Operating System Model Research Articles

Distributionally robust optimal configuration of battery energy storage system considering nodal RoCoF security constraints

The large-scale integration of renewable energy source (RES) exacerbates net load fluctuations, reduces system inertia, limits frequency response capabilities, and leads to uneven spatial distribution of inertia resources. To ensure the economic and safe operation of the system, we propose a distributionally robust optimal configuration scheme of battery energy storage system (BESS) considering nodal RoCoF security constraints to address these issues. First, we describe and model the dynamic frequency response characteristics after disturbances, derive the expressions for the system frequency nadir and maximum nodal RoCoF (MN-RoCoF). Second, we address net load uncertainty using the distributionally robust chance constrained (DRCC) approach and incorporate frequency security constraints into the BESS configuration optimization model, embedding the short-term system operation model into the long-term BESS planning. Then, to tackle the highly nonlinear MN-RoCoF constraint, we propose a divide and conquer-based support vector machine (D&C-SVM), to extract the linear relationship between the BESS virtual inertia constant and MN-RoCoF, reformulating the proposed scheme into a mixed-integer second-order cone programming (MISOCP). Finally, we conducted case studies on the IEEE 39-bus and 118-bus test systems. The results verify that the proposed configuration scheme can ensure that the RoCoF at all nodes remains below 1 Hz/s under the preset disturbances. The proposed D&C-SVM demonstrate 99 % accuracy in managing the nonlinear MN-RoCoF constraint. Moreover, the uncertainty in net load is well managed, with all chance constraints satisfied with a confidence level of over 95 %.

Verification of Operational Forecast Models in Cases of Extratropical Transition of North Atlantic Hurricanes

Abstract Operational forecast models are necessary for the prediction of weather events in real time. Verification of these models must be performed to assess model skills and areas in need of improvement, particularly with different types of weather events that may occur. Despite the devastating impacts that can be caused by tropical cyclones (TCs) that undergo extratropical transition (ET) and become post-tropical cyclones (PTCs), these storms have not been extensively studied in the context of short-term weather prediction. This study completes the first analysis of the Global Forecast System (GFS) and a preoperational version of the newly operational Hurricane Analysis and Forecast System (HAFS) models in forecasting the occurrence of ET and the rainfall associated with ET storms in the North Atlantic basin. GFS’s skill exceeds that of HAFS in forecasting the occurrence of ET, but HAFS tends to have lower track and rain-rate errors in the fully tropical phase of ET storms’ life cycles. Both models simulate rain rates that are often too high near the storm center and fail to capture the larger area of moderate rain rates that greatly contributes to total rainfall accumulation. The discrepancies in rain rates between the models and Integrated Multi-satellitE Retrievals for GPM (IMERG) could be attributed to the models’ tendency to keep storms too intense and too compact with an overly strong warm core, even throughout the ET process.

Receiving Paths Improvement of Digital Phased Array Antennas Using Adaptive Dynamic Range

In contemporary radar technology, the observation and detection of objects with low radar cross-sections remains a significant challenge. A multi-functional radar model employing a digital phased array antenna system offers notable advantages over traditional radar in addressing this issue. Nonetheless, to fully capitalize on these benefits, improving the structure of the receiving path in digital transceiver modules is crucial. A method for improving the digital receiving path model by implementing a matched filter approach is introduced. Given that the return signals from objects are often lower than the internal noise, the analog part of the digital transceiver modules must ensure that its dynamic range aligns with the level of this noise and the weak signal. The output signal level of the analog part must correspond to the allowable input range of the analog-to-digital converter. Improvements in the receiving path to achieve a fully matched model can reduce errors in the phase parameters and amplitudes of the useful signal at the output. The simulation results presented in this paper demonstrate a reduction in amplitude error by approximately 1 dB and a phase error exceeding 1.5 degrees for the desired signal at the output of each receiving path. Consequently, these improvements are expected to enhance the overall quality and efficiency of the spatial and temporal accumulation processes in the digital phased array antenna system. Furthermore, to maintain the matched filter model, we also propose incorporating an adaptive “pseudo-expansion” of the linear gain range. This involves adding a feedback stage with an automatic and adaptive bias voltage adjustment for the intermediate-frequency preamplifier in the analog part of the receiving path. Simulations to qualitatively verify the validity of this proposal are conducted using data from practical operational radar system models.

Operational optimization of a rural multi-energy system supported by a joint biomass-solid-waste-energy conversion system and supply chain

Biomass, as one of the most accessible renewable resources, could reduce dependency on fossil fuels and mitigate associated environmental impacts. With economic development and improved living standards, biomass in rural areas is converted into various forms of energy to meet high energy demands. However, existing studies on rural multi-energy system operations assume energy production from biomass is a given input parameter and consider it to be a fixed constant or subject to upper and lower bounds, independent of the biomass supply chain. Indeed, biomass energy production is significantly influenced by the collection, transportation, storage, and other processes within the supply chain. This work proposes a mixed integer linear program (MILP) for the joint optimization of an integrated rural multi-biomass-solid waste energy conversion system with a source-grid-demand-based biomass-solid waste supply chain (IRMBS-BSC system). The IRMBS-BSC system encompasses three sub-systems: the biomass-solid waste source sub-system, the biomass-solid waste grid sub-system, and the biomass-solid waste demand sub-system. Various biomass-solid wastes are classified, and a source model is established to evaluate their availability. Subsequently, a supply chain model is developed within the biomass-solid waste grid sub-system. Based on the available and transported quantity from the biomass-solid waste source sub-system and grid sub-system, a multi-energy system operation model, considering refined biomass conversion processes, is proposed. A case study involving a multi-energy system that integrates multiple rural community biomass-solid waste supplies is conducted. The results demonstrate that the proposed MILP, which considers the biomass-solid waste supply chain, can reduce energy imports during the load peak periods and can also decrease the overall operational costs of the multi-energy system.

Analysis of Carbon Emission Reduction with Using Low-Carbon Demand Response: Case Study of North China Power Grid

The power sector is the single industry with the largest carbon emission in China, the carbon emission of which accounts for more than 40% of China’s total carbon emissions. In relevant research on the simulation of power system operation, current studies focus more on energy conservation and economical operation, while few consider the low-carbon optimization of the power system from the perspective of carbon emissions. In addition, in relevant research on carbon reduction in the power system, current studies focus more on controlling the direct carbon emission of the source side and less on the indirect carbon emissions of the load side, which focus on the reverse effect of a user’s electricity consumption behavior on the carbon reduction goals of the power system. This article delved into a deterministic simulation model of power system operation based on time series load curves and proposed a carbon reduction mechanism called the low-carbon demand response mechanism, which guides users to actively respond and reduce the carbon emission of power systems. In addition, this article conducted an empirical analysis based on the planning data of the North China Power Grid. To minimize carbon emissions, a simulation of low-carbon optimization operation for the North China Power Grid in 2040 was carried out. Then, based on the simulation results, an analysis of the carbon reduction benefits of low-carbon demand response was carried out. Ultimately, the empirical analysis verified that low-carbon optimization operation and low-carbon demand response technology possess significant carbon reduction potential for the power system.

CHARGES FOR THE USE OF RAW WATER: AN ANALYSIS OF THE PERFORMANCE OF MODELS APPLIED IN WATERS UNDER FEDERAL DOMAIN IN THE CUREMA-MÃE D'ÁGUA WATER SYSTEM

Aiming to explore the potential fundraising, models used for charging the raw waters of São Francisco, Paraíba do Sul, and Doce rivers were applied and evaluated in the Curema-Mãe D'Água water system, located in semiarid Brazil. The methodology consisted of a bibliographical review, documentary research, system operation modeling, application of models, and critical analysis of operations and collections results, comprising a total of 240 months from 2002 to 2021. When applying the Standard Operational Policy (SOP), the water system did not meet the demands 35.4% of the time. The model with the highest collection was the one used in Paraíba do Sul River (R$ 12.71 million/year). The models applied in São Francisco (R$ 1.37 million/year) and Doce Rivers (R$ 3.58 million/year) presented the lowest collections. The Paraíba do Sul River model provided the highest charging with water abstract and consumption elements. The Doce River model, conversely, delivered the highest values through the effluent discharge element. The analysis of charging results indicates that each model has specific applications and outcomes. None of the models consider eventual water scarcity conditions in their formulations.

Navigating the New Age: Exploring Operations and Modern Business Models in Film and Television Production

This article explores the dynamic film and television industry’s operational system and diverse business models. The industry has adapted its strategies in response to globalization, technological advancements, and changing consumer behaviors. The operational system encompasses stages from development to exhibition, ensuring creative ideas translate into compelling content. Business models include the product-oriented, market-oriented, and Business Model 2.0. The digital revolution has ushered in the Long Tail, DIY, and 360-degree models, reshaping distribution and engagement. Applying field theory, the article examines the industry as a structured field where actors vie for different capitals. This multidimensional analysis deepens our understanding of how these models drive the industry’s evolution. The article discusses how the relationship between creating content and making money has become more complex due to the growth of streaming platforms and online distribution. This has led to a need for new ways to make money from content, such as subscription services, ads, and pay-per-view. The industry is focused on finding ways to make consistent profits.

Great Lakes wave forecast system on high-resolution unstructured meshes

Abstract. Wind-wave forecasts play a crucial role in the North American Great Lakes region towards ensuring the safety of communities, enhancement of the economy, and protection of property. Modeling wind waves in closed and relatively shallow basins with complex bathymetry like the Great Lakes is a challenge that is successfully tackled in part by using variable-resolution triangular unstructured meshes with no limits in terms of computational scalability and maximum resolution in the coastal areas. In this paper, we discuss recent advances in developing unstructured mesh capabilities as part of the spectral wave model WAVEWATCH III, in the context of National Oceanic and Atmospheric Administration (NOAA) operational requirements such as model robustness, efficiency, and accuracy. We revisit the history of developments leading to the transition from rectilinear to curvilinear grids and finally to an unstructured mesh version of NOAA's operational Great Lakes wave modeling system (GLWUv2.0). The article describes the development of the operational GLWUv2.0, from mesh design and scalability analysis to validation and verification for hindcast of storm cases and re-forecast using 4 months of retrospective simulations. In closed Great Lakes basins untouched by swell from distant sources, the atmospheric model's direct impact on wave behavior stands apart, showing reduced forecast accuracy over time, while maintaining consistent precision in accurately wind-hindcasted stormy conditions.

The Influence of Terrain Smoothing on Simulated Convective Boundary-Layer Depths in Mountainous Terrain

Many applications rely on a correct estimation of the convective boundary layer (CBL) depth over mountainous terrain, but often these applications use numerical model simulations. Although models inevitably smooth terrain, the amount of smoothing depends on grid spacing. We investigate the behavior of the CBL in coarse- and fine-grid models applied to mountainous terrain by using output from an operational mesoscale modeling system and by performing quasi-idealized simulations. We investigate different areas in different climate zones using different CBL top derivation methods, grid spacing ratios, planetary boundary layer (PBL) schemes, and terrain smoothing. We find that when compared to fine-grid simulations, CBL depths are systematically larger in coarse domains over mountaintops, and to a lesser extent in valleys. On average, differences between coarse- and fine-domains over mountaintops could reach around 10%. In certain locations, differences could be as high as 25%. We attribute the result to terrain smoothing. Similarly, when using a coarse-grid CBL height (relative to mean sea level) interpolated using fine-grid terrain information, there is good agreement with fine-grid CBL depths over mountaintops and less agreement in valleys. Our results have implications for applications that use output from coarse model grids in mountainous terrain. These include inverse modeling studies (e.g., greenhouse gas budget estimations or integrated water vapor transport), PBL evaluation studies, climate research, air quality applications, planning and executing prescribed burns, and studies associated with precipitation over mountainous terrain.

A Transparent Data-Driven Method for Stability-Constrained Load Restoration Considering Multi-Phase Load Dynamics

Load dynamics significantly complex the system stability behavior during the load restoration process after an outage event. It is highly challenging to model the load dynamics and solve the stability-constrained load restoration problem. In this paper, the load dynamic model considering the inrush phase and enduring phase of the cold load pickup effects are firstly modeled. Then, the load restoration optimization model is formulated with stability constraints, ramping constraints, and system operation models. A transparent data-driven method based on optimal decision trees is developed to address all the short-term stability criteria (including frequency, voltage, and transient stability) considering the inrush phase of load dynamics. A set of accurate, linear, and tractable stability constraints are extracted offline and implemented for fast online decision-making. Numerical studies based on the New-England testing system are conducted to validate the proposed method.

Analysis of optimal configuration of energy storage in wind-solar micro-grid based on improved gray wolf optimization

To make full use of the electric power system based on energy storage in a wind-solar microgrid, it is necessary to optimize the configuration of energy storage to ensure the stability of a multi-energy system. This paper analyses the structure and function of the microgrid system, establishes the mathematical model, and analyzes the output characteristics. A double-layer optimization model of energy storage system capacity configuration and wind-solar storage micro-grid system operation is established to realize PV, wind power, and load variation configuration and regulate energy storage economic operation. The mathematical model of the improved gray wolf optimization is constructed, and the typical landscape resource data of a residential district. Comparing the difference between energy storage without an installation and energy storage with improved algorithm, it is shown that the energy storage configuration of the improved gray wolf optimization improves the economy, efficient energy use, and revenue of the whole system.

A linear reduced-order model for the activated sludge process for the integration into a mixed-integer linear energy system optimisation model

Conventional wastewater treatment plants consume significant amounts of electricity. The constant aeration of the wastewater in order to foster the growth of microorganisms or the pumping of wastewater are two examples for energy-intensive processes within a plant. Case studies have shown that switching off blowers and inlet pumps for a certain period of time is possible without a loss in water quality. This yields a potential for wastewater treatment plants to provide demand response (DR) to the power system and thereby increase overall system flexibility. So far, the DR potential has only been quantified for individual plants, while the effects of large-scale DR provision by the wastewater treatment sector for the power system have not yet been studied. One reason for this is the lack of optimisation models which include both the wastewater treatment process and the power system operation in sufficient detail. Our model tackles this gap in the literature by providing a reduced-order linear biochemical model for the activated sludge process within a WWTP that can be incorporated into an operational energy system model. The results show that the effluent concentrations are predicted well by the linear reduced-order model in comparison to the results of the Standard Activated-Sludge model No. 1 (ASM1). Potential model applications are the variation of the airflow rate within a certain range and the variation of liquid influent flow rate to the system, which is a result of electricity load shedding of the inlet pumps and the blowers connected to the activated sludge tank.

Optimizing Energy Storage System Operations and Configuration through a Whale Optimization Algorithm Enhanced with Chaotic Mapping and IoT Data: Enhancing Efficiency and Longevity of Energy Storage Stations

To enhance the charging and discharging strategy of the energy storage system (ESS) and optimize its economic efficiency, this paper proposes a novel approach based on the enhanced whale algorithm. Recognizing that the standard whale algorithm can sometimes suffer from local optima in high-dimensional multiobjective optimization, this study introduces chaotic mapping and individual information exchange mechanisms to address this challenge. The proposed algorithm explores optimal configurations for different energy device placements and capacities through encircling and bubble searches, evaluating various multiobjective functions for optimization. In addition, the algorithm refines both the system operation model and the ESS configuration model, with the objective function being the analysis of the average annual revenue of the ESS. Model testing results demonstrate that this algorithm yields more moderate energy storage (ES) capacity decay, extending operational time to 3,124 days and achieving a full-life cycle benefit of the ESS as high as 1,821,623.68 yuan. Also, our algorithm demonstrates high efficiency, with minimal test time (68.36 seconds) and quick optimization (0.031 seconds per cycle), regardless of problem complexity.

Statistical Analysis of the Air-Cooling Process in a Cowshed

During the operation of the cooling system, a large array of temperature and humidity values was obtained inside and outside the cowshed. On the basis of the obtained data set, mathematical models of temperature, humidity and temperature–humidity indices were developed. The modelled values were relevant to the actual values. The mathematical models were built on the basis of regression analysis of the data set. The data set was obtained as a result of the observations of temperature and relative humidity on one of the farms in Lithuania in semi-insulated box-type cowshed for 244 places in the summer. It was established that the efficiency of the air-cooling system is higher at a higher temperature and lower relative humidity of the air entering the cowshed. Lower humidity values contribute to a more intense decrease in the temperature–humidity index during the operation of the cooling system. The presented mathematical models are useful tools for decision making regarding the choice of cooling system operation models, as they allow one to evaluate the cooling efficiency, taking into account the influence of external factors.

A day-ahead operation regulation method for solar water heating based on model predictive control

The operation performance of a solar heating system is largely influenced by the coupling of outdoor climate conditions and building energy operation patterns, and is characterized by strong randomness and large heating fluctuations, which makes the optimal control rules for solar heating systems in dynamic state. Although the traditional RBC(rule-based control), whose rules is preset, has a fast response speed, the fixed-rules are hardly to make the solar heating system in optimal state. Furthermore, solar heating systems is generally characterized by a large time delay in the heat transfer process. The control point lag occurs in the control signal of the dynamic control strategy. To address these issues, this paper proposes a model predictive control-based (MPC) control strategy for solar heating systems. The method uses seq2seq-LSTM to predict major operating parameters including ambient temperature, solar radiation, the load inside the building for the next 24 h and combines the heating system operation model to simulate and obtain the control signal at the next moment. The results showed that, compared with traditional control strategies including RBC and PID control strategies, the efficiency of solar collector regulated by MPC increased by 9%. Taken the tank temperature at the simulation end as the energy calculation reference point, the solar heating system’s total energy consumption employing MPC is 34.2% lower than that employing traditional control strategy.

Optimized scheduling study of user side energy storage in cloud energy storage model.

With the new round of power system reform, energy storage, as a part of power system frequency regulation and peaking, is an indispensable part of the reform. Among them, user-side small energy storage devices have the advantages of small size, flexible use and convenient application, but present decentralized characteristics in space. Therefore, the optimal allocation of small energy storage resources and the reduction of operating costs are urgent problems to be solved. In this study, the author introduced the concept of cloud energy storage and proposed a system architecture and operational model based on the deployment characteristics of user-side energy storage devices. Additionally, a cluster scheduling matching strategy was designed for small energy storage devices in cloud energy storage mode, utilizing dynamic information of power demand, real-time quotations, and supply at the load side. Subsequently, numerical analysis was conducted to verify that the proposed operational mode and optimal scheduling scheme ensured the maximum absorption of renewable energy, improved the utilization rate of energy storage resources at the user side, and contributed to peak shaving and load leveling in the power grid. The model put forward in this study represents a valuable exploration for new scenarios in energy storage application.

Development of an Optimal Water Allocation Model for Reservoir System Operation

Allocating adequate water supplies under the increasing frequency and severity of droughts is a challenge. This study develops an optimal reservoir system operation method to allocate water supplies from upstream reservoirs to meet the downstream water requirements; validates the proposed optimization model through the system operation of upstream reservoirs; and proposes new water supply policies that incorporate a transformed hydropower reservoir with an add-on water supply function and two multipurpose reservoirs. We use linear programming to develop an optimal water allocation model. This model provides an operational strategy for managing upstream reservoirs with different storage capacities. By integrating the effective storage ratio of each reservoir into the allocation estimation, the model ensures an optimal distribution of downstream water requirements. The results indicated well-balanced, effective storage ratios among the Chungju, Soyanggang, and Hwacheon Reservoirs across varying hydrological conditions. Specifically, during drought years, the average effective storage rates were 20.5%, 20.6%, and 19.07%, respectively. In normal years, these figures, respectively, were 59.3%, 68.6%, and 52.4%, while in wet years, the rates stood at 64.08%, 62.90%, and 54.61%. This study enriches the reservoir operation literature by offering adaptable solutions for collaborative reservoir management and presents efficient strategies for reservoir operations.

Energy Price Decoupling and the Split Market Issue

Load scheduling and dispatch by merit order on electricity generation networks has used a wholesale market electricity system operator model focused on system marginal pricing, in which the spot price of electricity at any point in time is equal to the system marginal cost given by the higher value of the price, which rations demand to capacity or the operating cost of the most expensive plant on the system, which is usually a fossil fuel price. This idea has come under challenge because renewable technologies such as wind power farms or solar power farms are treated as having close to zero operating costs. The challenges, under the general heading of energy price decoupling, include suggestions for operating split markets possibly overseen by a regulator, and the prediction that marginal cost pricing should be abandoned. This review evaluates these in terms of their economic impact, relating them to the policy debates on electricity market reform.

Multi-objective bi-level planning of the integrated energy system considering uncertain user loads and carbon emission during the equipment manufacturing process

Faced with urgent energy crisis and global warming, there is still debate on how to balance the relationship between the economy and environment when planning energy systems. Aiming at this problem, a multi-objective bi-level planning model of the integrated energy system has been developed. In the first stage, the equipment capacity is configured with the objective of minimize carbon emissions from the production phase. In the second stage, the system operation model is a bi-level model, considering the uncertainty loads. In addition, various energy storage strategies for the integrated energy system are simulated, including following the electric load (FEL), following the cold load (FCL), following the heat load (FHL), and price following renewable energy (PFR). In the results of capacity configuration, the photovoltaic installation capacity decreases by 44% with no wind power. Five cases are designed, which contain single and combination strategies. The results show that the system with PFR-FEL can produce carbon emissions and cost 7.3 × 107 kg/year, 4.7 × 107 CNY/year, respectively, while the system with PFR can produce carbon emissions and cost 7.8 × 107 kg/year, 5.2 × 107 CNY/year, respectively. Those results indicate that photovoltaic is more valuable than wind power in Urumqi, and the combination strategy has better performance than single strategy.

Модель функционирования технической системы с учетом организации регламентных работ

Рассмотрены два подхода к организации функционирования и обслуживания технической системы, отличающихся порядком использования запасных типовых элементов замены. Показано, что задача определения необходимого их количества для локальной вычислительной сети не может быть сведена к решению классических задач теории управления запасами. Разработаны математические модели функционирования и технического обслуживания системы, позволяющие определить необходимое количество запасных типовых элементов замены. The article revises two operation and maintenance organization approaches that have different spare elements use order. It is showed that the task of defining the required spare element quantity for local network cannot be derived from classic theory of stock management. The article describes developed math models for operation and maintenance that allow to define the required spare elements quantity.