Optimal System Operation Research Articles

Real-Time Power System Optimization Under Typhoon Weather Using the Smart “Predict, Then Optimize” Framework

With the increase of extreme weather events caused by global climate change, the issue of power system resilience has become more and more important. Traditional power system management methods cannot cope with dynamic real-time changes, and it is difficult to effectively predict and respond to potential failures caused by extreme weather. To this end, this paper proposes a real-time power system optimization method based on the Smart “Predict, then Optimize” (SPO) framework. The SPO method first uses the Transformer model to predict, in real time, the future line damage states and then dynamically adjusts the optimization strategy based on the prediction results. This method can efficaciously enhance the prediction accuracy of faulty lines under extreme weather conditions and optimize generation scheduling, load management, as well as EV battery scheduling to minimize the system cost. This study proposes a solution based on the SPO loss function, artificial intelligence prediction model, and bi-level optimization model to address the dynamic optimization of power systems under extreme conditions, significantly enhancing the system’s response to extreme weather events. The experimental results demonstrate that the SPO method can optimize system operation in real time, significantly reducing load shedding and total system cost during typhoon weather, which not only improves the system’s economic efficiency but also effectively enhances power system resilience.

Strategies for Multigeneration in Residential Energy Systems: An Optimization Approach

With the energy transition, energy supply trends indicate more autonomy for the final consumer, with a more decentralized, intelligent, and low-carbon scenario. Multigeneration technologies offer substantial socioeconomic and environmental advantages by enhancing the efficient utilization of energy resources. The main objective of this study is to develop a flexible, easy-to-use tool for the optimization of multigeneration systems (configuration and operation), focused on obtaining minimal annual costs. C++ was used for the implementation of the optimization problem, which was solved using IBM’s ILOG CPLEX Optimization Studio solver. The case study is a residential consumer center, with energy demands encompassing electricity (including electric vehicles), sanitary hot water, and coolth (air conditioning). The optimal economic solution indicates the installation of 102 photovoltaic modules and the use of biomass to produce hot water. When compared with a conventional solution, where all energy demands are met conventionally (no renewables nor cogeneration), the optimal economic solution reduced annual costs by 27% despite presenting capital costs 42% higher.

Deep reinforcement learning for multiple reservoir operation planning in the Chao Phraya River Basin

This study demonstrates application of Deep Deterministic Policy Gradient (DDPG)-based algorithm to provide comprehensive and flexible plans for reservoir operation planning of the multiple reservoir system in the Chao Phraya River Basin (CPYRB), Thailand aiming to mitigate flood and drought risks in the region. The multi-agent-based Deep Reinforcement Learning (DRL) model is accordingly constructed considering 7-D predicted inflow, reservoir water released from adjacent reservoir, downstream flow condition, and changes in reservoir water storage, as state variables. The desired goal is to increase water storage levels in all reservoirs by 10–15% to ensure higher potential in supplying water for crop cultivation over the dry seasons and preventing flood occurrences during wet season. Simulation results from 2009 to 2022 indicate that DRL–DDPG-based algorithm can perform well in solving sequential decision problems for optimal operation of multiple reservoir system to achieve the desired water storage goal. It can offer realistic simulation results of seasonal and annual release schemes and reservoir release ratios among reservoirs in the system compared to actual operation and Fmincon and ANFIS optimizations. Importantly, DRL model demonstrates a significant advantage in view of increasing the long-term water storage levels in all reservoirs as targeted in the modelling process while maintaining the similar and consistent release schemes in the reservoir system. For the multipurpose multiple reservoir system operation, adjusting the dynamic desired goals within multi-agent-based RL model is advisable to attain the specific desired outcomes and address various water scenarios.

Optimal Design and Operation of an Ultrasonic Driving System for Algae Removal Considering Underwater Environment Load.

This study investigates the optimal design and operation of an underwater ultrasonic system for algae removal, focusing on the electromechanical load of Langevin-type piezoelectric transducers. These piezoelectric transducers, which operate in underwater environments, exhibit variations in electrical-mechanical impedance due to practical environmental factors, such as waterproof molding structures or variations in pressure and flow rates depending on the water depth. To address these challenges, we modeled the underwater load conditions using the finite element method and analyzed the impedance characteristics of the piezoelectric transducer under realistic environmental conditions. Based on this analysis, we developed an ultrasound-driven system capable of efficient output control by incorporating the impedance characteristics of the transducer under load variations and subaquatic conditions. This study proposes analytical and experimental methods for modeling and analyzing practical ultrasound-driven systems for algae removal.

A photovoltaic-storage system configuration and operation optimization model based on Model Predictive Control

A photovoltaic-storage system configuration and operation optimization model based on Model Predictive Control

The Environmental and Public Health Benefits of Offshore Wind Power Deployment in China.

With the rapid decline in the levelized cost, offshore wind power offers a new option for the clean energy transition of the power sector in China's coastal areas. Here, we develop a power system capacity expansion and operation optimization model to simulate the penetration of offshore wind power in China and quantify the associated health effects. We find that offshore wind power has great potential in mitigating the negative impacts of existing coal-fired power emissions. By deploying cost-competitive offshore wind power, it is projected that by 2050, offshore wind power would contribute 2% to 5% (306-654 TWh) of China's total electricity generation, cumulatively reducing pollutants from the electricity sector by 3% and preventing 165,000 premature deaths. Notably, with the implementation of incentive policies, the scale of offshore wind power deployment expands two to three times, yielding public health benefits that far outweigh the costs of policy implementation.

Modeling and Analyzing the Spatiotemporal Travel Patterns of Bike Sharing: A Case Study of Citi Bike in New York

As the urban transportation demand continues to grow, the effective management and optimization of bike-sharing systems are of significant importance for urban planning and transportation engineering. This study aims to identify the spatiotemporal distribution of the peak-period departures and arrivals of bike sharing within Manhattan, New York, and to analyze the community clustering patterns and their underlying rules. Additionally, a comparative analysis across multiple time periods was conducted to enhance the research’s practical value. This study utilized GPS trajectory data from the New York City bike-sharing system for 2023. After analyzing the travel patterns throughout the year, we selected August, the month with the highest usage, to study the origin-destination (OD) travel aggregation patterns using flow models and the theoretical constructs of travel networks, measuring and analyzing travel characteristics. Subsequently, community detection algorithms were applied to analyze the clustering patterns and relationships among various neighborhoods. The findings revealed that the use of bike sharing in New York exhibits an overall trend of increasing and then decreasing throughout the year, with significantly higher usage in the spring and summer compared to the fall and winter. Notably, August saw the highest usage levels, with hotspots primarily concentrated in the southwestern part of Manhattan, which is also the economic center of New York City. The OD aggregation patterns across the upper, middle, and lower parts of August show distinct variations. Through community analysis, several strongly associated neighborhood clusters were identified, which exhibited both aggregation and dispersion trends over time. In southern Manhattan, a community with high modularity emerged, showcasing strong interconnections among neighborhoods. These findings provide valuable insights into the usage patterns of bike sharing in New York and the factors influencing them, offering significant implications for the optimization of bike-sharing system operations and planning.

Thermodynamic model of coal direct chemical looping combustion

ABSTRACT Chemical looping combustion (CLC) technology offers cost-effective CO2 emission reduction. Coal direct chemical looping (CDCL) emerges as a promising solid fuel CLC technology owing to its ability to directly utilize coal without prior gasification, enhancing system integration and efficiency. Despite significant progress in CDCL, challenges persist, including carbon deposition and insufficient oxidation and reduction of oxygen carriers (OCs). This study conducted a thermodynamic equilibrium analysis of a CDCL combustion reactor using Cantera 3.0, a chemical calculation module developed at the California Institute of Technology. The analysis explored the impact of different temperatures, OC-to-coal molar ratios (θ), and OCs types on combustion products. By employing the minimum Gibbs function method and analyzing intrinsic reaction mechanisms, the study provides insights for predicting and optimizing system performance. Thermodynamic equilibrium analysis revealed that with CuO as the OC, insufficient OC (θ < 0.6) led to Cu as the reduction product, while sufficient OC (0.6 < θ < 1.3) yielded Cu and CuO, and excess OC (θ > 1.3) resulted in Cu2O. To optimize CO2 capture, a molar ratio of θ of at least 1.4 and a temperature of at least 800°C are recommended. When Fe2O3 served as the OC, insufficient OC (θ < 0.45) produced Fe and FeO, while excess OC generated FeO and Fe3O4. Optimal operational performance and CO2 capture efficiency required an OC-to-coal molar ratio exceeding 1.4 and a temperature higher than 700°C. This study demonstrates that in CDCL combustion systems, selecting appropriate OCs, temperatures, and OC-to-coal molar ratios ensures stable and efficient combustion and CO2 capture. These findings offer crucial guidance for optimizing system operation.

Optimal Operation of Multi-reservoir System Utilizing DEA, AIDE Algorithm and Flood Control Assessment by MCDM Approach

Optimal Operation of Multi-reservoir System Utilizing DEA, AIDE Algorithm and Flood Control Assessment by MCDM Approach

Enhanced AC/DC optimal power flow via nested distributed optimization for AC/VSC-MTDC hybrid power systems

The deployment of voltage source converters (VSC) to facilitate flexible interconnections between the AC grid, renewable energy system (RES) and Multi-terminal DC (MTDC) grid is on the rise. However, significant challenges exist in exploiting coordinated operations for such AC/VSC-MTDC hybrid power systems. One of the most critical issues is how to achieve the optimal operation of such wide-area systems involving several power entities with as minimal communication burden as possible. To address this issue, an enhanced AC/DC optimal power flow (OPF) is specifically proposed. Firstly, a mixed-integer convex AC/DC OPF model is explicitly formulated to describe the optimal operation of such hybrid power systems. Subsequently, a nested distributed optimization method with double iteration loops is developed to offer optimal system-wide decision-making through a more “thorough” distributed communication architecture. In the outer iteration, the original AC/DC OPF problem is decomposed into several slave problems (SPs) associated with systems (including the AC grid and RESs) and one master problem (MP) associated with the integrated VSC-MTDC grid. Generalized Benders decomposition (GBD) serves to solve the master and slave problems iteratively. Techniques such as multi-cut generation and asynchronous updating are utilized to upgrade the GBD performance of computation efficiency and address communication delays. In the inner iteration, the master problem is continuously decomposed into multiple sub-MPs associated with individual VSCs. The alternating direction method of multipliers (ADMM) is employed to solve these sub-MPs iteratively. Proximal terms and heuristic approaches are embedded to enable parallel computation and handling of integer variables. Numerical experiment results finally validate the effectiveness of the proposed enhanced AC/DC OPF. The constructed AC/DC OPF model exhibits acceptable accuracy in terms of power flow calculation, and the developed nested distributed optimization method showcases decent convergence rate and solution optimality performances.

Robust optimization of regional biomass supply chain system design and operation with data-driven uncertainties

To address climate change and ensure energy security, biomass energy has become a popular renewable energy alternative to traditional fossil fuels. This study focuses on the strategic planning and tactical management problem of a regional biomass supply chain system with multiple feedstock and final products. A novel SVC-based data-driven robust optimization model is developed to provide the tradeoff solutions under uncertainties. Compared with the traditional robust optimization model, it can better depict the uncertain sets, reduce data redundancy, and provide less conservative strategies for decision makers according to their risk preferences. The proposed model is validated through a case study, Meizhou city in China. The results suggested the optimal investment scale and site for biorefineries and biomass power plants at the strategic planning level, as well as the feedstock supply network, inventory management, and production arrangement at the tactic management level. The optimal robust solutions can be derived by varying the envelope level of the uncertain sets. Moreover, sensitivity analysis is performed to investigate the influences of the variations of key parameters. It is found that the produced bioethanol and electricity lack cost advantages in the current situation, even with a 30 % reduction in main cost parameters. Insufficient biomass yield may require extra capital investment to satisfy bioenergy demand but with a higher supply cost. Thus, a high and long-term subsidy is necessary to facilitate the sustainable development of biomass and the low-carbon transition of the energy sector.

Optimal operation of diesel generator and battery energy storage system for total fuel cost minimization in hybrid power system

This study addresses the challenge of optimizing the operation of the diesel generator (DG) and battery energy storage system (BESS) to minimize the total fuel cost in a hybrid tugboat application, using the Whale Optimization Algorithm (WOA), a metaheuristic optimization algorithm. A specific fuel consumption (SFC) curve and an optimal control algorithm are developed to enhance the performance of the hybrid tugboat. Various case studies examine the impacts of different tugboat mode durations on the optimal operation of DG and BESS. The effectiveness of WOA is validated with other optimization algorithms. From the developed SFC curve and optimal control algorithm, the optimal DG operating conditions to improve fuel efficiency are determined. As a result, through the optimized operation of DG and BESS suggested by WOA, the total fuel cost of the hybrid tugboat is reduced by 9.23 % compared to the conventional tugboat. In contrast to other algorithms, WOA performed exceptionally well in achieving the optimal operation of DG and BESS, with the highest total fuel cost savings and the smallest BESS operating capacity. The findings highlight that DG should be utilized mostly during the operating mode with the longest duration and higher load demand to minimize the total fuel cost.

Numerical simulation of a forced circulation solar water heating system

This study presents a sophisticated numerical simulation model for a forced circulation solar water heating system (FC-SWHs), specifically designed for the unique climatic conditions of Algeria. The model aims to cater to the hot water needs of single-family houses, with a daily consumption of 246 L. Utilizing a dynamic approach based on TRNSYS modeling, the system’s performance in Ain Temouchent’s climate was scrutinized. The model’s validation was conducted against literature results for the collector outlet temperature. Key findings include a maximum monthly average outlet temperature of 38 °C in September and a peak cumulative useful energy gain of 250 W in August. The auxiliary heating system displayed seasonal energy consumption variations, with the highest rate of 500 kJ/hr in May to maintain the water temperature at 60 °C. The energy input at the storage tank’s inlet and the consistent high-level energy output at the hot water outlet were analyzed, with the former peaking at 500 W in May. The system ensured an average water tank temperature (hot, middle and bottom) and water temperature after the mixer, suitable for consumption, ranging between 55 °C and 57 °C. For applications requiring cooler water, the mixer’s exit temperature was maintained at 47 °C. The study’s key findings reveal that the TRNSYS model predicts equal inlet and outlet flow rates for the tank, a condition that is particularly significant when the system operates with high-temperature water, starting at 55 °C. The flow rate at this temperature is lower, at 7 kg/hr, while the water mass flow rate exiting the mixer is higher, at 10.5 kg/hr. In terms of thermal performance, the system’s solar fraction (SF) and thermal efficiency were evaluated. The results indicate that the lowest average SF of 54% occurs in July, while the highest average SF of over 84% is observed in September. Throughout the other months, the SF consistently stays above 60%. The thermal efficiency of the system varies, ranging from 49 to 73% in January, 43–62% in April, 48–66% in July, and 53–69% in October. The novelty of this research lies in its climate-specific design, which addresses Algeria’s solar heating needs and challenges. Major contributions include a thorough analysis of energy efficiency metrics, seasonal auxiliary heating demands, and optimal system operation for residential applications, supporting Algeria’s goal of sustainable energy independence.

Low-carbon optimization of multi-microgrid system operation considering supply–demand bidirectionality based on Nash bargaining

Low-carbon optimization of multi-microgrid system operation considering supply–demand bidirectionality based on Nash bargaining

Multi-point temperature or humidity prediction for office building indoor environment based on CGC-BiLSTM deep neural network

The aim of this study is to predict the temperature or humidity changes at multiple relevant points in a building using a deep neural network architecture with multi-task learning to provide more reference information for the design and optimal operation of heating and ventilation systems. For this purpose, traditional multi-task prediction algorithm architecture is combined with Customized Gate Control and other neural networks to build deep neural network architectures for indoor environments with multi-point temperature or humidity prediction tasks. To test the prediction effectiveness of the architecture, a task of predicting temperature or humidity 24 h in advance was designed on a real office building indoor environment dataset, and the prediction results were compared with other single-task and multi-task prediction models. Two experimental conditions were designed for this study, one using the complete training set and the other reducing the training set at a certain point. Through the final prediction results, it is found that the multi-task prediction architecture used in this paper shows better or nearly optimal results compared to other prediction models under both working conditions. This study provides some reference value for the application of multi-task prediction algorithms to the task of predicting indoor environments in buildings.

ARCHITECTURE MODEL OF THE GAS METERING VIRTUAL POINTS AUTOMATION SYSTEM

The article presents the results of the research into the architectural model for an automated gas metering system based on the concept of virtual points of gas metering, which is an abstraction layer above physical gas metering devices providing possibilities for great automation and optimization as well as flexibility and precision in the domain of centralized gas metering. The primary objective of the research is to design an optimal architectural model that satisfies the requirements and constraints of the system's conceptual model. This study delves into the process and describes the result of architectural model creation by covering the following aspects: defining system components and their responsibilities, assigning behaviors to the components, defining cross-component and external interactions, designing data models as well as providing API and communication solutions. Additionally, the research also focuses on the basic external interactions and infrastructure considerations necessary for the operation of the automated system. A significant portion of the research is dedicated to addressing specific challenges inherent in gas metering automation. These challenges include the prediction of gas calorific values, determining the temperature at the points of gas consumption, and the calculation of monthly gas usage in kilowatt-hours. By addressing these issues, the research ensures that the architectural model is completed and includes solutions for the domain problems at the scale of the architecture. The solutions proposed for these challenges are integrated into the overall architectural model, providing a robust framework for future research into the topic as well as a basement for the implementation of an accurate and efficient centralized gas metering automation system. Some of the solutions developed during the research lay on the line between software architecture and user experience design. Also, the research scope includes the infrastructural model required for the automated system to function optimally. This includes encompasses both infrastructure-centric and application-centric models, presenting different viewpoints on the reference infrastructure for the optimal system operation.

Operation Analysis of Power Systems with HVDC Interconnections Using a Transient Stability Aware OPF Method

High-voltage direct current (HVDC) transmission systems are widely used today due to their several advantages over high-voltage alternative current (HVAC) systems. They are primarily utilized in connecting different large grids or smaller independent networks, especially in submarine interconnections. Many studies have been conducted globally on the optimal planning, operation, and control of these power systems. Understanding the transient stability of these systems during significant disturbances is also of great importance. This paper specifically focuses on power systems with HVDC connections and high penetration of renewable energy sources. Analytical models were developed for power system components like HVDC converters, DC lines, and full converter wind generators. A simulation of a test-case power system was conducted, including various significant disturbances such as HVDC line trips, short circuits, power unit failures, and major changes in load. The voltage and frequency stability of the system under specific operational scenarios was also examined. The results obtained indicate technical limitations and operating guidelines to avoid undesirable conditions and system failure. In conclusion, adopting short-term stability tests the optimal operation point of the system, and the limitations in the penetration by RES units can be decided.

Optimization of carbon dioxide adsorption from industrial flue gas using zeolite 13X: A simulation study with Aspen Adsorption and response surface methodology

One of the best ways to prevent further warming of the planet and reduce pollutants is to remove or adsorb and recover carbon dioxide gas from the exit of cement factory exhaust. So far, several methods have been proposed for this; In this research, zeolite 13X adsorbent is used considering the operational conditions. To remove CO2 gas from the desired gas mixture, an adsorption operation unit with a column filled with zeolite 13X adsorbent was simulated in Aspen Adsorption V14 software. Changes in parameters such as temperature, and pressure and their effects on adsorption rate, adsorbent saturation time, and output heat rate were investigated to achieve higher system efficiency and optimal operating conditions. The adsorption unit was optimized by RSM to achieve maximum CO2 adsorption. Temperature and mole fraction of CO2 were identified as key influencing factors determining the adsorption performance of zeolite 13X for CO2 adsorption. It was found that the system should be operated at a temperature of 28.07 °C and a pressure of 1.202 bar to achieve maximum adsorption. Finally, 2.784 mol% CO2 gas was captured by the said process in optimal operating conditions. Further analysis revealed higher temperatures slightly decrease CO2 adsorption capacity, while increasing pressure enhances it. Conversely, elevated flow rates reduce breakthrough time, indicating a trade-off between adsorption capacity and capture efficiency.

Optimal scheduling of park-level integrated energy system considering multiple uncertainties: A comprehensive risk strategy-information gap decision theory method

Multiple uncertainties in the park-level integrated energy system (PIES) can affect the optimal operation of system. Information gap decision theory (IGDT) is a commonly used method of dealing with uncertainty by developing risky strategies to avoid risks or seek risky returns. However, there is no unified method or process for the selection of risk strategies and the setting of related parameters, leading to a certain blindness in the application of IGDT method. A comprehensive risk strategy (CRS)- IGDT approach is proposed for scheduling of PIES considering uncertainties of heat load, photovoltaic output and electric load. Risk averse strategy (RAS) and Risk seek strategy (RSS) scheduling models are constructed. Then an optimized solution method based on adaptive steps ratio (ASR) is proposed to solve the above two models. The CRS and comprehensive risk cost function are proposed from a risk equalization perspective. The target deviation coefficient and steps ratio in the IGDT model are automatically optimized with the objective of minimizing the comprehensive risk cost. Combining the two cases, the average cost reduction is 6.6 % compared to Risk-neutral (RN), 11 % compared to RAS-IGDT, and 4.1 % compared to RSS-IGDT. Moreover, the average costs of CRS-IGDT are lower compared to stochastic programming and robust optimization methods. The experiments verify the generalization and superiority of the proposed method in coping with different information gap situations caused by uncertainty. CRS-IGDT provides new research ideas for dealing with uncertainty problems in PIES and other fields.

Differential pressure power generation in UGS: Operational optimization model and its implications for carbon emission reduction

Underground Gas Storage (UGS) is an important facility for natural gas peak adjustment and ensuring the balance between energy supply and demand. The daily gas injection and production capacity of large UGS can reach several hundred million or even billions of cubic meters, and the energy consumed by the injection and production equipment is not to be underestimated, which also contains a large amount of pressure energy that can be utilized for power generation. In this context, the optimization of low-carbon operation of UGS is of great practical significance in engineering. In this study, an innovative UGS system operation optimization method is proposed to integrate a natural gas differential pressure generator set in the UGS system. Considering the complex interconnections among multiple storage areas, multiple wells and wellsites, and the hydro-thermodynamic constraints during the operation of injection and production pipeline network, and with the objective of minimizing the carbon emission, the UGS integrated with Differential Pressure Power Generation System (UGSIDPPGS) is established. In order to solve this complex model, this paper designs an optimization solution framework based on variational assignment, which effectively avoids the problem of falling into inferior solutions during the solution process. The optimization model is applied to the case study of a large-scale depleted gas reservoir in China, and the optimal operation schemes under three scenarios are successfully obtained. The results show that the UGSIDPPGS optimization model, not only effectively utilizes the pressure energy, generates up to 56.908 × 106 kW h per month, and reduces the CO2 emission by 56,738 tons, but also achieves the low-carbon and economic operation of the UGS. In conclusion, the operation strategy proposed in this study is of great value for optimizing the operation of UGS, and provides practical guidance and suggestions for the low carbon and environmental protection of UGS and the utilization of new energy sources.