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Accurate state of charge and capacity estimation is crucial for battery packs in electric vehicles. However, the cell inconsistencies, computational complexity, temperature variations, and complex drive cycles all pose great challenges for the state of charge and capacity estimation of battery packs in field operation. This work aims to develop a framework for state of charge and capacity estimation of battery packs in real electric vehicles with few representative cells and physics-informed machine learning. By analyzing the battery data using a quantity-capacity diagram, the representative cells within the pack can be identified based on the relaxation terminal voltage online. Leveraging the physics information of batteries, the state of charge of the representative cells are monitored in a closed-loop manner. Then, state of charge and available capacity of battery pack are achieved based on the state of only two representative cells. We validate our method by evaluating its performance on the datasets of battery pack in real electric vehicles across different temperatures and drive cycles. The improvements of 83.60 % and 85.13 % in average root mean square errors can be achieved at real drive cycles under 30 °C and 15 °C. The results demonstrate that accurate and robust state estimation of battery packs can be achieved in field operations by utilizing the battery data of only two cells in laboratory conditions. [Display omitted] • Co-estimation of state of charge and capacity of battery pack in real EVs. • Only using state information of two cells based on a quantity-capacity analysis. • Physics parameters are incorporated into data-driven model in a closed-loop. • Experimental validation is conducted on a battery pack from commercial EVs. [ABSTRACT FROM AUTHOR]

The accurate estimation of the state of charge (SOC) is crucial for the safe and reliable operation of Li-ion batteries. In this paper, novel dual-channel deep learning methods were proposed for SOC estimation during dynamic driving cycles. For dual-channel deep learning methods, each channel of the deep learning methods could be a single convolutional neural network (CNN) layer, single long short-term memory (LSTM) layer or sequentially combined structure of CNN and LSTM. After evaluation, it could be found that dual-channel deep learning methods could achieve higher accuracy than conventional single-channel method, however, the model size and floating-point operations (FLOPs) could be either reduced or increased. Among all the dual-channel deep learning methods, CNN + LSTM-CNN + LSTM method could achieve the highest accuracy and the shortest training time with sacrifices in computational complexity. CNN–CNN + LSTM method could achieve higher accuracy than the single-channel deep learning method with reduced computational complexity and model size. As the error of SOC estimation is highly correlated with voltage signals, conditional judgement was then integrated with dual-channel deep learning method to obtain higher accuracy. After integrated with conditional judgement, the averaged error of SOC estimation could be reduced by more than 45% without a substantial sacrifice in computational time. • Dual-channel deep learning methods are proposed for SOC estimation. • Two channels are responsible for processing of transient and moving averaged signals. • Model hyperparameters are optimized by Tree-structured Parzen estimator. • Cross validation method is applied to evaluate the proposed methods. • Conditional judgement is proposed to obtain higher accuracy. [ABSTRACT FROM AUTHOR]

Integrated energy systems offer substantial potential for enhancing renewable energy integration. However, due to the distinct dynamic characteristics between system components, the characterization of system flexibility in integrated energy systems remains a challenge. This further complicates their optimal design, as underestimation or overestimation of flexibility during design can result in unexpected operational performance degradation. This article aims to improve the accuracy of system flexibility characterization in the optimal design of an integrated energy system, and address the conflict between high-precision modeling requirements and the difficulty of solving high-dimensional optimization problems. Specifically, higher temporal resolution operational constraints are adopted to characterize system flexibility, and a variable temporal resolution optimization method is developed, which could effectively reduce computational burden whilst maintaining accuracy. Optimization results demonstrate a significant reduction in solving time, approximately 98.5 %, compared to directly shortening the time step. Moreover, the proposed method ensures optimal results with a reliable system configuration, preventing load loss during operation. Additionally, it significantly reduces total ramping amount and adjustment times of coal-fired units by over 56.0 % and 32.3 % respectively, while achieving a 0.9 % reduction in carbon emissions. Despite these benefits, the total annual costs of the system only experience a minimal increase of 0.6 %. • A variable temporal resolution optimal design method for integrated energy systems is developed. • The dynamic characteristics of various system components are distinguished. • The method enhances the model accuracy whilst reducing the computational complexity. • System layout obtained by the method has better reliability, safety, and lower carbon emissions. [ABSTRACT FROM AUTHOR]

Accurate estimating the state of health (SOH) and state of charge (SOC) is crucial for ensuring the reliable and safe operation of lithium-ion batteries. Traditional methods for the joint estimation of SOC and SOH typically rely on separate models, resulting in a decoupling of their relationship. Moreover, the current convolutional and recurrent-based deep models overlook the inherent connection between local features and global temporal features. These limitations not only hinder the extraction of combined feature information relevant to SOC and SOH during the charging process, but also increase computational complexity and diminish estimation accuracy. To solve these problems, this study proposes a novel SOC–SOH Estimation Framework (SSEF). The framework achieves parameter sharing by segmented training, effectively accounting for the intrinsic coupling between SOC and SOH. This enables a unified joint estimation of the two variables, leading to a substantial enhancement in efficiency. Additionally, a novel charging encoder that alternates between Temporal Convolutional Network and Bidirectional Gated Recurrent Unit is designed. It captures local temporal information and long-term dependencies related to SOC and SOH during charging. SSEF enables precise SOC and SOH estimation for whole-life-cycle lithium-ion batteries, enhancing accuracy and efficiency compared to prevalent methods. • A segmented training strategy is proposed for the estimation of SOC and SOH. • Joint estimation of SOC and SOH during the charging phase for whole-life-cycle lithium-ion batteries. • A novel encoding structure alternating between TCN and GRU, enhancing the model's feature extraction capabilities. [ABSTRACT FROM AUTHOR]

Accurately estimating the state-of-charge (SOC) of lithium-ion batteries under complicated temperature conditions is crucial in all-climate battery management systems. This paper proposes a model-based SOC estimation method for series-connected battery pack with time-varying cell temperature. Systematic battery experiments are conducted to investigate the influences of changing temperature on both cell characteristics and cell-to-cell inconsistencies. A normalized open-circuit voltage (OCV) model is developed and applied in cell Thevenin model to describe the temperature-dependent OCV-SOC characteristic. The battery pack SOC is analyzed considering the effect of passive balance control. Then, a lumped parameter battery pack model is established by connecting cell models in series. To reduce computational complexity, a dual time-scale parameter identification framework is proposed which is supported by an online filtering process of selecting variable reference cell (VRC). An adaptive co-estimator is presented to update pack parameters in dual time-scale using an optimized recursive least squares algorithm, and to estimate the battery pack SOC using an extended Kalman filter. Experimental verifications are conducted under time-varying environmental temperature ranging from −40 °C to 40 °C. Results indicate the established model can well describe the dynamic behavior of battery pack, and the proposed method can estimate the battery pack SOC with considerably high precision. • Investigate cell-to-cell inconsistencies experimentally at different temperatures. • Establish a temperature-dependent normalized OCV model. • Select VRC online and develop a dual time-scale identification framework. • Propose a VFFRLS-EKF based co-estimator considering fast-varying cell temperature. • Prove the accuracy and robustness of proposed method experimentally. [ABSTRACT FROM AUTHOR]

Accurate and efficient analysis of thermal systems is increasingly difficult with the increasing system scale and complexity. Many studies have to sacrifice the accuracy for calculation efficiency by simplifications. This work proposes a categorized and decomposed (C&D) algorithm to simulate thermal systems efficiently and robustly without introducing any simplification. It converts the system simulation into an equivalent optimization problem, which categorizes system constraints according to whether they are linear, and decomposes the optimization problem into a subproblem and a master problem to handle nonlinearity. Besides, variables' gradient information is used to accelerate the convergence and enhance the stability. The proposed algorithm is applied to a supercritical carbon dioxide (sCO 2) recompression Brayton cycle, and results show that it consumes about 8.84% calculation time compared with sequential modular method and requires much fewer initial values (about two orders of magnitude) compared with simultaneous equations method. Compared with the hierarchical and categorized algorithm, the proposed algorithm has a 48% larger convergence range regarding the deviation of initial values, and is more efficient when the initial value deviation is small. The proposed C&D algorithm owns a much higher efficiency and robustness and is a promising tool for complex thermal system simulation. • Propose a categorized and decomposed algorithm for thermal system simulation • Categorize constraints and introduce GBD to reduce computational complexity • Decompose equivalent problem of simulation based on linearity of constraints • Integrate gradient information of unknown variables to accelerate simulation • The algorithm shows high efficiency and robustness compared with SMM and H&C method [ABSTRACT FROM AUTHOR]

Large-scale integration of wind energy and large-amplitude wind power fluctuation in minutes to hours, imposes unprecedented challenges of retaining reliable and secure power systems. Proper detection and precise prediction of wind power ramp events could help power systems better optimize scheduling and mitigate the impact of extreme events. Thus, this paper proposes a novel ramp event forecasting approach named Informer Ordinal Regression Network with Label Diversity (IFORNLD), without distributional assumptions and increasing the computational complexity. The proposed framework retains the superiority of the Informer architecture which can easily handle very long sequences with efficient computation and effective gradient in training, and the strengths of the ordinal regression with label diversity (ORLD) method, which creates a multi-output model that creates diversity with similar computational complexity. The result of case studies on three wind power datasets demonstrate that the proposed framework is superior than some benchmark models, validating its effectiveness and advantages. • A novel probabilistic forecasting model for wind power ramp events is proposed. • The Informer architecture is used to process extremely long wind power sequences without iterative propagation. • The ORLD method is adopted to achieve diversity without increasing computational complexity. • The proposal is capable of simultaneously predicting wind power ramp events under different thresholds. [ABSTRACT FROM AUTHOR]

This paper proposes an effective kernel generalized likelihood ratio test (KGLRT) technique for fault detection in Photovoltaic (PV) systems. The proposed technique is considered as an improvement of the conventional KGLRT with extended online capabilities and lower computational complexity. The proposed online reduced KGLRT (OR-KGLRT) is based on transforming the process data into a higher dimensional space (where the data becomes linear), which makes the kernel-based scheme attractive for modeling nonlinear systems. The performance of the proposed method is evaluated and compared to the conventional KGLRT statistic using a simulated PV data. Both techniques are applied to detect single and multiple failures (including Bypass, Mismatch, Mix and Shading failures). The selected performance criteria are the good detection rate (GDR), false alarm rate (FAR), and computation time (CT). Simulation results show superior detection efficiency of the proposed approach compared to the conventional KGLRT statistic in terms of GDR, FAR and CT. [ABSTRACT FROM AUTHOR]

Abstract Economic dispatch (ED) is a non-convex, non-linear, and non-smooth optimization problem that determines the optimal output power of generation units to meet the forecasted demand from an economic point of view. The objective of this study is to develop and examine the applicability of a newly developed evolutionary particle swarm optimization (E-PSO) algorithm for optimization of the ED problem, where practical constraints, namely, valve-point effects, prohibited operating zones, multiple fuel usage, dynamic ramp rate limits, transmission losses, tie-line capacity, and spinning reserve are considered. In the developed E-PSO algorithm, three operators including mutation, crossover, and selection are applied to enable the search process to skip local optimal points and enhance computational efficiency. To further enhance the performance of the algorithm, an approach is proposed to dynamically adjust the inertia, cognitive, and social weight coefficients to improve exploration and exploitation for smooth convergence. Upon validation of the E-PSO algorithm by means of standard benchmark functions, four case studies including isolated and interconnected power systems are examined and the results are compared with those from other algorithms. The findings show that the proposed features enable the E-PSO algorithm to successfully optimize the ED problem in lower simulation time, while all constraints are met. Highlights • Economic dispatch (ED) is a non-convex, non-linear, and non-smooth problem. • Solution to ED problem determines optimal output power of generation units to meet forecasted demand. • Several enhancements are implemented in PSO algorithm (E-PSO). • E-PSO is validated. • Results demonstrate that proposed enhancements of E-PSO are effective for optimization of ED problem. [ABSTRACT FROM AUTHOR]

Abstract The Darrieus vertical axis wind turbine (VAWT) has been the subject of a large number of recent studies to improve self-starting capability and aerodynamic performance. This study presents a computational fluid dynamics simulation of the vertical axis wind turbine with fixed and variable pitch at different solidities. In order to introduce the best efficiency value of solidity for a variable-pitch vertical axis wind turbine, a computational modeling of two-dimensional transient flow around a VAWT at solidities between 0.2 and 0.8 and turbine with two, three and four blades is conducted. The numerical simulation models the flow field around a turbine rotor by solving the strong nonlinearity of URANS and SST k-ω turbulence model, utilizing the semi-empirical numerical model. To simulate the turbine in a variable pitch method, the user-defined function (UDF) code and the moving mesh method are used. The results show that variable pitch blades with high solidities are preferred when the initial self-starting torque is required. Moreover, the variable pitch blades are capable to produce more torque at high solidities. Therefore, the vortex interaction between upwind vortices and blades in downwind stream is decreased. The results verify that the power reduction of fixed pitch VAWT at high solidities could be solved by utilizing the variable pitch technique. Highlights • A straight-bladeVAWT with variable-pitch control system is investigated at different solidities. • The CFD method based k-w SST turbulence model is employed. • To simulate the turbine in a variable pitch method, the user-defined function (UDF) code is used. • The variable pitch blade produces fewer and smaller vortices, especially at high solidities. • At high solidities, the variable pitch have higher power production and is more controllable. [ABSTRACT FROM AUTHOR]

Modeling energy storage for a renewables-driven grid using every hour of the year gives more insight and higher accuracy, but can be computationally demanding. In this study, we propose a novel and straightforward Critical Time Step technique, independent of the load and generation profiles, to reduce computational requirements with little loss in accuracy for a solar-dominated system. Using the diurnal cycle, specifically the critical times of an hour after sunrise and an hour before sunset, results in an excellent tradeoff of accuracy and computational complexity compared with fixed-time-step approaches. The accuracy and value of the technique are evaluated by comparing results for three weather years. The technique systematically underestimates the capacity expansion needed but differentiates the three weather years with results correlating well with the hourly simulations. Overall, the results show a high accuracy for the Critical Time Step technique in predicting the power expansion of the resources and the energy rating expansion of the storage system for a grid with more than 35% share of solar in the total operational power. The highest error occurred for storage power buildout but did not exceed 10% relative to the 1-hr-resolution simulation for the studied case. • A novel temporal-resolution-reduction technique to reduce computational complexity. • Efficient, diurnal, physics-based modeling of long-duration storage energy capacity. • Independent of input profiles, unlike many aggregating time series methods. • Accurate storage and renewables expansions without using output data. • More accurate than common clustering methods especially for storage energy. [ABSTRACT FROM AUTHOR]

Integrated operation of distribution grids for multiple energy carriers promises hitherto unused synergies in terms of efficient generation, storage, and consumption. A major obstacle to the investment in such systems is their increased complexity, as conventional tools and methods were not designed to capture all relevant technical and economic aspects of hybrid grids. To address this obstacle, this work proposes a methodology to systematically assess multi-carrier energy grids under a holistic scope. By adopting a simulation-based approach that relies on detailed technical and economic models, an efficient and precise evaluation of both short-term (operational) and long-term (strategic) aspects is supported. The methodology enables the assessment of system configurations, control strategies, business models, and regulatory conditions in one coherent approach. As a proof-of-concept, the new methodology is applied to a real-world use case of a hybrid thermal-electrical distribution grid in a central European city. The results are comprehensively discussed to showcase how the various aspects of hybrid energy systems are addressed. The outcomes also demonstrate how this methodology aids the involved stakeholders in understanding the associated risks and potentials, paving the way for early adopters to realize multi-carrier energy distribution grids. [ABSTRACT FROM AUTHOR]

The ultra-supercritical once-through boiler (OTB) unit is an advanced power generation technology with high plant efficiency, high coal utilization, and low emission. However, it is difficult to realize a coordinate control for the ultra-supercritical OTB unit to achieve fast and stable dynamic response during load tracking and grid frequency disturbances, and its complexity and nonlinearity result in the ultra-supercritical OTB unit becoming a multiple-input and multiple-output system filled with severe coupling and interference. According to the affine nonlinear system theory, a nonlinear control method is developed to solve nonlinear decoupled optimal control problems of a nonlinear ultra-supercritical OTB unit. Firstly, the exact linearization technique is used to transform the nonlinear ultra-supercritical OTB system into a linear one and an optimal LQR is designed for the corresponding nominal system. The optimal nonlinear control law can be derived by coordinate conversion and condition feedback and the study can be conducted for reasonable experiments and analysis. Then, compared with the linear PID control and model predictive control, the effectiveness and superiority of the proposed nonlinear optimal control have been verified through different loads disturbances from 50% to 100% using the Matlab/Simulink Toolbox. [ABSTRACT FROM AUTHOR]

Concentrating solar thermal power plants rely in thermal energy storage systems in order to provide a stable power supply. However, they might not been able to meet power plant demands, mainly because of their storage sizes which are restricted due to economic reasons. One way of mitigating this effect is to control in an optimal way the charging and discharging processes. For the design and validation of advanced control strategies, an accurate dynamic model is essential. For this reason, a dynamic thermal energy tank model intended to be used in concentrating solar thermal power plant models is presented in this paper. The developed tank model is validated in charging and discharging processes and also at rest state in order to validate thermal losses dynamics. Simulation results are compared against experimental data from the CIEMAT-PSA molten salt testing facility. [ABSTRACT FROM AUTHOR]

To better facilitate renewable energy systems, the district heating sector is currently changing towards lower temperatures and increased cross-sectoral integration. Seasonal thermal energy storage systems alongside heat pumps have received an increasing attention. However, the operation of a seasonal thermal energy storage system alongside a heat pump is more complex than a short-term thermal energy storage system, and as such, several complex simulation models have been developed. These models have shown to be usable for simulating the operation, but due to their complexity are difficult to implement in simulation models that focus on overall energy system analyses. Based on the operation of an existing seasonal thermal energy storage system, this paper provides a simulation method for a seasonal thermal energy storage system with a heat pump that can be utilized in overall energy system simulation models. The simulation method is based on the proven different operational situations of the seasonal thermal energy storage system. The method is shown to be able to approximate the actual operation on an hourly basis and the yearly thermal losses. [ABSTRACT FROM AUTHOR]

The paper aims at presenting a methodology to choose the proper sizing of an integrated Photovoltaic unit and a storage architecture starting from the knowledge of the load and solar irradiance time profiles. To do this, first a simple logic to manage the charging/discharging of the storage is proposed, then the function representing the overall cost during the whole life of the system is derived. The computation of the cost function for different photovoltaic and storage sizes allows finding the best sizing of the system. Finally, a thorough economic analysis is conducted to provide criteria to check whether the investment is economically advantageous. This is done considering the impact of the economic variables which are more influential on the investment key assessment parameters. The proposed methodology has been validated in different realistic test cases against the results provided by an energy management system, showing an excellent agreement between the approaches. Moreover, the economic analysis has been applied to four realistic situations in order to tailor the microgrid components ratings on the specific test case. [ABSTRACT FROM AUTHOR]

Physics-based models for lithium-ion battery have been regarded as a promising alternative to equivalent circuit models due to their ability to describe internal electrochemical states of battery. However, the huge computational burden and numerous parameters of these models impede their application in embedded battery management system. To deal with the above problem, a reduced-order physics-based model for lithium-ion battery with better tradeoff between the model fidelity and computational complexity is developed. A strategy is proposed to extend the operation from a fixed point to full state of charge range. As the model consists of constant, varying, identifiable and unidentifiable parameters, it is impractical to identify the full set of parameters only using the current-voltage data. To sort out the identifiable parameters, a criterion based on calculating the determinant and condition number of Fisher information matrix ( FIM ) is employed. A subset with maximum nine identifiable parameters is obtained and then identified by nonlinear least square regression algorithm with confidence region calculated by FIM . Compared with the outputs from commercial software, the effectiveness of the battery model and extending strategy are verified. The estimated parameters deviate from the true values slightly, and produce small voltage errors at different current profiles. [ABSTRACT FROM AUTHOR]

The design of a simplified yet accurate physics-based battery model enables researchers to accelerate the processes of the battery design, aging analysis and remaining useful life prediction. In order to reduce the computational complexity of the Pseudo Two-Dimensional mathematical model without sacrificing the accuracy, this paper proposes a simplified multi-particle model via a predictor-corrector strategy and quasi-linearization. In this model, a predictor-corrector strategy is used for updating two internal states, especially used for solving the electrolyte concentration approximation to reduce the computational complexity and reserve a high accuracy of the approximation. Quasi-linearization is applied to the approximations of the Butler-Volmer kinetics equation and the pore wall flux distribution to predict the non-uniform electrochemical reaction effects without using any nonlinear iterative solver. Simulation and experimental results show that the isothermal model and the model coupled with thermal behavior are greatly improve the computational efficiency with almost no loss of accuracy. [ABSTRACT FROM AUTHOR]

Modeling of industrial plants, and especially energy systems, has become increasingly important in industrial engineering and the need for accurate information on their behavior has grown along with the complexity of the industrial processes. Consequently, accurate and flexible simulation tools became essential yielding the development of modular codes. The aim of this work is to propose a new modular mathematical modeling for industrial plant simulation and its reliable numerical implementation. Regardless of their layout, a large class of plant's configurations is modeled by a library of elementary parts; then the physical properties, compositions of the working fluid, and plant's performance are estimated. Each plant component is represented by equations modeling fundamental mechanical and thermodynamic laws and giving rise to a system of algebraic nonlinear equations; remarkably, suitable restrictions on the variables of such nonlinear equations are imposed to guarantee solutions of physical meaning. The proposed numerical procedure combines an outer iterative process which refines plants characteristic parameters and an inner one which solves the arising nonlinear systems and consists of a trust-region solver for bound-constrained nonlinear equalities. The new procedure has been validated performing simulations against an existing modular tool on two compression train arrangements with both series and parallel-mounted compressors. [ABSTRACT FROM AUTHOR]

The TLV (tip leakage vortex) breakdown occurs under some conditions in modern turbines, which leads to extra vortex breakdown losses, but the mechanisms of vortex breakdown and its influencing factors remain unclear. This paper computationally investigates the effects of inlet conditions on the TLV dynamics in an unshrouded turbine. The TLV dynamics analysis is first shown, and then the effects of inlet CBL (casing boundary layer) parameters and flow incidence on the TLV breakdown and loss are investigated respectively. Based on these, a comparison of effects of different inlet conditions on tip leakage mixing loss is examined. Results indicate that the increased CBL thickness and turbulence intensity increases the adverse-pressure gradient in the rear part of the blade tip in varying degrees, but has a minor effect on TLV breakdown location. An increased incidence leads both to the reduction of the initial streamwise velocity on the vortex core and the adverse-pressure gradient in the rear part of the blade tip. Overall, as the incidence increases, the TLV breakdown location moves first upstream and then downstream. All these mean that the TLV initial state is another influencing factor on its breakdown. [ABSTRACT FROM AUTHOR]

The global drive for integration of RESs (renewable energy sources) means they will have an increasing role in power systems. It is inevitable that such resources introduce more operational variability and uncertainty to system functioning because of their intermittent nature. As a result, uncertainty management becomes a critical issue in long-term TEP (Transmission Expansion Planning) in power systems which feature a significant share of renewable power generation, especially in terms of computational requirements. A significant part of this uncertainty is often handled by a set of operational states, here referred to as “snapshots”. Snapshots are generation—demand patterns that lead to OPF (optimal power flow) patterns in the network. A set of snapshots, each one with an estimated probability, is then used in network expansion optimization. In long-term TEP of large networks, the amount of operational states must be reduced to make the problem computationally tractable. This paper shows how representative snapshots can be selected by means of clustering, without relevant loss of accuracy in a TEP context, when appropriate classification variables are used for the clustering process. The approach relies on two ideas. First, snapshots are characterized by their OPF patterns instead of generation—demand patterns. This is simply because network expansion is the target problem, and losses and congestions are the drivers of network investments. Second, OPF patterns are classified using a “moments” technique, a well-known approach to address Optical Pattern Recognition problems. Numerical examples are presented to illustrate the benefits of the proposed clustering methodology. The method seems to be very promising in terms of clustering efficiency and accuracy of the TEP solutions. [ABSTRACT FROM AUTHOR]

Accurate monthly electric load forecasting plays an important role in the development of a smart grid. Although many algorithms have been proposed for load forecasting, these algorithms suffer from poor prediction accuracy or high computational complexity; moreover, they require a large amount of data for training. To address these challenges, in this study, a new seasonal-index model based on grey model optimisation is proposed. First, three trend equations, i.e., linear, exponential, and logarithmic equations, are adopted in the proposed model to fit the development law of electricity consumption. We then use a grey model to predict the seasonal index (SI). Finally, the forecast is obtained by combining the trend forecast value with the SI. To completely evaluate the performance of the proposed algorithm, monthly electric load data from some typical industries of a city in southern China were used in the test. On comparing with some benchmark models, the results indicate that, if the electric-load data of the industries shows the characteristics of trends and (or) seasonality, the proposed algorithm can significantly improve the forecast accuracy even with a small monthly electric load dataset, otherwise, its performance will degrade in a certain level. In particular, we can obtain a mean absolute percentage error (MAPE) of 3.6%, 2.6% and 2.9% for three typical datasets, respectively, and each with only 54 data points. Moreover, the proposed algorithm has the lowest execution complexity, which confirms its advantages over the benchmark schemes. • Proposing a new seasonal index model based on grey model optimisation. • Evaluating the performance of the proposed forecast algorithm with the load data from a south city of China. • Analyzing the limitations of the proposed forecast algorithm by numerical testing. [ABSTRACT FROM AUTHOR]

A novel experimental approach of inter- and intramodel comparisons is conducted with five power market models to give recommendations for modelers working on decarbonization pathways of Europe until 2050. The experiments investigate the impact of model type (optimization vs. simulation), planning horizon (intertemporal vs. myopic), temporal resolution (8760 vs. 384 h), and spatial resolution (28 countries vs. 12 mega-regions). The model type fundamentally determines the evolution of capacity expansion. Planning horizon (assumed foresight of firms) plays a minor role for scenarios with high carbon prices. For low carbon prices in turn, results from myopic models deviate considerably from those of intertemporal models. Lower temporal and spatial resolutions foster wind power via storage and via neglected transmission boundaries, respectively. Using simulation instead of optimization frameworks, a shorter planning horizon of firms, or lower temporal and spatial resolutions might be necessary to reduce the computational complexity. This paper delivers recommendations on how to limit the discrepancies in such cases. • Comparison of five power market models with harmonized input data and model features. • Evaluating the role of model type, planning horizon, spatial and temporal resolution. • Model type decisive for investment decisions. • Myopic modeling hardly changes variable renewable energy expansion but might favor conventional power plants. • Spatial and temporal resolution crucial for ease of variable renewable energy integration. [ABSTRACT FROM AUTHOR]

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. [ABSTRACT FROM AUTHOR]

In optimisation models, the characteristic features of capacity investment and dispatch planning of supply technologies are well explored for electricity systems. Little attention to date has been paid to exploring these features in an adequate manner for heat systems. This paper discusses the implementation of decentralised residential heat supply in optimising energy system models and presents an application in TIMES, an energy system model with broad application. It is shown that standard TIMES does not allow for an appropriate implementation of residential heat systems. Consequently a two-part solution procedure is presented. A special integrated TIMES feature is explored and a tailor-made methodological extension of the TIMES standard code developed. Mixed integer programming is employed to accurately model the capacity planning of residential heat supply systems. Model results yielded by the new approach comply with the realistic capacity planning of heat supply systems in a shortened computation time. A case study for selected residential heat demand categories yields contrasting results for the mixed integer programme and the standard linear programme. The model's extension to cover larger, centralised combined heat and power plants and district heating networks remains for further work; the lower solution times encountered seem promising in this respect. [ABSTRACT FROM AUTHOR]

3D computational analysis was performed to investigate heat transfer and pressure drop characteristics of flow in SWFET (Smooth Wavy Fin-and-Elliptical Tube) heat exchanger with four new VGs (vortex generators), RTW (rectangular trapezoidal winglet), ARW (angle rectangular winglet), CARW (curved angle rectangular winglet) and WW (Wheeler wishbone). The numerical model was well validated with the available experimental results. Numerical results illustrate that vortex generators can bring about further heat transfer enhancement through careful adjustment of the position with respect to the elliptical tube, type and attack angle of vortex generators. The influences of the geometrical factors including attack angles of the winglets (αVG = 15∘,30∘,45∘,60∘ and 75°) and width/length aspect ratio (w/l = 0.5,1.0) of the Wheeler wishbones on enhancing the heat transfer performance of a smooth wavy fin heat exchanger with a three-row staggered elliptical tube bundle are investigated. A parametric study on the winglet vortex generators indicated that for the small attack angle, CARW vortex generators gives better thermohydraulic performance under the present conditions. The best thermal performance with winglet VGs in larger attack angle, was obtained at RTW VGs arrangement. For the SWFET heat exchangers, the WW VGs with w/l = 0.5 provide the best heat transfer performance. [ABSTRACT FROM AUTHOR]

Systems relying on renewable energies demonstrated to be a reliable and sustainable option to electrify isolated communities autonomously. Hybrid systems that combine different energy resources and distribution microgrids are the most efficient design configurations. The design of these systems requires the use of decision support tools, while projects' promoters generally dispose of low design resources. This study presents a heuristic method to design off-grid electrification projects based on wind and solar energies considering micro-scale resource variations and a combination of independent generation points and microgrids. The method considers generation far from users and a pre-selection process is presented in order to screen the initial pool of potentially infinite generation points. Different algorithm versions are evaluated and performances are compared with existing tools: VIPOR, known software for microgrids design, and a recently developed mixed integer linear programming (MILP) model. The proposed heuristic performs better than VIPOR with mean improvements of around 6% and, for communities of more than 40 users, considerably enhances solutions obtained by the MILP model with a much lower computational time (1 min against 1 h). The method is a complete and simple tool that can efficiently support the design of stand-alone community electrification projects with renewable energies. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we formulate a RLOPF (risk-limiting optimal power flow) problem for systems with high penetration of wind power to address the issue of possibly violating the security constraints in power systems due to the volatility of wind power generations. To cope with the computational complexity of the proposed RLOPF problem, we propose a computationally efficient RLOPF algorithm assisted by the off-line constructed probability distribution models for bus voltage magnitudes and transmission line real power flows. We apply the proposed RLOPF algorithm to the RLOPF problems on a 26-bus power system for two cases of significantly different re-dispatching percentage share for non-renewable power generations to compensate the volatility of wind power generations. The test results reveal that the performance of all solutions obtained by the proposed RLOPF algorithm of various step-sizes in both cases meet the required probability 0.95 on satisfying the security constraints in the presence of variable wind power generations, and the CPU time consumption are mostly within 1 s. We also test the performance of conventional OPF (optimal power flow) solution on both cases, and the resulted probability are all smaller than 0.783. These test results demonstrate the merit and the computational efficiency of the proposed RLOPF algorithm. [Copyright &y& Elsevier]

Abstract: This paper presents teaching–learning-based optimization algorithm for solving MAED (multi-area economic dispatch) problem with tie line constraints considering transmission losses, multiple fuels, valve-point loading and prohibited operating zones. TLBO (teaching–learning-based optimization) is one of the recently proposed population based algorithms which simulates the teaching–learning process of the class room. It is a very simple and robust global optimization technique. The effectiveness of the proposed algorithm has been verified on three different test systems, both small and large, involving varying degree of complexity. Compared with differential evolution, evolutionary programming and real coded genetic algorithm, considering the quality of the solution obtained, the proposed algorithm seems to be a promising alternative approach for solving the MAED problems in practical power system. [Copyright &y& Elsevier]

To reduce computational complexity, macro-energy system models commonly implement reduced time-series data. For renewable energy systems dependent on seasonal storage and characterized by intermittent renewables, like wind and solar, adequacy of time-series reduction is in question. Using a capacity expansion model, we evaluate different methods for creating and implementing reduced time-series regarding loss of load and system costs. Results show that adequacy greatly depends on the length of the reduced time-series and how it is implemented into the model. Implementation as a chronological sequence with re-scaled time-steps prevents loss of load best but imposes a positive bias on seasonal storage resulting in an overestimation of system costs. Compared to chronological sequences, grouped periods require more time so solve for the same number of time-steps, because the approach requires additional variables and constraints. Overall, results suggest further efforts to improve time-series reduction and other methods for reducing computational complexity. • Dependence on renewables and storage reduces the adequacy of time-series reduction. • Implementation as a re-scaled sequence prevents loss of load, but overestimates costs. • Further efforts to improve time-series reduction or other methods are needed. [ABSTRACT FROM AUTHOR]

Abstract: While software simulations in the building technology field are essential for designing efficient systems, for specific applications, the tools currently available are computationally too expensive. This paper presents a novel approach for the fast computation of accurate simulation results and demonstrates its efficacy for solving a multiobjective optimization problem. A surrogate modeling approach is proposed, which is based on an effective interpolation algorithm and an optimal distribution of sampling nodes. The highest accuracy is shown to be achieved by a Poisson disk node distribution and an interpolation function developed from a cubic radial basis function. Furthermore, since global interpolation algorithms in high-dimensional spaces are fundamentally inefficient, a local adaptation is developed. The fidelity of the surrogate model is shown to generalize to higher dimensions with respect to the average number of nodes per dimension η s . The application potential is tested on a case study consisting of a multiobjective design optimization problem. The results show that the surrogate and the fine model converge to the same solution. Being 150 times faster than the original model, the surrogate model is thus a valid choice for computationally demanding applications. [Copyright &y& Elsevier]

Abstract: To provide electric utility service of desired quality at the lowest possible cost, voltage drop and power loss reduction is crucial for distribution network. Distribution systems with Distributed Energy Resources (DER) have shown an enormous potential for power loss and voltage drop reduction. This paper investigates existing islanding detection techniques for feeder performance and proposes a new islanding detection algorithm (NIDA) for node voltage profile improvement and power loss reduction in the distribution network. The NIDA has been validated with three case studies. It has been investigated for its capabilities to improve the feeder performance in electric distribution system. The proposed algorithm outperforms the conventional approaches, which have many limitations to detect the islanding operation. The non-uniform distribution of electric loads, unity power factor, complexities in the design of control interface and functioning of the system in multi-DER environment are the most common obstacles in implementation of existing islanding detection techniques. NIDA is capable of handling the above mentioned complexities. The simulation results show that NIDA can detect islanding formation during the fault condition. The DER(s) provide uninterruptible power supply to sensitive load in the island, maintaining the nodes voltage and the power loss within the permissible limits. [ABSTRACT FROM AUTHOR]

Because of the problems of the various sources of uncertainties, wind power point forecasting models may lead to risks for power system operation and planning. The uncertainty sources include input uncertainty, model uncertainty, parameters uncertainty, and so forth. In general, in order to enhance the reliability and credibility of the wind power forecasting model outputs, prediction interval forecasting instead of point forecasting is used and provides a range of future values. However, for wind power prediction interval forecasting models, conventional methods for building prediction interval suffer from the assumption of data distribution, large computational complexity or difficult computation, resulting in generating inappropriate prediction interval. Besides, because of increased uncertainty from longer forecasting steps, constructing effectively the prediction interval of multistep-ahead forecasting is an important issue. Therefore, in this paper, we develop a center-of-concentrated-based neural network method for building the prediction interval of wind power forecasting systems in order to avoid the restrictive condition of data distribution, and the problem of difficult computation. Moreover, simulation results using different neural networks and heuristic optimizations are compared and analyzed to illustrate the effectiveness and feasibility of the proposed method. [Display omitted] • A center-of-concentrated-based prediction interval method is proposed. • The method is used to find the prediction interval of wind power forecasting. • The method considers the output concentration, the coverage probability and the prediction interval bound. [ABSTRACT FROM AUTHOR]

Combined bidding strategies can reduce the imbalance penalty caused by wind power uncertainty. Power generation stakeholders can benefit from the combined bidding with a suitable coalition. However, the coalition formation method for wind farms with other resources is less reported. In this paper, a sequential coalition formation method is studied. The relationship constraint of wind-thermal combined bidding in a day-ahead market is described by the star graph. Technique for order preference by similarity to ideal solution (TOPSIS) is used to rank the thermal power plants considering multiple evaluation criteria. On the basis of the star graph and the TOPSIS, the coalition structure graph (CSG) is simplified. An actual wind farm and five thermal power plants in northeast China are utilized to verify the effectiveness and practicability of the proposed method. The results show that the proposed method can reduce the computational complexity of coalition formation. When wind power deviations are 0.1, 0.2, 0.3, and 0.4, the retrieval coalition structures are only 4.93%, 4.93%, 6.40%, and 7.39% of those in the basic CSG. The utilities of the formed coalitions have increment rates of 1.30%, 2.79%, 4.13%, and 5.45% compared with those under separated bidding in different wind power scenarios. • A sequential coalition formation method is proposed based on TOPSIS. • CSG is simplified with the star graph and sequential coalition formation method. • A fair utility allocation is guaranteed by the nucleolus solution. • Effectiveness and practicability are verified by actual data of northeast China. [ABSTRACT FROM AUTHOR]

The performance of supersonic ejectors is mainly limited by the choking of the flow. Below a critical pressure, the entrainment capability of the ejector remains indeed constant. As the coefficient of performance of ejector-based heat driven refrigeration cycles (HDRC) is directly linked to the ejector entrainment ratio, it constitutes a real limitation towards better overall cycle performance. The compound-choking criterion has recently demonstrated to better explain this limitation compared to the Fabri-choking theory for supersonic ejectors working with air. However, as all ejector-driven HDRCs work with real gas refrigerants, this criterion needs to be extended to real gases. In the present work, the compound-choking criterion is first extended analytically to real gases. Wall-resolved turbulence modelings for supersonic ejectors working with R134a and blends of R134a and HydroFluoroOlefeins (HFO) are then performed. An improved thermodynamic model is finally used to demonstrate the superiority of the compound-choking criterion over the Fabri-choking one for supersonic ejectors. Results show that the β parameter constitutes an unambiguous indicator of the ejector choking condition, much better suited to determine the operating regime than the sonic line criterion. Moreover, the mean error in entrainment ratio predictions is reduced from 17.54 % to 5.28 % when using the compound-choking theory instead of the Fabri-choking one, with virtually no difference in the complexity or computational cost of the model. • The compound-choking theory is applied to single-phase supersonic ejectors. • The compound-choke theory leads to more accurate supersonic ejector models. • The condition β < 0 is a clear indicator of the supersonic ejector operating regime. • The theory is applicable to different operating conditions and working fluids. [ABSTRACT FROM AUTHOR]

The pseudo-two-dimensional (P2D) electrochemical model can give insight into the internal behavior of lithium-ion batteries, which is of great significance for intelligent battery management. However, the computational complexity of the P2D model greatly limits its onboard application. This paper devotes to develop a reduced-order electrochemical model (ROEM) with high fidelity yet low computational cost. First, for the simplification of the electrolyte diffusion process in batteries, domain decomposition technique is applied to divide the whole cell sandwich into two computation domains. The polynomial approximation method is adopted to describe the electrolyte concentration distribution in each domain, so that the electrolyte diffusion process is represented by two independent state variables. Next, combining the simplified electrolyte diffusion process and other dynamics in lithium-ion batteries, the complete ROEM is obtained as a five-state diagonal system. Finally, the prediction accuracy of the ROEM on electrolyte concentration, electrolyte diffusion overpotential and terminal voltage is verified by comparing with the P2D model and experimental results. Moreover, the proposed ROEM has an ideal computing speed for real-time application, which is at least 600 times faster than the rigorous P2D model. • Domain decomposition and polynomial approximation are employed for model order reduction. • The electrolyte diffusion process is reduced to a two-state diagonal system. • The reduced order electrochemical model (ROEM) is a five-state diagonal system. • The prediction accuracy of the ROEM is verified under various conditions. [ABSTRACT FROM AUTHOR]

Traditionally, models pooling flexible demand and generation units into Virtual Power Plants have been solved via separated approaches, decomposing the problem into parts dedicated to market clearing and separate parts dedicated to managing the state-constraints. The reason for this is the high computational complexity of solving dynamic, i.e. multi-stage, problems under competition. Such approaches have the downside of not adequately modeling the direct competition between these agents over the entire considered time period. This paper approximates the decisions of the players via 'actor networks' and the assumptions on future realizations of the uncertainties as 'critic networks', approaching the tractability issues of multi-period optimization and market clearing at the same time. Mathematical proof of this solution converging to a Nash equilibrium is provided and supported by case studies on the IEEE 30 and 118 bus systems. Utilizing this approach, the framework is able to cope with high uncertainty spaces extending beyond traditional approximations such as scenario trees. In addition, the paper suggests various possibilities of parallelization of the framework in order to increase computational efficiency. Applying this process allows for parallel solution of all time periods and training the approximations in parallel, a problem previously only solved in succession. • A single-level model for competition between aggregators of flexible demand, generators and hybrids combining both. • A versatile and computationally efficient framework to derive Nash equilibria for stochastic multistage models. • A decomposition concept to parallelize training of agents and time periods. • A bi-level model to transform area based market clearing results into physical transmission grid results. [ABSTRACT FROM AUTHOR]

Uncertain load behavior affects the energy system operation scheduling in battery-integrated energy systems (BIES) due to the direct relation between optimal battery scheduling and load demand in the forthcoming time interval. In this paper, the optimal schedule of BIES is investigated with considering load demand uncertainty. The previous works have introduced a cost function and found optimal strategies using evolutionary optimization algorithms, which may fall in local minima. Against the previous works and in order to avoid falling in local minima, a closed-form expression is presented in this paper by analyzing a mathematically-modeled forecast error and considering battery characteristics. In the proposed method, the optimal battery charging/discharging state is analytically obtained based on forecast error and system parameters without the need to use numerical approximation algorithms. The applicability of the proposed scheduling strategy in the real energy system is shown by applying it to online optimal control of the battery in a real system with load demand forecast error, and also by comparing the results with previous methods. The proposed approach leads to the globally-optimal solution for battery scheduling and also shrinks the computational complexity of the BIES scheduling problem. • Analytical approach to optimal control of battery-integrated energy systems. • Load demand uncertainty effect on battery optimal control. • Reducing the computational complexity of battery-integrated energy systems. • Presenting a closed-form expression for optimal battery control. [ABSTRACT FROM AUTHOR]

Research on manufacturing scheduling has historically emphasized production efficiency. With rising environmental consciousness, manufacturing companies are paying increasing attention to energy efficiency on the shop floor. Manufacturing consumes a large amount of electricity globally. The mismatch between electric supply and demand has been a huge problem, which even becomes worse when renewable energy becomes more popular. The time-of-use (TOU) pricing policy is a widely used demand response (DR) approach, trying to align demand to supply. This paper considers energy-efficient scheduling of a single batch processing machine with non-identical job sizes and release times under a TOU electric tariff so as to simultaneously minimize total electricity cost, a criterion of environmental and energy sustainability, and makespan, a criterion of productivity. A mathematical formulation is developed to optimize electricity cost and makespan. Due to computational complexity, a hybrid multi-objective meta-heuristic algorithm is developed to find the Pareto front. Two constructive heuristics are presented to group jobs into batches. Two different approaches are presented to improve total electricity costs. The performance of the proposed model and algorithms is evaluated through extensive numerical experiments. Production managers can use the model and algorithms provided in this work to make a trade-off between productivity and sustainability. • A new energy-efficient scheduling tool for batch processing machine with arbitrary job sizes and release times. • Consideration on the time-of-use pricing policy for tradeoff between productivity and total electricity cost. • A fast hybrid multi-objective meta-heuristic algorithm to find the Pareto front for practical applications. [ABSTRACT FROM AUTHOR]

One of the most important steps in designing wind turbines is to find airfoils with better performance. One of the major hurdles with parameterizing the entire airfoil shape, However, the large computational cost and complexity impose a major hurdle to analyze the airfoils in the optimization loop with parameterizing the entire airfoil shape. In order to solve this problem, GABP artificial neural network is used to optimize the design of airfoil. Bessel polynomial was used to simplify the airfoil curve to 8 pairs of coordinates. Then 1446 arrays were used as training set and 50 sets of data are used as test set. Finally, the ANN which can predict the lift coefficient and the maximum lift-drag ratio of airfoil is trained. The accuracy of the two parameters is 90%. In this paper, the characteristics of Bessel curve are used to train the neural network to optimize the airfoil. By adjusting the control points, the new airfoil can be created. It takes 168 s and has been adjusted 529 times, and the optimization target is successfully achieved. The method in this paper can provide new ideas for airfoil optimization and greatly reduce the optimization time. Furthermore, with the sufficient data input, the research can contribute to an efficient prediction and optimization on other airfoil performance. • Airfoils fitted with Bessel polynomials have fewer control points. • ANN training data is composed of control points of Bessel curve. • The accuracy of ANN in predicting maximum lift-drag ratio is 90%. • New airfoil obtained by adjusting control points. • Aerodynamic Performance of New Airfoil Predicted by ANN. [ABSTRACT FROM AUTHOR]