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This paper is the second in a two-part series on the concavity-convexity of the distribution system security region (DSSR). In Part I, the concave-convex characteristics and mechanism of DSSR are revealed by observing many case grids. In Part II (this paper), the concave-convex principle of DSSR is revealed through mathematical deduction. Firstly, the relevant concepts of sub-regions are proposed, and a DSSR model based on the union of sub-regions is developed. Secondly, the concave-convex principle of DSSR is proposed. The proposed criteria show that the security region of a distribution system with a single load transfer path for each feeder is convex, while that with multiple load transfer paths for some feeders can be concave or convex. Thirdly, the proposed criteria are deduced mathematically. Fourthly, based on the criteria, a method is proposed to judge whether a DSSR is concave or convex. Fifthly, the proposed criteria and method are verified through several cases, and the DSSR concavity-convexity of real distribution systems in China is also analyzed. Finally, the applications of DSSR concavity-convexity are illustrated. This work lays a mathematical foundation for the research of the topological properties of DSSR and is also useful for the application of DSSR. • The DSSR mathematical model based on the union of sub-regions is developed. • Three criteria are proposed and proved, revealing the concave-convex principle of DSSR. • A judgment method for DSSR concavity-convexity is proposed based on these criteria. • The DSSR concavity-convexity of real distribution systems in China is analyzed. • The application values of DSSR concavity-convexity are demonstrated. [ABSTRACT FROM AUTHOR]

Periodic cleaning of photovoltaic (PV) panels, such as every three months, is a common industry practice. However, this fixed period may not be optimal for maximizing the profit of a PV power generation system, due to numerous time-variant influencing factors, such as weather, temperature, etc. To increase the overall profit of a solar farm, it is highly desirable to have a flexible cleaning schedule that considers time-variant influencing factors. For this requirement, a rolling-horizon cleaning recommendation system is presented in this paper. Within this cleaning recommendation system, a prediction model and profit model are proposed. The prediction model, called the ensemble long-term and nonlinear autoregressive, can provide a time-variant future horizon by analyzing and compressing the time-variant characteristics in historical information. The profit model based on mathematical constraints, can process time-variant future horizon output from prediction model to generate a flexible optimized recommendation for cleaning schedule. The effectiveness of the proposed system is validated in real farms and all data used in this paper is collected from real world. The two case studies in experiments show that the profit improvement can reach up to 6% and 30%, respectively. [Display omitted] • A rolling-horizon cleaning recommendation system for PV panels. • A novel long-term and nonlinear predictive method. • A concise profit model for easy deployment in industry. • Flexible cleaning schedule considering time-variant characteristics. • Improvements of 6% and 30% in two different real farms, respectively. [ABSTRACT FROM AUTHOR]

• The mathematical model of ASC and its calculation method are proposed. • The model of optimized ASC (OASC) is proposed, which can further exploit the ASC. • The ASC obtained is complete and precise as it considers all load growth patterns. • The ASC obtained can be used for real-time evaluation of supply capability margin. This paper proposes the mathematical model, and calculation method of available supply capability (ASC) for distribution systems. Firstly, the mathematical model for ASC is established, considering all allowed load increments from the present operating point to the security boundary of a distribution system. Secondly, the calculation method for the ASC model is proposed. The results of the proposed method can describe the available supply capability of a distribution system completely, including not only the ASC values, but also some other important data, such as all load growth patterns and cross-boundary points when the system reaches its security boundary. Finally, an IEEE RBTS test system with DGs is used to demonstrate the proposed models and method. Similar to the ATC of the transmission systems, the proposed ASC is an important operational index that can help operators accurately evaluate the supply capability margin of a distribution system. The ASC relevant operational guides are also given in this paper. This work lays the foundation for establishing the ASC theory for distribution systems, which corresponds to the ATC theory for transmission systems. [ABSTRACT FROM AUTHOR]

• A realistic PHS model is proposed for energy management applications. • Flow rate, water level, evaporation and precipitation affect model performance. • The proposed PHS model is validated with experiments in different operating points. • The error of the estimated stored water is reduced from 13.17% to 0.74%. This paper proposes a comprehensive pumped hydro storage model with applications in microgrids and smart grids. Existing models within current literature produce high error in calculating stored energy since some critical parameters are ignored. Thus, they are not suitable choices for energy management applications. Accordingly, the main objective of this study is to provide a more realistic model by estimating all the essential parameters in the system. First, all the losses due to the pump, pipes, and fittings are modelled. Next, a water balance approach is used to calculate the volume of water in the upper reservoir considering inflow, outflow, precipitation, and evaporation. Finally, the turbine power is calculated as a function of the water level in the reservoirs, considering the hydraulic losses of the turbine, pipes and fittings. The proposed model is validated using the experimental results of a physical system. The accuracy of the model is compared with other established models. The results demonstrate that the proposed model decreases the error of the estimated stored energy from 13.17% to 0.74%. Moreover, this study shows the capability of the model to simulate different configurations. The model provided in this paper assists researchers in the field and is of benefit to engineers in designing, sizing, and managing pumped hydro storage systems. [ABSTRACT FROM AUTHOR]

The proportion of spot trading and short-term contracts has gradually increased in the rapidly growing LNG market, leading to more uncertainties in LNG demand and prices that significantly challenge LNG shipping decisions. In this paper, a mathematical model is developed to minimize transportation costs from multiple exporting countries to multiple importing countries under demand and price uncertainty, both of which are results of LNG market liberalization. A Gaussian process metamodel based simulation optimization method is proposed to solve the fleet planning problem, accounting for various uncertainties. A case study is given to illustrate the effects of uncertainties on the optimal solutions. The results demonstrate that shipping companies may purchase fewer ships and charter more ships to hedge against the risk of uncertainty. LNG market liberalization can significantly reduce its transportation costs and carbon emissions. The results suggest the need for further LNG market liberalization and measures to mitigate uncertainties for shipping companies, such as removing destination clauses. • More price and demand uncertainties stemming from LNG market liberalization challenge fleet planning decisions. • Consider a fleet size and composition optimization problem with a mathematical model. • A Gaussian process based simulation optimization method solves the fleet planning problem. • Shipping companies may purchase fewer ships and charter more ships to hedge against the risk of uncertainty. • LNG market liberalization can significantly reduce transportation costs and carbon emissions. [ABSTRACT FROM AUTHOR]

Abstract: Papermaking is known as an energy-intensive, but not always efficient industry. This is at least partly due to the fact that the majority of papermaking technologies and procedures were established at a time when energy was both cheap and plentiful. A considerable fraction of the energy required for papermaking is consumed in removing water by the use of steam in the paper machine’s drying section. It is also known that even a small reduction in steam use can result in a significant reduction in production costs and environmental effects. To date, a great deal of research has been undertaken, aimed at improving the performance of the drying section by making more efficient use of dryer steam. This paper investigates a new approach to reducing thermal energy use in paper making by seeking to enhance the amount of water removed in sections of the machine prior to the drying section. The proposed method is focused on sequential modelling of the effect of vacuums used in the forming section on the thermal energy consumption in the drying section. The primary models explain how different vacuum pressures can affect the flow of water from the sheet in the forming section whilst the secondary models describe the effect of increased drainage on the steam requirements in the dryer. Operational data from a UK paper mill are used to illustrate the proposed method. The models developed can have subsequent application to optimising the use of thermal energy in paper making. [Copyright &y& Elsevier]

This paper presents a dynamic organic Rankine cycle waste heat recovery (ORC-WHR) Simulink® model and an engine model for heavy-duty diesel applications. The dynamic, physics-based ORC-WHR system model includes parallel evaporators, flow control valves, a turbine expander, a reservoir, and pumps. The evaporator model contains an enhanced pressure drop model, which calculates pressure drop for each working fluid phase via a linear relation to the axial location inside each phase. The ORC-WHR component models parameters are identified over large range of steady state and transient experimental data, which are collected from an ORC-WHR system on a 13 L heavy-duty diesel engine. The component models are integrated into an entire system model and the boundary conditions, inputs and outputs for the individual models are described. A GT-POWER® engine model and its transient validation is presented. The speed and torque profiles of a long-haul, constant speed variable-load heavy-duty cycle are processed through the engine model to produce the exhaust and recirculated exhaust gas transient conditions relevant for the ORC model. The ORC-WHR system then simulated over these highly transient engine conditions. Overall, this paper provides detailed guidelines for ORC-WHR system modeling, model calibration, and component models integration. [ABSTRACT FROM AUTHOR]

The distribution system is demanded to be more efficient, more flexible and more intelligent due to the continued growth of the electricity loads, the high efficiency of energy utilization and the environmental protection. The traditional distribution systems are facing the challenge of evolving from passive networks to the active distribution networks (ADN) with the integration of multiple controllable resources. This paper presents an optimal operation and schedule model of the ADN, considering a variety of controllable resources such as distributed generations, battery storages and interruptible loads. To solve the optimization problem, a modified fuzzy adaptive PSO assisted by Kriging model (KMA-MFAPSO) is developed in this paper. In KMA-MFPSO, a novel constraint handling technique (CHT) based on the PSO is proposed to handle the constraints effectively. In addition, under the premise of ensuring the accuracy of the calculation, the Kriging model is used in KMA-MFAPSO to calculate the power flow the ADN approximately, which greatly speeds up the solving process. Finally, the effectiveness of the proposed algorithm is tested on a modified IEEE-123 system. The optimal results obtained by the proposed method are compared with the results obtained by using other solving algorithms. The simulation results indicate that KMA-MFAPSO is very robust and fast to solve the optimization problem so that it can be used in practical systems. [ABSTRACT FROM AUTHOR]

The thermal performance of a novel solar air collector with metal corrugated packing in the buildings of cold regions is studied in this paper. Mathematical models are developed to investigate the thermal performance of the collector and the results are verified by experiments. The hydraulic analysis is conducted experimentally to study the pressure drops of the air flows in the corrugated packing. Effects of the structural and operating parameters, such as the collector width, height, specific surface area and solar radiation intensity, ambient air temperature, air inlet temperature and velocity are studied to optimize the thermal performance of the collector. Comparisons are conducted among the metal corrugated packing solar air collector, the unglazed transpired solar collector, the glazed transpired solar collector and the packed bed solar collector with iron chips. The results indicate that the metal corrugated packing solar air collector is more appropriate to be used in the rural buildings of cold regions for its advantages of large heat transfer area, high heat transfer coefficient and good economic performance. [ABSTRACT FROM AUTHOR]

Understanding the contribution a resource can make to the power system could indicate where its value lies. This paper estimates the capacity value of a load-shifting resource which is capable of providing multiple services. The capacity value represents the contribution of a resource to generation adequacy and an understanding of this contribution is important to compare how different power system resources can assist power system operators and planners. Additionally, policy-makers and market operators need an appreciation of the capacity value of different resources in order to design capacity remuneration mechanisms. A building energy model coupled with a power system model, co-optimizing the supply-side and the demand-side, is employed in this paper to estimate the capacity value of a specific load-shifting resource. The resource examined is electric thermal storage heating devices for space and water heating. Ireland is used as a test case. It was found that these load-shifting devices can provide an adequacy contribution to the power system and thus have a capacity value. The capacity value, for the Irish case, can be up to 26% for the DR resource in question for the given year but the values are typically much lower due to operational constraints (reserve provision) and due to occupancy profile impacts. The results highlight the need for holistic modeling of demand response resources, as well as the need for additional work for different load-shifting resources and more data. [ABSTRACT FROM AUTHOR]

State of charge (SOC) estimators with online identified battery model have proven to have high accuracy and better robustness due to the timely adaption of time varying model parameters. In this paper, we show that the common methods for model identification are intrinsically biased if both the current and voltage sensors are corrupted with noises. The uncertainties in battery model further degrade the accuracy and robustness of SOC estimate. To address this problem, this paper proposes a novel technique which integrates the Frisch scheme based bias compensating recursive least squares (FBCRLS) with a SOC observer for enhanced model identification and SOC estimate. The proposed method online estimates the noise statistics and compensates the noise effect so that the model parameters can be extracted without bias. The SOC is further estimated in real time with the online updated and unbiased battery model. Simulation and experimental studies show that the proposed FBCRLS based observer effectively attenuates the bias on model identification caused by noise contamination and as a consequence provides more reliable estimate on SOC. The proposed method is also compared with other existing methods to highlight its superiority in terms of accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Several studies have shown that the revenue of wind power generators on spot markets (“market value”) diminishes with increasing deployment. This “value drop” is mostly observed in power markets that are dominated by thermal power plants, such as in Germany. This paper assesses the wind market value in power systems where hydroelectric stations with large reservoirs prevail, such as in Sweden. Due to their dispatch flexibility, such hydropower compensates for wind power output variability and thereby mitigates the wind power value drop. The market value of electricity from wind declines with penetration in both types of power systems, but it tends to decline at a slower rate if hydropower is present. This paper presents empirical evidence on the relevance of this effect derived from market data and numerical model results. Our results indicate that when moving from 0% to 30% wind penetration, hydropower mitigates the value drop by a third. As a result, 1 MWh of wind energy is worth 18% more in Sweden than in Germany. Sensitivity analyses indicate high robustness despite large parameter uncertainty: in 80% of all sensitivities, wind energy is valuable 12–29% more in Sweden than in Germany. The benefits of hydropower seem to level off at around 20% wind penetration. This suggests that the hydro flexibility is “exhausted” at this level. Low wind speed wind turbines, carbon pricing, and upgrades of hydropower generation capacity can lever the added value of hydro flexibility further. Not only is wind energy more valuable in the presence of hydropower, hydroelectricity also becomes more valuable if paired with wind power. [ABSTRACT FROM AUTHOR]

It is noted that human behaviour changes can have a significant impact on energy consumption, however, qualitative study on such an impact is still very limited, and it is necessary to develop the corresponding mathematical models to describe how much energy savings can be achieved through human engagement. In this paper a mathematical model of human behavioural dynamic interactions on a social network is derived to calculate energy savings. This model consists of a weighted directed network with time evolving information on each node. Energy savings from the whole network is expressed as mathematical expectation from probability theory. This expected energy savings model includes both direct and indirect energy savings of individuals in the network. The savings model is obtained by network weights and modified by the decay of information. Expected energy savings are calculated for cases where individuals in the social network are treated as a single information source or multiple sources. This model is tested on a social network consisting of 40 people. The results show that the strength of relations between individuals is more important to information diffusion than the number of connections individuals have. The expected energy savings of optimally chosen node can be 25.32% more than randomly chosen nodes at the end of the second month for the case of single information source in the network, and 16.96% more than random nodes for the case of multiple information sources. This illustrates that the model presented in this paper can be used to determine which individuals will have the most influence on the social network, which in turn provides a useful guide to identify targeted customers in energy efficiency technology rollout programmes. [ABSTRACT FROM AUTHOR]

The concept of eco-industrial park (EIP) has recently become the subject of a great deal of attention from industry and academic research groups. This paper proposes a series of systematic approaches for multi-level modelling and optimisation in EIPs. The novelties of this work include, (1) building a four-level modelling framework (from unit level to process level, plant level and industrial network level) for EIP research, (2) applying advanced mathematical modelling methods to describe each level operation, (3) developing efficient methodologies for solving optimisation problems at different EIP levels, (4) considering symbiotic relations amongst the three networks (material, water and energy networks) at the top EIP level with the boundary conditions of economic, social and legal requirements. For methodology demonstration, two cases at process level and industrial network level respectively are tested and solved with the developed modelling and optimisation strategies. Finally, the challenges and applications in future EIP research are also discussed, including data collection, the extension of the current networks to EIPs, and the feasibility of the proposed methodologies for complex EIP problems. The extended EIPs include the combination of material exchanges, energy systems and waste-water treatment networks. The aspects considered for future industrial ecology are carbon emission, by-product reuse, water consumption, and energy consumption. The main object of this paper is to explain the detailed model construction process and the development of optimisation approaches for a complex EIP system. In future work, this system is expected to share services, utility, and product resources amongst industrial plants to add value, reduce costs, improve environment, and consequently achieve sustainable development in a symbiosis community. [ABSTRACT FROM AUTHOR]

The liberalization of electricity markets gave rise to new patterns of futures prices and the need of models that could efficiently describe price dynamics grew exponentially, in order to improve decision making for all of the agents involved in energy issues. Although there are papers focused on modelling electricity as a flow commodity by using Heath et al. (1992) approach in order to price futures contracts, the literature is scarce on attempts to consider a seasonal volatility as input to models. In this paper, we propose a futures price model that allows looking into observed stylized facts in the electricity market, in particular stochastic price variability, and periodic behavior. We consider a seasonal path-dependent volatility for futures returns that are modelled in Heath et al. (1992) framework and we obtain the dynamics of futures prices. We use these series to price the underlying asset of a call option in a risk management perspective. We test the model on the German electricity market, and we find that it is accurate in futures and option value estimates. In addition, the obtained results and the proposed methodology can be useful as a starting point for risk management or portfolio optimization under uncertainty in the current context of energy markets. [ABSTRACT FROM AUTHOR]

Highlights • A mathematical approach to minimizing the turbine cost of energy based on wind statistics is developed. • The turbine cost of energy model includes the turbine rated power and the turbine rated wind speed. • A general procedure to minimize the turbine cost of energy is presented. • Wind sites with higher mean wind speed require higher turbine rated wind speed. • Turbine rated wind speed has a larger influence on turbine cost of energy than turbine rated power. Abstract With the aim of reducing green gas emission, wind turbine installations worldwide have grown rapidly in recent years. Wind energy itself is free, but has costs due to the wind turbine infrastructure and maintenance. The installation size of the wind turbine at a specific location is not only determined by the wind statistics at that location, but also by the turbine infrastructure and the maintenance cost. The payback time of the turbine is determined by the turbine cost of energy (COE). In this paper, a mathematical approach is proposed to minimize the turbine cost of energy based on wind statistics. Turbine annual energy production (AEP) is calculated based on turbine output power and annual wind speed distribution. A wind turbine cost model developed by U.S. National Renewable Energy Laboratory (NREL) is used for turbine cost analysis. The turbine cost of energy model includes the turbine rated power and the turbine rated wind speed. Finally a general guideline to minimize the turbine COE is presented. Three case studies are conducted to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Highlights • Chemical and energy processes demand advanced mathematical tools. • Hybrid methods are powerful in terms of computational burden, accuracy, and reliability. • MD and LBM help to further understand physics of processes at small scales. • This review provides helpful guidelines and procedures for various hybrid models. • This study offers proper screening criteria for various modeling approaches. Abstract Mathematical modeling and simulation methods are important tools in studying various processes in science and engineering. In the current review, we focus on the applications of hybrid models (HMs) in chemical processes, oil and gas processes, and applied energy systems. These processes suffer from complexities that demand advanced mathematical tools to be employed for various purposes such as process development, identification, simulation and modeling, optimization, control, classification, clustering, forecasting, and monitoring. Beside the lack of adequate knowledge about the processes related to the chemical and energy systems, there are other mathematical complexities such as non-linearity, large and multi-scale, long dynamics, uncertainties, and high dimensionality. The HMs can provide a practical solution to such complex processes. The arrangements of different black-box models with themselves or with white-box models are alternatively used to reduce the model complexities. The hybrid gray-box models are of significant importance as they combine the physical significance and generalization capabilities of the white-box models with the complexity reduction capability of the black-box models to facilitate/enhance the modeling strategy, while a desired precision is targeted. Such a hybridized model enables the physically-meaningful computation for the time-demanding applications. In this paper, we review different sub-models, hybridization strategies, structural designs, screening criteria, and new directions in hybrid modeling, with focus on the corresponding applications in chemical, petroleum, and energy systems. [ABSTRACT FROM AUTHOR]

The existence of ac and dc grid leads to a new concept of hybrid microgrid in which both grids are tied by an interlinking converter (ILC). Various control schemes of ILC have been discussed in literature but there is a lack of a generalized, autonomous control which considers the tie line capacity between ac and dc grids. In addition, the majority of schemes involve switching between control modes which increases power losses. To address these issues, this paper presents a generalized droop scheme of ILC for power sharing and control in a standalone hybrid ac/dc microgrid. A 3-dimensional plane is proposed where the axis correspond to dc voltage, ac frequency and converter power. The converter utilizes frequency droop at ac terminal and voltage droop at dc terminal so any change in voltage or frequency results in active power flow from an underloaded to overloaded grid. A corresponding mathematical model is also presented which determines the impact of load change on frequency and dc voltage. The proposed scheme enables autonomous power transfer in both directions without any form of communication. A comparison between the traditional normalization scheme and proposed scheme is also presented where it is observed that this method reduces the frequency deviations and transient response time, thereby giving a better performance. Both the simulations and experimental results confirm the validity of the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, a systematic review of academic literature and policy papers since 2008 is undertaken with an aim of identifying the prevalent energy systems models and tools in the UK. A list of all referenced models is presented and the literature is analysed with regards sectoral coverage and technological inclusion, as well as mathematical structure of models. The paper compares available models using an appropriate classification schema, the introduction of which is aimed at making the model landscape more accessible and perspicuous, thereby enhancing the diversity of models within use. The distinct classification presented in this paper comprises three sections, which specify the model purpose and structure, technological detail and mathematical approach. The schema is not designed to be comprehensive, but rather to be a broad classification with pertinent level of information required to differentiate between models. As an example, the UK model landscape is considered and 22 models are classified in three tables, as per the proposed schema. [ABSTRACT FROM AUTHOR]

Buildings account for approximately 32% of the total energy consumption globally and up to 40% in the developed countries, which makes buildings a prime target for energy conservation. Various energy conservation measures (ECMs) have been proposed to improve the energy efficiency in buildings, and these ECMs are usually designed and assessed using calibrated building energy models. However, there is empirical evidence that reveals noticeable discrepancies between simulated performances of ECMs reported in building energy models and their actual performances measured in buildings. This paper examines two possible causes of such discrepancies. Specifically, this paper tests the following two hypotheses: (1) using assumed occupancy data as opposed to actual occupancy data in building energy simulation reduces the reliability of estimated performance of demand-driven ECMs; and (2) using an energy model built for one ECM to cross estimate energy consumption of another ECM is statistically inaccurate. An educational building was used as a test bed. The results proved both hypotheses true, showing that estimations were more accurate and consistent for models calibrated using actual occupancy compared with those using assumed occupancy, and that cross-ECM estimation resulted in statistical inaccuracy. The findings indicated that current building energy modeling methods have limited reliability in ECM performance assessment, and need to be improved to better support the design and implementation of ECMs in buildings. [ABSTRACT FROM AUTHOR]

Reverse cycle defrosting is the most widely used standard defrosting method for air source heat pump (ASHP) units. It was suggested in previous experimental studies that during reverse cycle defrosting, downwards flowing of the melted frost due to gravity over a vertically installed multi-circuit outdoor coil in an ASHP unit has negative effects on defrosting performance. Therefore, an experimental study on draining away locally the melted frost for an experimental ASHP unit having three refrigerant circuits using water collecting trays was carried out and the study results were separately reported. To enable further quantitative analysis on the effects of local draining away the melted frost on reverse cycle defrosting performance of the ASHP unit, a modeling study on the defrosting process, at the two experimental settings of with and without the use of water collecting trays between circuits was carried out and is reported in this paper. Two semi-empirical mathematical models, corresponding to the two settings, were developed. In this paper, firstly the detailed development of the two semi-empirical models is presented. This is followed by reporting the validations of the two models using the experimental data previously reported. Finally, detailed discussions on the potential uses of the two models developed and the limitations of the modeling work reported are included. [ABSTRACT FROM AUTHOR]

Direct air-cooling condenser has attracted significant attention in the last decade due to the employment of Rankine-cycle based power plants from renewable (e.g., concentrated solar) or traditional (e.g., coal) heat sources in water-scarce areas. The optimal operating strategy of direct air-cooling condenser to maximize net power gain under given plant status and boundary conditions is rather complicated due to strong impacts from the steam turbine subsystem and varying ambient conditions. This paper aims at determining, for various boundary conditions, the optimal operating fan frequency and the corresponding back pressure of a typical large-scale air-cooled coal-fired power plant via accurate off-design models of both the turbine subsystem and air-cooling condenser, which are derived by combining aggregated physical equations and real operating data. Several data pre-processing techniques, e.g., quasi steady-state selection, are employed first to improve the data quality. Then, the processed data are divided into two parts for the performance characterization of involved equipment and the accuracy testing of the derived models, respectively. The results show that good agreement has been achieved between the prediction of the established models and the real operating data within a wide range of load factor (50–100%), and ambient temperature (10–30 °C). To maximize the plant profit, practical and quantitative operating guidelines of the air fans have been derived, which are further employed to examine current operating strategy of the air-cooling condenser of the considered power plant. It is found that with a load factor over 85%, even the full-load operation of all equipped air fans cannot deliver the theoretical optimal back pressure for the steam turbine subsystem, indicating potential benefits of enlarging the condenser for high operating loads. The proposed identification procedure can be easily implemented as an online monitoring and supervision system to practically assist the optimal plant operation. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are commonly employed in various applications owing to high energy density and long service life. Lithium-ion battery models are used for analysing batteries and enabling power control in applications. The Doyle-Fuller-Newman (DFN) model is a popular electrochemistry-based lithium-ion battery model which represents solid-state and electrolyte diffusion dynamics and accurately predicts the current/voltage response using a set of nonlinear partial differential equations. However, implementation of the full DFN model requires significant computation time. This paper proposes a computationally efficient implementation of the full DFN battery model, which is convenient for real-time applications. The proposed implementation is based on applying model order reduction to a spatial and temporal discretisation of the governing model equations. For model order reduction, we apply proper orthogonal decomposition and discrete empirical interpolation method, which leads to a set of reduced order nonlinear algebraic equations. These equations are solved using a particular numerical scheme, based on a damped Newton’s method. In a simulation study, the computational efficiency of the proposed implementation is shown and the resulting accuracy is presented. [ABSTRACT FROM AUTHOR]

Assessment of solar potential over a location of interest is an important step towards the successful planning of renewable energy projects. However, solar data are not available for every point of interest due to the absence of meteorological stations and sophisticated solar sensors, so solar radiation has to be estimated using models. This paper presents a hybrid technique to improve the performance of a widely used modeling technique i.e. artificial neural network (ANN). Four different architectures of ANN, namely: multilayer perceptron (MLP), Adaptive neuro-fuzzy inference system (ANFIS), Nonlinear autoregressive recurrent exogenous neural network (NARX), and generalized regression neural networks (GRNN), are used in this study. A wavelet multiresolution analysis is applied to decompose the complex meteorological signals into relatively simple parts, wavelet sub-series, using discrete wavelet transformation (DWT) algorithm. The wavelet sub-series are modeled by the ANN models and reconstructed to estimate the original signal. Hence, enhancing the learning process of these models. Four meteorological parameters, namely: temperature (T), relative humidity (RH), wind speed (WS), and sunshine duration (SSD), are used to mode the global horizontal irradiation (GHI) over Abu Dhabi, the United Arab Emirates. The proposed approach is compared to standalone ANN models and validated using well-known statistical validation metrics including coefficient of determination (R 2 ), root mean square error (RMSE), mean bias error (MBE), mean absolute percentage error (MAPE), and t -statistics. In addition, wavelet cross spectrum (WCS) is used as a visual indicator of the model performance in time, frequency, and phase domains. The results show that using the proposed strategy considerably improves the modeling performance of the ANN with a maximum improvement of 6.84% in R 2 for MLP. In addition, minimum RMSE of 2.78% is observed for GRNN. [ABSTRACT FROM AUTHOR]

The conventional way to utilize biogas either is energy-intensive due to biogas upgrading or causes huge waste of energy grade and environmental pollution by direct burning. This paper proposes a biogas and solar energy-assisted combined cooling, heating and power (BSCCHP) system that upgrades the caloric value of biogas before combustion by introducing a thermochemical conversion process that is driven by solar heat. Adopting commercially established technologies including steam reforming and parabolic dish concentrators, the system exhibits an enhanced system exergy efficiency, and the technology considerably reduces the direct CO 2 footprint and saves depletable fossil fuel. With a solar thermal share of 22.2%, the proposed system not only has a high net solar-to-product thermal and exergy efficiency of 46.80% and 26.49%, respectively, but also results in a commensurate 18.27% reduction of the direct CO 2 footprint compared with the reference individual systems. The effect of critical parameters in the biogas steam reforming process on the system performance was studied. A proper selection of the steam/carbon ratio leads to the optimal direct CO 2 footprint and system exergy efficiency. Pursuing a very high conversion of biogas by improving the reforming temperature is not a wise choice from a system perspective. Finally, a typical-day dynamic simulation was conducted to preliminarily explore the general operation features. This study may provide a new way to efficiently use the renewable energy in the distributed energy system. [ABSTRACT FROM AUTHOR]

Literature is rich in methodologies for “aggregated” load forecasting which has helped electricity network operators and retailers in optimal planning and scheduling. The recent increase in the uptake of distributed generation and storage systems has generated new demand for “disaggregated” load forecasting for a single-customer or even down at an appliance level. Access to high resolution data from smart meters has enabled the research community to assess conventional load forecasting techniques and develop new forecasting strategies suitable for demand-side disaggregated loads. This paper studies how calendar effects, forecasting granularity and the length of the training set affect the accuracy of a day-ahead load forecast for residential customers. Root mean square error (RMSE) and normalized RMSE were used as forecast error metrics. Regression trees, neural networks, and support vector regression yielded similar average RMSE results, but statistical analysis showed that regression trees technique is significantly better. The use of historical load profiles with daily and weekly seasonality, combined with weather data, leaves the explicit calendar effects a very low predictive power. In the setting studied here, it was shown that forecast errors can be reduced by using a coarser forecast granularity. It was also found that one year of historical data is sufficient to develop a load forecast model for residential customers as a further increase in training dataset has a marginal benefit. [ABSTRACT FROM AUTHOR]

This paper presents the design of an energy management strategy based on a low complexity Fuzzy Logic Control (FLC) for grid power profile smoothing of a residential grid-connected microgrid including Renewable Energy Sources (RES) and battery Energy Storage System (ESS). The proposed energy management strategy uses generation and demand forecasting to anticipate the future behavior of the microgrid. Accordingly to the microgrid power forecast error and the Battery State-of-Charge (SOC) the proposed strategy performs the suitable control of the grid power. A simulation comparison with previous energy management strategies highlights the advantages of the proposed work minimizing fluctuations and power peaks in the power profile exchanged with the grid while keeping the energy stored in the battery between secure limits. Finally, the experimental validation in a real residential microgrid implemented at Public University of Navarre (UPNa, Spain) demonstrates the proper operation of the proposed strategy achieving a smooth grid power profile and a battery SOC center close to the 75% of the rated battery capacity. [ABSTRACT FROM AUTHOR]

Actual technology improvements are contributing to DC and AC micro grid diffusion characterized by renewables photovoltaic and storage systems. Photovoltaic technologies have the advantage of a capillary distribution, but they are characterized by an intrinsic variable behavior due to continuously changing weather conditions. This drawback can be overcome by an appropriate temporal and energetic match among photovoltaic generation and storage capacity, so increasing micro grids reliability and efficiency levels. An accurate forecast of photovoltaic production can contribute to smooth photovoltaic systems intermittency problems so supporting generation and storage balance. Many different forecasting algorithms have been proposed in literature to provide long, medium and short-term predictions of photovoltaic production. A criterion for selection among them is not a priori and it is not uniquely identifiable. In this paper, the attention is focused on eleven data-driven models to obtain 12 h ahead forecast, also including some models not usually used in solar field but successfully employed in other expertise fields . This study addresses simple linear models, as Multiple Linear Regression, nonlinear models, such as Classification And Regression Tree, Model Tree M5, Extreme Learning Machines, weighted k-Nearest Neighbors, Multivariate Adaptive Regression Spline, Support Vector Machines, Bayesian Regularized Neural Networks and ensemble methods, as Random Forests, Cubist and Extreme Gradient Boosting. The goal is to compare methods characterized by different complexity levels to understand if a higher complexity model can provide better performances. Furthermore, the considered forecasting methodologies are compared applying two different training methodologies (online and offline) to identify the most performing training mode. The application of optimization algorithms permits to identify optima parameters and the optimum training dataset length for each model. After the optimization step, a statistical analysis is carried out to compare methods forecasting performances for the production of a 1 kW p photovoltaic plant installed at the ENEA Research Center of Portici. The case study results demonstrate promising forecasting performances applying the online training mode. Among all studied methods, Support Vector Machines, M5 and the Cubist can assure minima prediction errors and satisfying accuracy with optima dataset lengths. Cubist and M5 represent the best performing models since they are able to minimize prediction errors both in case of optima and minima training datasets. [ABSTRACT FROM AUTHOR]

As the demand for clean and sustainable energy increases, it is useful to explore alternative energy strategies, including harvesting the kinetic energy of vibration. In this paper, we design and characterize a renewable energy-harvesting system that collects energy from rail track vibrations by using a mechanical vibration rectifier (MVR). The MVR consists of meshing gears and one-way bearings, and it transforms bidirectional vibrations into unidirectional rotation to improve the efficiency of transmission. A DC motor is used as a generator, and a supercapacitor is used to store the electricity. The operation of the MVR was modelled and simulated including vehicle-track contact, track vibration, the dynamic response of the MVR and an electromechanical analysis of the generator. A prototype was manufactured to demonstrate the feasibility of the design. A peak voltage of 58 V comes close to meeting the requirements for practical usage in rail applications. These devices would be able to supply power for safety equipment and allow for emergency repairs in areas without power. [ABSTRACT FROM AUTHOR]

Reliable online estimation of state of charge (SOC) and capacity is critically important for the battery management system (BMS). This paper presents a multi-timescale method for dual estimation of SOC and capacity with an online identified battery model. The model parameter estimator and the dual estimator are fully decoupled and executed with different timescales to improve the model accuracy and stability. Specifically, the model parameters are online adapted with the vector-type recursive least squares (VRLS) to address the different variation rates of them. Based on the online adapted battery model, the Kalman filter (KF)-based SOC estimator and RLS-based capacity estimator are formulated and integrated in the form of dual estimation. Experimental results suggest that the proposed method estimates the model parameters, SOC, and capacity in real time with fast convergence and high accuracy. Experiments on both lithium-ion battery and vanadium redox flow battery (VRB) verify the generality of the proposed method on multiple battery chemistries. The proposed method is also compared with other existing methods on the computational cost to reveal its superiority for practical application. [ABSTRACT FROM AUTHOR]

Fault diagnosis of photovoltaic (PV) arrays is important for improving the reliability, efficiency and safety of PV power stations, because the PV arrays usually operate in harsh outdoor environment and tend to suffer various faults. Due to the nonlinear output characteristics and varying operating environment of PV arrays, many machine learning based fault diagnosis methods have been proposed. However, there still exist some issues: fault diagnosis performance is still limited due to insufficient monitored information; fault diagnosis models are not efficient to be trained and updated; labeled fault data samples are hard to obtain by field experiments. To address these issues, this paper makes contribution in the following three aspects: (1) based on the key points and model parameters extracted from monitored I-V characteristic curves and environment condition, an effective and efficient feature vector of seven dimensions is proposed as the input of the fault diagnosis model; (2) the emerging kernel based extreme learning machine (KELM), which features extremely fast learning speed and good generalization performance, is utilized to automatically establish the fault diagnosis model. Moreover, the Nelder-Mead Simplex (NMS) optimization method is employed to optimize the KELM parameters which affect the classification performance; (3) an improved accurate Simulink based PV modeling approach is proposed for a laboratory PV array to facilitate the fault simulation and data sample acquisition. Intensive fault experiments are carried out on the both laboratory PV array and the PV model to acquire abundant simulated and experimental fault data samples. The optimized KELM is then applied to train the fault diagnosis model using the data samples. Both the simulation and experimental results show that the optimized KELM based fault diagnosis model can achieve high accuracy, reliability, and good generalization performance. [ABSTRACT FROM AUTHOR]

• A dynamic model of pavement PV arrays with cellular automata is established. • An STNS model is used to characterize the change of irradiation intensity caused by vehicle shadow. • The irradiance matrix varying with vehicle shadow at different times can be obtained. • The influence of random vehicle shadows on the output characteristics is further explored and analysed. • Three representative scenarios are set to study the randomness of vehicle shading and its effects. This paper proposes a dynamic modelling of two-lane pavement photovoltaic (PV) arrays based on cellular automata theory, and the influence of random vehicle shadows on the output characteristics is explored and analysed. In the proposed model, a mathematical model of two-lane pavement PV array is established with considering the influence of bypass diodes and blocking diodes. In order to characterize the change of irradiation intensity caused by vehicle shadow, an asymmetric two-lane Nagel-Scheckenberg (ATNS) model is introduced in the driving process of vehicles. Moreover, the vehicle position at each time is obtained to further establish the randomly changing vehicle shadow matrix. The actual irradiance matrix can be obtained by combining vehicle shadow matrix with the irradiance matrix related to the external environment, and then a dynamic model of two-lane pavement PV arrays is established. To evaluate the effectiveness of the proposed model, the influence of the slowing probability and shading degree on the proposed model is analysed, where three representative scenarios are set to study the randomness of vehicle shading and its effects. Finally, simulations and experiments are conducted and the results show that the slowing probability and shading degree are the key two factors on the output characteristics of two-lane pavement PV arrays, and the dynamic random vehicle shadows significantly affect the output characteristics, resulting in a changing multi-peak state of the power-voltage curve and the global maximum power point being in a floating fast-changing state. [ABSTRACT FROM AUTHOR]

Abstract: Industry uses around one third of the world’s energy, and accounts for about 40% of global carbon dioxide emissions. There is increasing economic and social pressure to improve efficiency and create closed-loop industrial systems, in which energy efficiency plays a key role. This paper describes some of the key concepts involved in modelling the energy flows in manufacturing, both for the building services and the industrial processes. Detailed dynamic energy simulation of buildings is well established and routinely used, working on a time series basis – but current tools are inadequate to model the energy flows of many industrial processes. There are also well-established models of manufacturing flows, used to optimise production efficiency, but typically not modelling energy, and usually representing production and material flows as event-driven processes. The THERM project has developed new software tools to model energy-related and other utility flows in manufacturing, incorporating these into existing thermal models of factory buildings. This makes it possible to map out the whole energy system, and hence to test efficiency measures, to understand the effect of processes on building energy use, to investigate recycling of heat or cooling into other processes or building conditioning, and so on. The paper describes some of the key concepts and modelling approaches involved in developing these models, and gives examples of some real processes modelled in factories. It concludes that such models are entirely feasible and potentially very useful, although to develop a tool which comprehensively models both energy and manufacturing flows would be a major undertaking. [Copyright &y& Elsevier]

This paper presents the evaluation of the performance of a ground source heat pump system monitored plant providing heating/cooling to an office building located in the Universitat Politècnica de València in Spain. The system was designed using GLHEPRO software and it has been monitored since 2005. Once a ground source heat pump has been designed, it is important to analyze its performance along the years after its construction and check whether the design was appropriate and the simulation predictions were consistent with real experimental measurements. This paper first presents the impact of the GSHP system in the ground thermal response. The simulations obtained in GLHEPRO software will be analyzed and compared to experimental measurements. The second purpose of this work is to compare the performance simulation results of a complete ground source heat pump system model built in TRNSYS, with the experimental measurements which have been registered and collected for one cooling day. Numerical predictions and experimental results are compared and discussed. [ABSTRACT FROM AUTHOR]

Abstract: Catalytic naphtha reforming process is a vital process for refineries due to the production of high-octane components, which is intensely demanded in our modern life. The significance of this industrial process induced researchers to investigate different aspects of catalytic naphtha reforming process intensively. Some of the investigators try to improve this process by representing more effective catalysts, while others try to elucidate its kinetic and deactivation mechanisms and design more efficient reactor setups. The amount of these established papers is so much that may confuse some of the researchers who want to find collective information about catalytic naphtha reforming process. In the present paper, the published studies from 1949 until now are categorized into three main groups including finding suitable catalyst, revealing appropriate kinetic and deactivation model, and suggesting efficient reactor configuration and mode of operation. These studies are reviewed separately, and a suitable reference is provided for those who want to have access to generalized information about catalytic naphtha reforming process. Finally, various suggestions for revamping the catalytic naphtha reforming process have been proposed as a guideline for further investigations. [Copyright &y& Elsevier]

Abstract: There are many different aspects a production-planning model has to be able to handle to make a model adequate for the purpose. One aspect is the handling of start-ups and shutdowns for different processes. The production plan is likely to be changed when considering, for example, a cost connected to the start-up and/or shutdown of processes. Besides costs associated with start-ups and shutdowns, waste may be produced during the start-up and shutdown. However, there is also the possibility of carrying out soft start-ups and shutdowns or limiting the number of start-ups and shutdowns. Thus, start-ups and shutdowns have to be handled in an adequate way in models to produce reliable and accurate results. In optimisation tools, this may be dealt with by introducing certain constraints, including integers. In this paper, the implementation of alternative ways to consider start-ups and shutdowns are presented. This is done in the energy system optimisation tool reMIND, which deals with Mixed Integer Linear Programming (MILP) problems. The purpose of this paper is to show four alternatives to consider start-ups and shutdowns in optimisation models. This involves, in total, almost 50 constraints. Also, a simple dairy case study is included in the paper to visualise the effect of implementing the different alternatives to shutdowns. [Copyright &y& Elsevier]

Abstract: The present paper introduces a life cycle modeling approach for representing actual demand of energy or energy intensive products delivered within a system (electricity, heat, etc.) for optimization of the energy mix, according to some of the available life cycle impact assessments (LCIAs). Unlike classical LCA modeling approach, the real amount of several energy products leaving the system and the interactions due to the presence of multi-output processes are considered within the present approach. As a case study, future scenarios are obtained for the Belgian electricity mix production and the heat mix potentially substituted by CHP or biomass, switching between abandoning or not power from nuclear energy. The possibility of using natural gas, biomass for cogeneration, wind power and solar photovoltaic energy are considered within the availability ranges of these resources. Finally, results are presented from successive optimizations according to the sustainability potential defined in a previous paper. A pathway to a more sustainable Belgian energy system is obtained. Finally it is concluded that under the modeling conditions and without nuclear energy it is not possible to obtain a reduction of GHGs and despite diminishing of non-renewable resource consumption, a rising of toxicity is obtained. [Copyright &y& Elsevier]

In this paper we consider control-oriented modeling of a sensible thermal energy storage (TES) tank with a helical immersed heat exchanger (IHX) coil. A key focus of the modeling approach is to minimize the number of dynamic states required to adequately describe the system dynamics. The resulting model is well-suited for model-based control design, real-time simulation, and hardware-in-the-loop testing aimed at intelligent operation of TES systems. We use a discretized approach to model the temperature dynamics of the water within the storage tank. We use a quasi-steady approach to model the IHX coil dynamics, thereby limiting computational complexity. In simulation, the model runs up to 1200× faster than real-time. A simulated case study of model-based feedback control demonstrates the utility of the modeling approach. The model contains four tuning parameters that are empirically determined using experimental data collected from a commercially available domestic hot water storage tank. The model is then validated, both temporally and spatially, against data collected during the simultaneous charge/discharge mode. Finally, we quantify the trade-off between model fidelity and increased control volume discretization, showing that a 60 node model yields a RMSE value under 4.5%. [ABSTRACT FROM AUTHOR]

As one of the most successfully commercialized distributed energy resources, the long-term effects of microturbines (MTs) on the distribution network has not been fully investigated due to the complex thermo-fluid-mechanical energy conversion processes. This is further complicated by the fact that the parameter and internal data of MTs are not always available to the electric utility, due to different ownerships and confidentiality concerns. To address this issue, a general modeling approach for MTs is proposed in this paper, which allows for the long-term simulation of the distribution network with multiple MTs. First, the feasibility of deriving a simplified MT model for long-term dynamic analysis of the distribution network is discussed, based on the physical understanding of dynamic processes that occurred within MTs. Then a three-stage identification method is developed in order to obtain a piecewise MT model and predict electro-mechanical system behaviors with saturation. Next, assisted with the electric power flow calculation tool, a fast simulation methodology is proposed to evaluate the long-term impact of multiple MTs on the distribution network. Finally, the model is verified by using Capstone C30 microturbine experiments, and further applied to the dynamic simulation of a modified IEEE 37-node test feeder with promising results. [ABSTRACT FROM AUTHOR]

Heating, Ventilation and Air Conditioning (HVAC) systems represent a significant portion of total residential energy consumption in North America. Programmable thermostats are being used broadly for automatic control of residential HVAC systems while users initialize their everyday schedules and preferences. The main aim of smart grid initiatives such as time-varying prices is to encourage consumers to reduce their consumption during high electricity demand. However, it is usually a hassle to residential customers to manually re-programme their thermostats in response to dynamic electricity prices or environmental conditions that vary over time. In addition, the lack of energy management systems such as thermostats capable of learning autonomously and adapting to users’ schedule and preference changes are major obstacles of existing thermostats in order to save energy and optimally benefit from smart grid initiatives. To address these problems, in this paper an adaptable autonomous energy management solution for residential HVAC systems is presented. Firstly, an autonomous thermostat utilizing a synergy of Supervised Fuzzy Logic Learning (SFLL), wireless sensors capabilities, and dynamic electricity pricing is developed. In the cases that the user may override the decision made by autonomous system, an Adaptive Fuzzy Logic Model (AFLM) is developed in order to detect, learn, and adapt to new user’s preferences. Moreover, to emulate a flexible residential building, a ‘house energy simulator’ equipped with HVAC system, thermostat and smart meter is developed in Matlab-GUI. The results show that the developed autonomous thermostat can adjust the set point temperatures of the day without any interaction from its user while saving energy and cost without jeopardizing user’s thermal comfort. In addition, the results demonstrate that if any change(s) occurs to user’s schedules and preferences, the developed AFLM learns and adapts to new modifications while not ignoring energy conservation aspects. [ABSTRACT FROM AUTHOR]

The possibility of molten-salt freezing in pipe-flow systems is a key concern for the solar-energy industry and a safety issue in the new generation of molten-salt reactors, worthy of careful consideration. This paper tackles the problem of coolant solidification in complex pipe networks by developing a transient thermohydraulic model and applying it to the ‘Direct Reactor Auxiliary Cooling System’ (DRACS), the passive-safety system proposed for the Generation-IV molten-salt reactors. The results indicate that DRACS, as currently envisioned, is prone to failure due to freezing in the air/molten-salt heat exchanger, which can occur after approximately 20 minutes, leading to reactor temperatures above 900 °C within 4 hours. The occurrence of this scenario is related to an unstable behaviour mode of DRACS in which newly formed solid-salt deposit on the pipe walls acts to decrease the flow-rate in the secondary loop, facilitating additional solid-salt deposition. Conservative criteria are suggested to facilitate preliminary assessments of early-stage DRACS designs. The present study is, to the knowledge of the authors, the first of its kind in serving to illustrate possible safety concerns in molten-salt reactors, which are otherwise considered very safe in the literature. Furthermore, and from a broader prospective, the analytical tools developed in this study can also be applied to examine the freezing propensity of molten-salt flows in other complex piping systems where standard, finite element approaches are computationally too expensive. [ABSTRACT FROM AUTHOR]

Abstract: The paper mainly focuses on applying the two-stage combustion system with a pre-chamber into the stationary internal combustion spark ignited engine. It especially concentrates on applying throttle less operation at partial load and reduction of the NO x emission. Considerations conducted in the paper are based on the in-cylinder combustion progress analysis. Additionally, analysis of tailpipe toxic emission, with particular focus on the NO x formation in the engine equipped with the pre-chamber, is also performed. The paper presents both results of 3-D combustion modeling in the SI engine and results conducted on a test SI engine. The 3-D modeling was performed in the KIVA-3V code. Next, results from modeling were compared with results obtained from tests. Finally, satisfactory good consistency between modeled and experimental courses of both pressure, temperature and NO x were obtained. Thus, the engine model with the proposed two-stage combustion system properly simulates engine working conditions on the test bed. Results from both analyses confirmed that the two-stage combustion system significantly shortens combustion duration of an ultra lean gasoline–air mixture and contributes to reduction in NO x . [Copyright &y& Elsevier]

Abstract: This paper describes the development of a simple hybrid desalination system of a Multi-Effect Distillation (MED) and an adsorption (AD) cycle operating at sub-atmospheric pressures and temperatures. By hybridizing the conventional MED with an AD cycle, there is a symbiotic enhancement of performances of both cycles. The performance enhancement is attributed to (i) the cascade of adsorbent’s regeneration temperature and this extended the usage of thermal energy emanating from the brine heater and (ii) the vapor extraction from the last MED stage by AD cycle which provides the effect of lowering saturation temperatures of all MED stages to the extent of 5°C, resulting in scavenging of heat leaks into the MED stages from the ambient. The combined effects of the hybrid cycles increase the water production capacity of the desalination plant by nearly twofolds. In this paper, we demonstrate a hybrid cycle by simulating an 8-stage MED cycle which is coupled to an adsorption cycle for direct vapor extraction from the last MED stage. The sorption properties of silica gel is utilized (acting as a mechanical vapor compressor) to reduce the saturation temperatures of MED stages. The modeling utilizes the adsorption isotherms and kinetics of the adsorbent+adsorbate (silica-gel+water) pair along with the governing equations of mass, energy and concentration. For a 8-stage MED and AD cycles operating at assorted temperatures of 65–90°C, the results show that the water production rate increases from 60% to twofolds when compared to the MED alone. The performance ratio (PR) and gain output ratio (GOR) also improve significantly. [Copyright &y& Elsevier]

Abstract: This paper introduces the frequency domain approach to characterize and analyze wind speed patterns. It first presents the technique of and the prerequisite conditions for the frequency domain approach. Three years of wind speed data at 10 different locations have been used. This paper demonstrates that wind speed patterns during different times and at different locations can be well characterized by using the frequency domain approach with its compact and structured format. We also perform analysis using the characterized dataset. It affirms that the frequency domain approach is a useful indicator for understanding the characteristics of wind speed patterns and can express the information with superior accuracy. [Copyright &y& Elsevier]

Abstract: This paper introduced the concept, potential application and benefits relating to a novel solar photovoltaic/loop-heat-pipe (PV/LHP) heat pump system for hot water generation. On this basis, the paper reported the process and results of characterizing the performance of such a system, which was undertaken through dedicated thermo-fluid and energy balance analyses, computer model development and operation, and experimental verification and modification. The fundamental heat transfer, fluid flow and photovoltaic governing equations were applied to characterize the energy conversion and transfer processes occurring in each part and whole system layout; while the energy balance approach was utilized to enable inter-connection and resolution of the grouped equations. As a result, a dedicated computer model was developed and used to calculate the operational parameters, optimise the geometrical configurations and sizes, and recommend the appropriate operational condition relating to the system. Further, an experimental rig was constructed and utilized to acquire the relevant measurement data that thus enabled the parallel comparison between the simulation and experiment. It is concluded that the testing and modelling results are in good agreement, indicating that the model has the reasonable accuracy in predicting the system’s performance. Under the given experimental conditions, the electrical, thermal and overall efficiency of the PV/LHP module were around 10%, 40% and 50% respectively; whilst the system’s overall performance coefficient (COPPV/T ) was 8.7. Impact of the operational parameters (i.e. solar radiation, air temperature, air velocity, heat-pump’s evaporation temperature, glazing covers, and number of the absorbing heat pipes) to the performance of the system (in terms of efficiencies of the PV/LHP module and the system’s overall performance coefficient COPPV/T ) was investigated individually. The results indicated that lower solar radiation, lower air temperature, higher air velocity and smaller cover number led to enhanced electrical efficiency but reduced thermal efficiency of the module; whereas lower heat-pump’s evaporation temperature and larger number of heat absorbing pipes gave rise to both thermal and electrical efficiencies of the module. The research results would assist in developing a high efficient solar (space or hot water) heating system and thus contribute to realisation of the energy saving and associated carbon emission targets set for buildings globally. [Copyright &y& Elsevier]

Abstract: The objective of this paper was to present a comprehensive analysis including analytical and numerical models which were developed to predict the performance of a solar chimney power plant in Kerman, Iran. The numerical model results including air temperature, velocity and electrical power output were validated by comparing with experimental data of the Manzanares prototype power plant. Also the mathematical model was verified with the practical power output of the Kerman pilot plant. Also in this paper, a novel approach to evaluate the influence of the site altitude on the potential of solar chimney power plants was presented and thereby a coefficient called altitude effectiveness was defined using Manzanares prototype geometrical parameters in different site altitudes. The developed model was applied to improve the performance of a solar chimney pilot power plant built in Kerman, Iran. Based on an approximate cost model, the thermo-economic optimal configurations of the pilot power plant were illustrated; and also it was found that the chimney diameter was the most important structural dimension to improve the performance of this pilot power plant. [Copyright &y& Elsevier]

Abstract: The main goal of this paper is to present a Multistage Distribution network Expansion Planning (MDEP) problem in the presence of Distributed Generations (DGs) in a multi-objective optimization framework. The proposed model simultaneously optimizes two objectives: minimization of investment and operation costs and maximization of reliability index. The proposed optimization model is solved subject to AC power flow constraints to obtain the optimal configuration of feeders (adding and removing lines) including the optimal size of branch conductor, replacement of conductor for reserve feeders, and generated power of DGs. To include reliability concerns in the proposed MDEP problem, an analytical approach on the basis of graph theory is implemented to evaluate the Energy-Not-Supplied (ENS) index as an extra objective function. Also, in this paper, in order to identify Pareto optimal solutions of the multi-objective MDEP problem, a hybrid Particle Swarm Optimization (PSO) and Shuffled Frog Leaping (SFL) algorithm is implemented. A synthetic distribution test system is considered for the MDEP problem in a 4-year planning horizon. The results of the hybrid PSO and SFL algorithm are compared with those of the classical PSO and SFL methods. [Copyright &y& Elsevier]

Abstract: In the current uncertain context that affects both the world economy and the energy sector, with the rapid increase in the prices of oil and gas and the very unstable political situation that affects some of the largest raw materials’ producers, there is a need for developing efficient and powerful quantitative tools that allow to model and forecast fossil fuel prices, CO2 emission allowances prices as well as electricity prices. This will improve decision making for all the agents involved in energy issues. Although there are papers focused on modelling fossil fuel prices, CO2 prices and electricity prices, the literature is scarce on attempts to consider all of them together. This paper focuses on both building a multivariate model for the aforementioned prices and comparing its results with those of univariate ones, in terms of prediction accuracy (univariate and multivariate models are compared for a large span of days, all in the first 4 months in 2011) as well as extracting common features in the volatilities of the prices of all these relevant magnitudes. The common features in volatility are extracted by means of a conditionally heteroskedastic dynamic factor model which allows to solve the curse of dimensionality problem that commonly arises when estimating multivariate GARCH models. Additionally, the common volatility factors obtained are useful for improving the forecasting intervals and have a nice economical interpretation. Besides, the results obtained and methodology proposed can be useful as a starting point for risk management or portfolio optimization under uncertainty in the current context of energy markets. [Copyright &y& Elsevier]

Abstract: A simulation model of a two mode power-split hybrid electric vehicle (HEV) is proposed in this paper for the purpose of HEV dynamics analysis and control system design. The bond graph methodology is used to model dominant dynamic effects of the mechanical part of the HEV transmission. Simple quasi-static battery model, the environment model, the tire and the power losses model of a vehicle are included, as well. A low-level electric generator speed control loop is designed, which includes a PI controller tuned according to the symmetrical optimum tuning procedure. Finally, off-line optimization by conjugate gradient-based BPTT-like optimal control algorithm, which is based on the presented mathematical model, is also given in the paper. [Copyright &y& Elsevier]

Abstract: Households represent one of the most interesting sectors, when analyzing Croatia’s energy balance. It makes up one of the largest energy consumers with around 75PJ per year, which is almost 29% of Croatia’s final energy demand. Considering this consumption, implementing various mechanisms, which would lead to improvements in energy efficiency of this sector, seems relevant. In order to plan future energy systems, important would be to know future possibilities and needs regarding energy demand of different sectors. Through this paper, long term energy demand projections of Croatian households sector will be shown. Focus of the paper will be on various mechanisms influencing future energy demand scenarios. Important would be to quantify this influence, whether positive or negative, and see which mechanisms would be the most significant. Energy demand projections in this paper are based upon bottom-up approach model which combines and processes a large number of input data. The model will be compared to Croatian National Energy Strategy and certain differences and conclusions will be presented. One of the major conclusions shown in this paper is significant possibilities for energy efficiency improvements and lower energy demand in the future, based on careful and rational energy planning. Different financial, legal and technological mechanisms can lead to significant savings in the households sector which leads to lower GHG emissions and lower Croatian dependence on foreign fossil fuels. [Copyright &y& Elsevier]