Operation Optimization Research Articles

Methodology for risk assessment of multi-event scenarios on radioactive waste repository

Deep geological disposal, which is a permanent method that isolates a storage facility in rocks 200 to 1000 m deep underground, plays a role in safely disposing spent nuclear fuel to prevent excessive radioactive species from leaking out and its safety should be primarily proved. Risk for various radionuclide leakage scenarios should be calculated to be used as a safety indicator. One of the scenarios that significantly affects the safety of repository is the occurrence of external events such as an earthquake scenario. Several occurrences of such external disasters are expected over a long period of time which the safety functions of the deep disposal site must be maintained. Rather than performing a conservative single event evaluation, we expected that performing a more realistic evaluation, such as considering the number of events, is preferrable for design and operation optimization, particularly in an early project phase. This paper suggests a need for assessing multi-event scenarios and the methodology for simulation of several external disasters using earthquakes as an example. The methodology and algorithm of simulating multiple earthquakes and assessing risk distribution under those events at repository site is introduced in GoldSim software, and its results are expected to be used at simulating other event scenarios.

Optimization of the cruising speed for high-speed trains to reduce energy consumed by motion resistances

As one of the effective measures to reduce energy consumption of high-speed railway, train operation control has gained much attention because of its cost-effectiveness. Conventional approaches to eco-driving generally rely on single-mass train models and lack a thorough investigation into the vehicle cruising process, which can overlook real-world operation conditions and hinder further improvements in energy efficiency. To address these issues, this paper proposes a comprehensive framework for optimizing the cruising speed of high-speed trains (HSTs) based on track conditions to reduce energy consumed by motion resistances (ECMR). Firstly, a multibody dynamics simulation model of the HST is developed and validated to accurately evaluate ECMR under a range of operation scenarios. Simulation results show that the impact of speed on ECMR varies considerably according to track characteristics. Subsequently, a neural network is constructed to derive the regressive relationship between operation conditions and ECMR. On this basis, an optimization model for the cruising speed under different track conditions is formulated to minimize the ECMR of a railway segment with a given operation time. Finally, the effectiveness of the model is validated through case studies, which demonstrate that ECMR can be reduced by up to 27.85% compared to traditional control strategies. The proposed optimization method can be integrated with current train speed profiles to further enhance energy efficiency. This framework has broad applicability for addressing engineering problems related to train operation optimization.

Operation optimization of hydrogen-containing energy storage cogeneration type microgrid based on improved dung beetle algorithm

Operation optimization of hydrogen-containing energy storage cogeneration type microgrid based on improved dung beetle algorithm

Operation Optimization of Natural Gas Pipe Network Considering Energy Metering

Operation Optimization of Natural Gas Pipe Network Considering Energy Metering

Multiagent Hierarchical Deep Reinforcement Learning for Operation Optimization of Grid-Interactive Efficient Commercial Buildings

Multiagent Hierarchical Deep Reinforcement Learning for Operation Optimization of Grid-Interactive Efficient Commercial Buildings

Coordinated operation optimization strategy for multiple microgrids considering uncertainties in renewable energy output

Abstract In the process of constructing a new power system with a focus on new energy sources, microgrids will be vigorously developed as an effective means of accommodating renewable resources. The synergistic operation of multiple microgrids, merging into a multi-stakeholder framework, is a method to strengthen the overall effectiveness of the system. However, existing Nash bargaining models consider relatively fewer influencing factors. To enhance the reliability and economic viability of microgrids, this paper effectively proposes a model that combines Nash bargaining with two-stage robust optimization. Specifically, the optimization model is divided into two stages. In the first stage, equipment configuration is optimized with the objective of system economy. In the second stage, uncertainties arising from renewable energy sources are considered. Subsequently, the two-stage robust model utilizes C&CG to obtain energy exchange values between microgrids. The Nash bargaining model is then addressed using the ADMM algorithm. Finally, through case simulations, the improvement in the benefits of different microgrids is verified to be in the range of 13% to 24%.

Virtual power plant optimization with demand response and multi-complementary energy planning

Abstract To effectively address the increasing complexity of system operation optimization and resource allocation caused by the continuous enrichment of supply-side resources and the flexible variability of demand-side loads in the energy system, a robust optimization model for virtual power plants considering demand response and multi-energy complementarity is proposed. Handling renewable energy and load uncertainty in virtual power plants by building a two-stage robust optimisation model. The introduction of robust coefficients allows for flexible adjustment of the conservatism of the optimized scheduling. Results indicate that considering demand response and multi-energy complementarity can effectively enhance the system’s robustness and flexibility.

Economic Operation Optimization Under Real-Time Pricing for an Energy Management System in a Redundancy-Based Microgrid

Economic Operation Optimization Under Real-Time Pricing for an Energy Management System in a Redundancy-Based Microgrid

Optimising design and operation of sewage source heat pump: techno-economic and environmental assessment

ABSTRACT Heat pump can be used to recover abundant thermal energy contained in the discharge of municipal wastewater treatment plants. While there are some design standards for common heat pump systems, the design of a sewage source heat pump (SSHP) system is still often based on a fixed heat load and neglects the interdependencies between the equipment sizing and operating parameters. To address the issue that previous design methods have not balanced investment and operational costs well from a global optimisation perspective, this work formulates the simultaneous optimisation of SSHP design and operation as a non-linear programming problem. The proposed model features the consideration of multiple working conditions caused by the impact of ambient temperature variation on the heat load of the SSHP system. The feasibility and potential benefits of the optimised SSHP system are also evaluated by incorporating techno-economic performances and environmental impact analyses into the mathematical framework. A case study is carried out to demonstrate the effectiveness of the proposed methodology. The results show that the total annual cost of the optimally designed and operated SSHP in Harbin could be 9% lower than in Beijing and 39% lower than in Shanghai, suggesting that constructing and running the SSHP system in severe cold regions with great heating demands might be more economical than in less cold regions. The CO2, SO2, and NOx emissions of the SSHP could be approximately 50% less than that of coal-fired boiler heating, and 80% less than that of direct electric heating with coal-fired electricity.

Globally optimal sequencing of optimal reactive dispatch control adjustments to minimize operational losses in transmission systems by graph shortest path, parallel computing, and dynamic programming

Minimizing operational losses in transmission systems through the Optimal Reactive Dispatch (ORD), a non-convex mixed-integer nonlinear programming problem, is crucial for operational cost reduction, resource optimization, and greenhouse gas emission mitigation. Besides all intricacies associated with solving ORDs, transmission system operators encounter the challenge of determining sequences in which ORD control adjustments must be implemented before significant changes occur in generators scheduled power output and system loading. Sequencing ORD control adjustments, in spite of not being novel, remains modestly scrutinized in the literature. This paper introduces a two-phase framework that tackles the globally optimal sequencing of n ORD control adjustments over n! potential paths by solving the ORD to minimize operational losses in transmission systems in the first phase, and optimally sequencing ORD control adjustments employing fast power flow calculations, graph shortest path, parallel computing, and dynamic programming in the second phase. We discuss the framework’s second phase asymptotic time complexity, which is exponential over factorial for brute-force approaches, and its capability to guarantee globally optimal paths toward minimal operational losses determined in the framework’s first phase. ORD control adjustments for transmission systems with up to 27 controllable variables are benchmarked against two mixed-integer nonlinear programming solvers: BARON, a global non-convex solver, and Knitro, a local solver (assuming convexity around local optima). Globally optimal sequences of ORD control adjustments over n! potential paths (more than 1028 for sequencing 27 control adjustments) and average algorithm runtimes validate the straightforward application and, more importantly, effectiveness of such a comprehensive framework.

A Novel Technique for the Optimization of Energy Cost Management and Operation of Microgrids Inspired from the Behavior of Egyptian Stray Dogs

Managing costs in microgrids presents a formidable challenge due to the intricate blend of renewable and non-renewable energy sources that underpin their power generation. Ensuring seamless integration of microgrids with the national grid is pivotal for continuous load demand satisfaction and adherence to liberalized energy market mandates. To address this challenge, this paper introduces a new optimization technique for the Cost Management and Operation System (CMOS) of multi-source microgrids through a smart management unit. The cornerstone of this unit is the Egyptian Stray Dog Optimization (ESDO) algorithm, meticulously designed to optimize operational costs in line with load demands, energy cost dynamics, and generation proficiencies. Rigorous testing of the proposed system was conducted on a multi-resource microgrid using MATLAB, encompassing various operational scenarios. The simulation outcomes consistently highlighted the unit’s capability to achieve optimal cost-efficiency. Comparative analysis with other optimization techniques, particularly Particle Swarm Optimization (PSO), demonstrated the superior performance of the Egyptian Stray Dog algorithm, underscoring its potential as a leading solution in this domain.

Operational parameters optimization of convection air-drying with ultrasound pretreatment of Cyperus rotundus L. tubers by RSM

Drying is an important step in the postharvest processing of agricultural products, as it allows the product to be preserved by reducing water activity to safe values, which stops microorganism growth and enzymatic reactions. In this context, the objective of this study was to assess how various drying temperatures, combined with ultrasonic pretreatment, affect drying rates and essential oil production. Assays were conducted according to a Central Composite Rotational Design, considering two factors with the following intervals: duration of the ultrasonic bath from 0 to 40 min, drying air temperature from 40 to 70 °C. The optimization was carried out using Response Surface Methodology (RSM). The essential oil was extracted by hydrodistillation and its constituents were identified by gas chromatography coupled to mass spectrometry. Histochemical analyses of fresh tubers were performed. The Midilli model was the one that best described the drying kinetics of Cyperus rotundus in all treatments. The drying time of C. rotundus tubers was dependent only on temperature (decreasing linear effect), it was possible to describe the variation in drying time as a function of temperature. It was not possible to adjust a model for ultrasonic bath time or drying temperature that would optimize both essential oil yield and the drying time of C. rotundus tubers. However, in this study, shorter drying times were observed compared to previous studies on C. rotundus tubers drying in the shade at room temperature. A total of 29 chemical constituents have been identified in the essential oil extracted from C. rotundus tubers, of which the majority are: 3,4-dimethyl-3-cyclohexen-1-carboxaldehyde (19.23%); caryophyllene oxide (17.63%); and α-cyperone (9.54%). Histochemical analyses of C. rotundus tubers showed the presence of starch, phenolic compounds, and lipids.

Automatic response framework for large complex natural gas pipeline operation optimization based on data-mechanism hybrid-driven

In recent years, the scale of gas transmission networks has been continuously expanding, and the operational conditions are becoming increasingly complex. This poses higher requirements for the centralized control of the pipeline network operation. Relying solely on the experience of scheduling personnel may not comprehensively address the operational issues within the network. There is an urgent need for efficient automatic response methods (ARM) to assist in formulating operation schemes and ensuring the safe and stable operation of the pipeline network. Therefore, this paper proposes an automatic response framework of large complex natural gas pipeline operation optimization based on data-mechanism coupling to provide operation schemes that ensure safety, stability, and economic benefits. First, two ARM is proposed, namely the Data-Based ARM using operation scheme database, and the Opti-Model ARM based on optimization modeling. Subsequently, the rapid response features of Data-Based ARM and the optimal response characteristics of Opti-Model ARM are combined to establish the Integrated ARM. Finally, these three ARMs are compared and analyzed through a regional natural gas pipeline network in China. The result indicates that the Data-Based ARM can quickly produce a variety of matched solutions but cannot ensure economic optimality. Response solutions obtained through Opti-Model ARM reduce the compressor energy costs by 4.6–10.8 % compared to on-site operational schemes, but they take longer in response time. In contrast, the economic attributes of solutions derived from the Integrated ARM are on par with Opti-Model ARM, but with a 58.7 % improvement in response speed. The proposed Integrated ARM can swiftly and accurately offer economically viable operation schemes tailored to the varying needs of pipeline operators, which can help to address the energy demand challenges of the future, fostering sustainable development.

Operation optimization of propane pre-cooled mixed refrigerant LNG Process: A novel integration of knowledge-based and constrained Bayesian optimization approaches

Liquefied natural gas (LNG) technology, particularly the propane precooled mixed refrigerant (C3MR) process, has demonstrated efficiency and emerged as a distinctive dual-refrigerant technology widely used in LNG production. However, the liquefaction process is the highest energy-intensive stage within its supply chain as it consumes about 8 % of the LNG energy content. Thus, for the first time, this study proposes systematic knowledge-based and constrained Bayesian optimization approaches to identify the optimal operation of the C3MR process. These approaches optimize both the operational parameters (pressures and flow rates) and the composition of the mixed refrigerant with practical equipment specifications and rigorous constraints. The results show that the specific energy consumption (SEC) is reduced to 0.264 kWh/kgLNG, which is 14.6 %, and 26 % lower than the basic C3MR process (unoptimized case) and typical industrial C3MR processes, respectively. In addition, the optimized SEC in this study is 14.5 % to 38.6 % lower than those reported in the literature. At large-scale LNG production (10,000 tons per day), the reduction in the SEC is translated into an 18 MW decrease in compression power, saving approximately 4.7 million $ per year for each C3MR train. Moreover, the coefficient of performance (COP) of the C3MR process was improved by about 15 %, and the CO2 emissions were reduced by 17 % (7 tons per year) compared to the basic C3MR process, indicating potential advancements in large-scale LNG liquefaction processes.

Coordinated optimization of configuration and operation of a photovoltaic integrated building cooling system with electricity and ice storages under source-load uncertainties

Solar photovoltaic integration into buildings provides an effective and attractive method to decrease fossil energy consumption and reduce greenhouse gases emission. This study proposes a flexible photovoltaic integrated building cooling system with electricity and ice storages to address the challenges posed by the instability of solar energy and the integration and coordination of multiple components. Uncertainties in solar irradiance, ambient temperature, and cooling load are characterized using a scenario analysis method that combines Latin Hypercube Sampling and the K-Means++ algorithm. Then, aiming to achieve the minimal total cost, including investment, operational, and CO2 emission costs, a two-layer stochastic optimization model is developed to simultaneously determine the optimal capacities and operational strategies for each device. Genetic algorithm and mixed integer linear programming are, respectively, used to solve the two-layer optimization problem. The comparisons between optimum schemes with and without CO2 emission cost demonstrates that when the base chiller’s capacities are 1179 kW and 1081 kW in cases 1 and 2 respectively, it can independently meet 72.37 % and 61.27 % of the cooling load demand throughout the cooling season. In addition, the optimal capacity of photovoltaics, considering CO2 emission, increases by 109.7 %, and its penetration rate reaches 18.3 %, which is 9.7 % higher than the optimal scheme not considering CO2 emission. However, the annualized investment cost increases by 52.3 %. The developed methods provide effective tools for the system design and operation management of solar energy integrated building cooling system.

Potential use of methane gas from the Villavicencio sanitary landfill, Colombia

The study evaluates the generation and recovery capacity of methane at the Villavicencio Landfill in Colombia, using the LandGEM model. It shows a significant increase in methane generation, rising from about 1.5 million cubic meters in 2010 to over 8.5 million in 2020, indicating a growing urgency to implement effective mitigation measures. It was estimated that by the year 2042, the electrical energy production from methane could be 248.067 kW/day, capable of supplying about 43.705 homes monthly. Additionally, thermal energy generation would be 468.572 kWh/day, useful for industrial processes. Management scenarios were proposed, where, for example, operational optimization could increase electric production to 342.333 kW/day, benefiting more than 60.000 homes monthly. The conclusions highlight the direct correlation between the amount of waste and methane generation, and the significant potential for converting these emissions into energy, pointing towards regional energy self-sufficiency and sustainability. Methane recovery represents a valuable alternative to the dependence on fossil fuels and for the development of a circular economy.

Optimization of reservoir release operation using genetic algorithm method

Genetic Algorithms (GA) are highly effective in optimizing hydropower release parameters, identifying global optima in complex systems, and adapting to varying circumstances, making them ideal for managing hydropower conditions. This study employs GA to optimize hydropower release at Wonorejo Reservoir, Indonesia, aiming to balance power generation with flood control, irrigation, and environmental sustainability. The application of GA improved operational efficiency, resulting in a 10.24 % increase in electricity production. Notably, during the rainy season, the improvement was 27.3 % higher than in the dry season, highlighting GA responsiveness to seasonal variations. These findings demonstrate GA potential as a valuable tool for reservoir management.

Optimal Operation of Virtual Power Plants Based on Stackelberg Game Theory

As the scale of units within virtual power plants (VPPs) continues to expand, establishing an effective operational game model for these internal units has become a pressing issue for enhancing management and operations. This paper integrates photovoltaic generation, wind power, energy storage, and constant-temperature responsive loads, and it also considers micro gas turbines as auxiliary units, collectively forming a typical VPP case study. An operational optimization model was developed for the VPP control center and the micro gas turbines, and the game relationship between them was analyzed. A Stackelberg game model between the VPP control center and the micro gas turbines was proposed. Lastly, an improved D3QN (Dueling Double Deep Q-network) algorithm was employed to compute the VPP’s optimal operational strategy based on Stackelberg game theory. The results demonstrate that the proposed model can balance the energy complementarity between the VPP control center and the micro gas turbines, thereby enhancing the overall economic efficiency of operations.

Optimal energy concept for decarbonisation of sea-buckthorn processing plants

The decarbonisation of the industrial sector is required to achieve the objectives defined within the European Climate and Energy framework, since industry accounts for 37% of the total global energy consumption and for a quarter of global energy system CO2 emissions [1]. In this context, this work aims the decarbonisation of a sea-buckthorn processing plant using structural and operational optimisation for the analysis of proposed energy supply concepts based on the integration of renewable energy sources, energy conversion components and storage systems. Different configurations are evaluated to select the concept that leads to a minimisation of the operating costs and a CO2 emissions reduction of the industrial site with installation costs within the investment range defined and the minimum payback period. Thus, the selected decarbonisation concept is able to achieve a reduction of 25% in operation costs (OPEX) and 31% in CO2 with an investment (CAPEX) of 288.7 T€ and a payback time of 5.8 years. Additionally, the developed methodology can be used for the analysis of decarbonisation concepts for other industrial processes by the integration of an adapted techno- economic definition of the components for each energy concept proposed.

Data based digital twin for operational performance optimization in CFB boilers

The benchmarks of controllable variables for circulating fluidized bed (CFB) boilers with small capacity dependencies on operator experience result in boiler efficiency penalties, increased energy consumption, and performance degradation. This study proposes a digital twin (DT) for a CFB boiler based on data-driven methods to determine the benchmarks of controllable variables by mining historical operating data to save energy, with a coefficient, Cm, proposed for the online evaluation of boiler performance. A hybrid data-driven model integrating kernel density estimation and the K-means++ algorithm was proposed to optimize boiler performance for different coal types, with Cm set as the clustering objective to determine the benchmarks of controllable variables from an operator's perspective. In addition, a classification model adopting an extreme gradient boosting algorithm was presented to identify the coal type online. The proposed DT was applied to an on-duty 150 t/h CFB boiler. The results showed that the proposed DT could identify coal types online with an accuracy of no lower than 94.8 %. Moreover, after the industrial commission of DT, the boiler efficiency increased from 84.41 % to 85.96 % on average, the energy savings reached 151.01 GJ, while steam production increased 59,998 kg over three days, thus highlighting that the proposed benchmarks for controllable variables help enhance the performance of CFB boilers.