unit capacity is 9730.80 kWh. The proposed model realizes the mutual profits of EHIESS and multi-microgrid system.

Reliable cost-efficient integration of pumped hydro storage in islanded hybrid microgrid for optimum decarbonization

The employment of renewable energy-based resources is growing rapidly, necessitating the use of energy storage technologies to effectively manage their intermittent power generation. This study proposes an optimal planning for various distributed generators in a microgrid system. The objective is to size and operate a reliable hybrid islanded microgrid with minimum total system operational cost. To determine the optimal energy management and size of each unit, the problem is formulated applying Particle Swarm Optimization methodology utilizing sets of historical data such as wind speed, solar irradiation, and load demand on an hourly basis. With the proposed methodology, the capacities of photovoltaic, wind turbine, pumped hydro storage, and fuel cell are optimized as 1300 kW, 1125 kW, 400 kW, and 1590 kW, respectively. The obtained results concluded that optimum size reduces 14.85 % and 22.6 % in the fuel cell consumption in summer and fall respectively. Furthermore, the amount of CO2 emission is reduced by 10.6 ton in those mentioned seasons. The results obtained indicate that the proposed system having cost of energy (COE) of 0.2961 ($/kWh), and an excess energy of 196.208 MWh. An effectiveness analysis highlights the significant impact of incorporating pumped hydro storage, increasing the reliability index by 50.9 %.

A Novel Day-Ahead Optimization-Oriented Low-Carbon Economic Scheduling Scheme for Integrated Energy Systems

As the global energy structure undergoes transformation and the low-carbon development process continues to advance, integrated energy systems have progressively emerged as crucial technical support for achieving sustainable development. In this paper, a joint-day optimal scheduling model is put forward considering the existence of dispatchable resources in community integrated energy systems (CIES). The aim is to cut down the system operation cost and enhance energy utilization efficiency. This model is founded on the concept of energy hubs and combines the shiftable, transferable, and reducible characteristics of demand-side flexible loads. It includes gas turbine power generation systems, energy storage, as well as wind and solar renewable resources. System operation cost and carbon trading cost are comprehensively taken into account, and ultimately, the CIES low-carbon economic dispatch model with the lowest total cost as the optimization objective is established. The Yalmip toolbox and Cplex solver are employed to solve the model. The optimization results of flexible electric and thermal loads participating in dispatching under different scenarios are analyzed through simulation. The economic benefits of electric and thermal independent dispatching are compared and analyzed, and the economic benefits of electric and thermal coupled dispatching are verified. The study reveals that the rational scheduling of user-side flexible loads can notably reduce operating costs, lower the load peak-to-valley difference and carbon emissions, and boost the comprehensive economic and environmental benefits of the system.

A joint optimization strategy for electric vehicles and air conditioning systems with building battery configuration

Building air conditioning systems, electric vehicles and battery energy storage systems all provide substantial flexibility for grid operations. However, the joint optimization strategy involving these three demand response resources in buildings has been infrequently studied. This research proposes a day-ahead optimization strategy to coordinate the joint operation of air conditioning systems and electric vehicles. In this framework, cooling load is predicted using an autoregressive exogenous model, while electric vehicle charging load is predicted through Monte Carlo simulations. An evaluation method is introduced to assess the comprehensive benefits of the demand response strategy, considering thermal comfort, economic efficiency, and grid friendliness. Using an office building in Tianjin, China, as a case study, the results indicate that the joint optimization strategy reduces operational costs by 3.71 % and peak electricity load by 38.62 % compared to the original strategy. Furthermore, it enhances the grid friendliness of the energy system by 42.64 %. The configuration of the battery energy storage system is also explored in conjunction with the optimization strategy to further improve grid friendliness. The impact of economic factors and thermal comfort on the configuration of the battery energy storage system is discussed. In the case study, raising the upper temperature limit by 1 °C can save at least 17.1 % in capacity, while battery energy storage system investment and operational costs can respectively be reduced by 55.04 % and 27.14 % within the thermal comfort range.

Impacts of Plug-in Electric Vehicles and Renewable Energy Source on Unit Commitment Problem using Cat Mouse Based Optimization A lgorithm

Objectives: The main objective of this paper is to solve the Cost Based Unit Commitment (CBIC) problem considering Renewable Energy Sources (RES) and Plug-in Electric vehicles (PEVs). The proposed problem minimizes the total operating of the interconnected system. Methods: A novel and recently developed successful optimization tool of Cat and Mouse Based Optimizer (CMBO) is proposed for optimal solution of Cost Based UC problem. The proposed CMBO approach is having two groups such as Cat group and Mice group for searching the global optimal solution with less computational time. It contains two phases like Cat’s movement towards Mice and Mice escape to a safe place is effectively updating the new position of Cat and Mice to easily reaching the best solutions. The control parameter of CMBO is number of agents is 50, number of variables is 10 and maximum number of iteration is 500. MATLAB 14.0 platform is utilized for the projected problem. Findings: The method tested on standard IEEE-39 bus system (10 generators and 24 hour) with an equivalent PEV and Wind farm. The CMBO approach minimize the total operating cost with various test cases. The outcomes such as UC schedule, Real power output of thermal, wind and PEV units, fuel cost, startup cost and total operating cost of the interconnected system are numerically and graphically reported. The total operating cost is 4.11 % minimized when compared with base case approach and 0.73 % reduced than the other existing method viz. Parallel UCs-RP method. Novelty: The CMBO is recently developed powerful optimizer and parameter free algorithm, so easily reaches the global optimal solutions. The obtained simulated results are compared with other mathematical and intelligent computational approaches for proving the efficiency and performance of the proposed CNBO technique. Keywords: Unit Commitment, Plug­in Electric Vehicles, Renewable Energy Sources, Cost minimization, Cat and Mouse Based Optimizer

Stochastic optimal scheduling of a combined wind-photovoltaic-CSP-fire system accounting for electrical heat conversion

Aiming at the consumption problem caused by the increasing scale of wind power and photovoltaic (PV) grid-connected, a multi-energy co-generation system is constructed with wind power, PV, concentrated solar power (CSP), and thermal power; in addition, in order to reduce the impact of the prediction errors of wind power, PV, and loads on the system’s economic operation, the photovoltaic and thermal power plants are used to provide the system's backup capacity together, and the opportunity constraint model of the reliability of the backup capacity is established, so as to satisfy the system reliability constraints at a certain higher confidence level; finally, a sampling-based deterministic transformation method is introduced to simplify the model. The model is simplified by introducing a sampling-based deterministic transformation method of opportunity constraint; finally, a stochastic optimal dispatch model of the combined wind-photovoltaic-CSP-fire system, which accounts for the conversion of electricity and heat, is constructed with the objective of minimising the integrated operating cost of the combined system, and the effectiveness of the proposed model is analysed by simulation verification.

Design and Construction of Automatic Railway Crossing Gate Control Using Proximity and Infrared Sensors Based on Omron CP1E E30-SDRA PLC

Railway crossing gates are one of the railway infrastructure facilities. Currently, there are still many problems, especially in traffic accidents. The cause of traffic accidents at railway crossings generally occurs due to the lack of facilities and infrastructure (rail crossing gates) and negligence of guards in carrying out their duties. Therefore, it is necessary to design an automatic railway crossing gate. The prototype of the automatic railway crossing controller uses proximity and infrared sensors to detect the arrival and departure of trains. When the infrared or proximity sensor detects the arrival of a train, the Programmable Logic Controller (PLC) activates Pulse Width Modulation (PWM), buzzer, and Light Emitting Diode (LED), so that the crossing motor goes down. After the crossing touches the limit switch, the motor stops and the buzzer and Light Emitting Diode (LED) remain on. The infrared or proximity sensor detects the departure of the train so that it reverses the motor so that the crossing goes up, then turns off the buzzer and Light Emitting Diode (LED). In this way, it is hoped that this design can be used to increase the efficiency of the system's operational costs and optimize the railway crossing system and is expected to reduce the number of traffic accidents at railway crossings.

Optimised Two-Layer Configuration of SESS-CCHP System Considering Wind and Light Output Correlation and Load Sensitivity

With the gradual depletion of fossil energy sources and the diversification of users’ energy demand, combined cooling, heating and power (CCHP) microgrids have become a hot technology to improve energy efficiency and promote efficient and synergistic energy operation. However, the uncertainty and correlation of wind power and photovoltaic (PV) outputs have posed a great challenge to the reliability of CCHP system operation, so CCHP systems are often equipped with energy storage devices to improve system flexibility to ensure the reliability of energy supply. However, system-owned reserves still have shortcomings such as high investment O&M costs and large space requirements. As an emerging model, “shared energy storage” can reduce the investment pressure of users and open up new ways for the economic and stable operation of CCHP systems. Therefore, based on the scenario of wind and solar power correlation and considering different types of load flexibility, this paper proposes to construct a shared energy storage station (SESS)-CCHP double-layer synergistic optimal allocation model. The model incorporates the consideration of the actual operation strategy of the CCHP system in the planning stage of energy storage. An example analysis shows that SESS reduces the total operating cost of the CCHP system by 25.96% and improves the new energy consumption rate by 10.46% compared with no energy storage. Compared with the system independently configured with energy storage, the cost saving is 2.14%, thus validating the effectiveness of the proposed model.

Fault diagnosis and intelligent maintenance of industry 4.0 power system based on internet of things technology and thermal energy optimization

The application of Internet of Things technology provides a new opportunity for the fault diagnosis and maintenance of power system. This study aims to explore how to improve the fault diagnosis ability of power system through Internet of Things technology, and realize the efficient utilization of heat energy, so as to achieve the goal of intelligent maintenance. Based on the analysis of current power system operation status, this paper determines the key role of thermal energy management in improving system operation efficiency and reducing energy consumption. It then uses iot sensors and data analytics to monitor heat flow and loss in the power system in real time, identifying potential points of failure through big data. In order to realize intelligent maintenance, this paper designs a fault prediction model based on thermal energy optimization, combined with machine learning algorithm, to further improve the accuracy of fault diagnosis. The experimental results show that the fault diagnosis method combined with the Internet of Things technology can significantly reduce the fault incidence and optimize the efficiency of heat energy use. By applying this model, the overall operating cost of the power system is reduced and the maintenance efficiency is improved.