In this paper, a novel systematic and integrated methodology to assess gas supply reliability is proposed based on the Monte Carlo method, statistical analysis, mathematical-probabilistic analysis, and hydraulic simulation. The method proposed has two stages. In the first stage, typical scenarios are determined. In the second stage, hydraulic simulation is conducted to calculate the flow rate in each typical scenario. The result of the gas pipeline system calculated is the average gas supply reliability in each typical scenario. To verify the feasibility, the method proposed is applied for a real natural gas pipelines network system. The comparison of the results calculated and the actual gas supply reliability based on the filed data in the evaluation period suggests the assessment results of the method proposed agree well with the filed data. Besides, the effect of different components on gas supply reliability is investigated, and the most critical component is identified. For example, the 48th unit is the most critical component for the SH terminal station, while the 119th typical scenario results in the most severe consequence which causes the loss of 175.61×10⁴ m³ gas when the 119th scenario happens. This paper provides a set of scientific and reasonable gas supply reliability indexes which can evaluate the gas supply reliability from two dimensions of quantity and time.

Apartments account for over 60% of total residential buildings and consume a significant portion of primary energy in South Korea. Various energy efficiency measures have been implemented for both new apartment constructions and existing apartment retrofits. Old apartment structures have poor thermal performances, resulting in a high energy consumption. The South Korean government initiated retrofitting projects to improve the energy efficiency in old apartments. Apartment owners typically replace old windows with high-performance windows; however, there is still a demand for better and more innovative retrofit methods for a highly improved energy efficiency. This paper proposes an advanced double-skin façade (DSF) system to replace existing balcony windows in old apartments. Considering the cold climate conditions of Seoul, South Korea, it mainly discusses heating energy savings. Three case models were developed: Base-Case with existing apartment, Case-1 with typical retrofitting, and Case-2 with the proposed DSF system. The EnergyPlus simulation program was used to develop simulation models for a floor radiant heating system. A typical gas boiler was selected for low-temperature radiant system modeling. The air flow network method was used to model the proposed DSF system. Five heating months, i.e., November to March, and one representative day, i.e., January 24, were selected for detailed analysis. The main heat loss areas consist of windows, walls, and infiltration. The results reveal that the apartment with the DSF retrofit saves 38.8% on the annual heating energy compared to the Base-Case and 35.2% compared to Case-1.

To improve the overall efficiency of the energy system, the basic structure for the energy internet of coordination and optimization of “generation-grid-load-storage” of Huangpu District, Guangzhou, China is designed, while the arrangement for the output of centralized and distributed energy module and energy storage are proposed. Taking economic benefit maximization, environmental benefit maximization and energy efficiency maximization as sub-objectives, the mathematical model of multi-objective optimal allocation and operation strategy of the energy internet is established considering supply-demand balance constraints, equipment characteristic constraints, operation mode constraints, and energy conditions constraints. The calculation results show that without considering the outsourced electricity, the balanced strategy, the economic development strategy, the environmental protection strategy, and the energy efficiency strategy are obtained by calculation, which are all superior to the traditional energy supply strategy. Moreover, considering the outsourced electricity, the proportion of outsourced electricity to total electricity is 19.8%, which is the system optimization of the energy internet under certain power demand. Compared with other strategies without outsourced electricity, the outsourced electricity strategy can have a certain emission reduction effect, but at the same time reduce the economic benefit. Furthermore, the huge difference in demand for thermal and cooling load between industrial and commercial areas results in the installed capacity of gas distributed energy stations in industrial areas being nearly twice as large as that in commercial areas. The distributed photovoltaic power generation is allocated according to the proportion of the installed roof areas of photovoltaic power generation system in residential, industrial, and commercial areas.

This paper investigates the scheduling strategy of schedulable load in home energy management system (HEMS) under uncertain environment by proposing a distributionally robust optimization (DRO) method based on receding horizon optimization (RHO-DRO). First, the optimization model of HEMS, which contains uncertain variable outdoor temperature and hot water demand, is established and the scheduling problem is developed into a mixed integer linear programming (MILP) by using the DRO method based on the ambiguity sets of the probability distribution of uncertain variables. Combined with RHO, the MILP is solved in a rolling fashion using the latest update data related to uncertain variables. The simulation results demonstrate that the scheduling results are robust under uncertain environment while satisfying all operating constraints with little violation of user thermal comfort. Furthermore, compared with the robust optimization (RO) method, the RHO-DRO method proposed in this paper has a lower conservation and can save more electricity for users.

The asymmetric plate heat exchanger (APHE) has the possibility of achieving balanced pressure drops on both hot and cold sides for situations with unbalanced flow, which may in turn enhance the heat transfer. In this paper, the single-phase water flow and heat transfer of an APHE consisted of two types of plates are numerically (400≤Re≤12000) and experimentally (400≤Re≤ 3400) investigated. The numerical model is verified by the experimental results. Simulations are conducted to study the effects of N, an asymmetric index proposed to describe the geometry of APHEs. The correlations of the Nusselt number and friction factor in the APHEs are determined by taking N and working fluids into account. It is found that an optimal N exists where the pressure drops are balanced and the heat transfer area reaches the minimum. The comparison between heat transfer and flow characteristics of the APHEs and the conventional plate heat exchanger (CPHE) is made under various flow rate ratios of the hot side and the cold side and different allowable pressure drops. The situations under which APHE may perform better are identified based on a comprehensive index Nu/f^1/3.

To better understand the characteristics of a large-scaled parabolic trough solar field (PTSF) under cloud passages, a novel method which combines a closed-loop thermal hydraulic model (CLTHM) and cloud vector (CV) is developed. Besides, the CLTHM is established and validated based on a pilot plant. Moreover, some key parameters which are used to characterize a typical PTSF and CV are presented for further simulation. Furthermore, two sets of results simulated by the CLTHM are compared and discussed. One set deals with cloud passages by the CV, while the other by the traditionally distributed weather stations (DWSs). Because of considering the solar irradiance distribution in a more detailed and realistically way, compared with the distributed weather station (DWS) simulation, all essential parameters, such as the total flowrate, flow distribution, outlet temperature, thermal and exergetic efficiency, and exergetic destruction tend to be more precise and smoother in the CV simulation. For example, for the runner outlet temperature, which is the most crucial parameter for a running PTSF, the maximum relative error reaches −15% in the comparison. In addition, the mechanism of thermal and hydraulic unbalance caused by cloud passages are explained based on the simulation.

Power systems are being transformed to enhance the sustainability. This paper contributes to the knowledge regarding the operational process of future power networks by developing a realistic and stochastic charging model of electric vehicles (EVs). Large-scale integration of EVs into residential distribution networks (RDNs) is an evolving issue of paramount significance for utility operators. Unbalanced voltages prevent effective and reliable operation of RDNs. Diversified EV loads require a stochastic approach to predict EVs charging demand, consequently, a probabilistic model is developed to account several realistic aspects comprising charging time, battery capacity, driving mileage, state-of-charge, traveling frequency, charging power, and time-of-use mechanism under peak and off-peak charging strategies. An attempt is made to examine risks associated with RDNs by applying a stochastic model of EVs charging pattern. The output of EV stochastic model obtained from Monte-Carlo simulations is utilized to evaluate the power quality parameters of RDNs. The equipment capability of RDNs must be evaluated to determine the potential overloads. Performance specifications of RDNs including voltage unbalance factor, voltage behavior, domestic transformer limits and feeder losses are assessed in context to EV charging scenarios with various charging power levels at different penetration levels. Moreover, the impact assessment of EVs on RDNs is found to majorly rely on the type and location of a power network.

The natural draft dry cooling tower (NDDCT) has been increasingly used for cooling in power generation in arid area. As crosswind affects the performance of a NDDCT in a complicated way, and the basic affecting mechanism is unclear, attempts have been made to improve the performance of a NDDCT based on limited experiences. This paper introduces a decoupled method to study the complicated crosswind effects on the inlet and outlet of a NDDCT separately by computational fluid dynamics (CFD) modeling and hot state experiments. Accordingly, the basic affecting mechanism of crosswind on the NDDCT performance is identified. Crosswind changes the inlet flow field of a NDDCT and induces mainstream vortices inside the tower, so as to degrade the ventilation. Besides, low crosswind deflects the upward plume at the outlet to further degrade the ventilation, while high crosswind induces the low pressure area at the outlet to reduce the ventilation degradation.

A small-scale silica gel-water adsorption system with modular adsorber, which utilizes solar energy to achieve the cogeneration of domestic air conditioning and water heating effect, is proposed and investigated in this paper. A heat recovery process between two adsorbers and a mass recovery process between two evaporators are adopted to improve the overall cooling and heating performance. First, the adsorption system is tested under different modes (different mass recovery, heat recovery, and cogeneration time) to determine the optimal operating conditions. Then, the cogeneration performance of domestic cooling and water heating effect is studied at different heat transfer fluid temperatures. The results show that the optimal time for cogeneration, mass recovery, and heat recovery are 600 s, 40 s, and 40 s, respectively. When the inlet temperature of hot water is around 85°C, the largest cooling power and heating power are 8.25 kW and 21.94 kW, respectively. Under the condition of cooling water temperature of 35°C, the obtained maximum COP_c, COP_h, and SCP of the system are 0.59, 1.39, and 184.5 W/kg, respectively.

As the world witnesses a continual increase in the global energy demand, the task of meeting this demand is becoming more difficult due to the limitation in fuel resources as well as the greenhouse gases emitted which accelerate the climate change. As a result, introducing a policy that promotes renewable energy (RE) generation and integration is inevitable for sustainable development. In this endeavor, electrification of the transport sector rises as key point in reducing the accelerating environment degradation, by the deployment of new type of vehicles referred to as PHEV (plug-in hybrid electric vehicle). Besides being able to use two kinds of drives (the conventional internal combustion engine and the electric one) to increase the total efficiency, they come with a grid connection and interaction capability known as the vehicle-to-grid (V2G) that can play a supporting role for the whole power system by providing many ancillary services such as energy storage mean and power quality enhancer. Unfortunately, all these advantages do not come alone. The uncontrolled large scale EV integration may present a real challenge and source of possible failure and instability for the grid. In this work the large scale integration impact of EVs will be investigated in details. The results of power flow analysis and the dynamic response of the grid parameters variation are presented, taking the IEEE 14 bus system as a test grid system.

Prediction of power generation of a wind turbine is crucial, which calls for accurate and reliable models. In this work, six different models have been developed based on wind power equation, concept of power curve, response surface methodology (RSM) and artificial neural network (ANN), and the results have been compared. To develop the models based on the concept of power curve, the manufacturer’s power curve, and to develop RSM as well as ANN models, the data collected from supervisory control and data acquisition (SCADA) of a 1.5 MW turbine have been used. In addition to wind speed, the air density, blade pitch angle, rotor speed and wind direction have been considered as input variables for RSM and ANN models. Proper selection of input variables and capability of ANN to map input-output relationships have resulted in an accurate model for wind power prediction in comparison to other methods.

The main concerns in the world today, especially in the energy field, are subjected to clean, efficient, and durable sources of energy. These three aspects are the main goals that scientist are paying attention to. However, the various types of energy resources include fossil and sustainable ones, but still some challenges are chasing these kinds from energy conversion, storage, and efficiency. Hence, the most reliable and considered energy resource nowadays is the utilized one which is as highly efficient, clean, and everlasting as possible. So, in this review, an attempt is made to highlight one of the promising types as a clean and efficient energy resource. Solid oxide fuel cell (SOFC) is the most efficient type of the fuel cell types involved with hydrogen and hydrocarbon-based fuels, especially when it works with combined heat and power (CHP). The importance of this type is due to its nature of work as conversion tool from chemical to electrical for generation of power without noise, pollution, and can be safely handled.

Prediction of solar radiation has drawn increasing attention in the recent years. This is because of the lack of solar radiation measurement stations. In the present work, 14 solar radiation models have been used to assess monthly global solar radiation on a horizontal surface as function of three parameters: extraterrestrial solar irradiance (G₀), duration sunshine (S) and daylight hours (S₀). Since it has been observed that each model is adequate for some months of the year, one model cannot be used for the prediction of the whole year. Therefore, a smart hybrid system is proposed which selects, based on the intelligent rules, the most suitable prediction model of the 14 models listed in this study. For the test and evaluation of the proposed models, Tamanrasset city, which is located in the south of Algeria, is selected for this study. The meteorological data sets of five years (2000–2004) have been collected from the Algerian National Office of Meteorology (NOM), and two spatial databases. The results indicate that the new hybrid model is capable of predicting the monthly global solar radiation, which offers an excellent measuring accuracy of R² values ranging from 93% to 97% in this location.

This paper proposes a control strategy for interface of distributed energy sources into the weak grid system with a focus on the energy and ancillary services. A novel controller has been designed and implemented to tackle the challenges of coupling terms in the LCL filter, the transient behavior under sudden changes, and the voltage support under fault condition using the feedback linearization technique. The controller proposed has been implemented on the PV system connected with the weak grid using the LCL filter and the performance of the controller has been verified using Matlab/Simulink through simulation under different conditions. The results of the controller proposed have been compared with the conventional PI dual loop controller. The simulation results obtained demonstrate the effectiveness and simplicity of the controller design strategy.

The maximum demand of power utilization is increasing exponentially from base load to peak load in day to day life. This power demand may be either industrial usage or household applications. To meet this high maximum power demand by the consumer, one of the options is the integration of renewable energy resources with conventional power generation methods. In the present scenario, wind energy system is one of the methods to generate power in connection with the conventional power systems. When the load on the conventional grid system increases, various bus voltages of the system tend to decrease, causing serious voltage drop or voltage instability within the system. In view of this, identification of weak buses within the system has become necessary. This paper presents the line indices method to identify these weak buses, so that some corrective action may be taken to compensate for this drop in voltage. An attempt has been made to compensate these drops in voltages by integration of renewable energy systems. The wind energy system at one of the bus in the test system is integrated and the performance of the system is verified by calculating the power flow (PF) using the power system analysis tool box (PSAT) and line indices of the integrated test system. The PF and load flow results are used to calculate line indices for the IEEE-14 bus test system which is simulated on PSAT.

China’s fossil energy is characterized by an abundance of coal and a relative lack of oil and natural gas. Developing a strategy in which coal can replace oil and natural gas is, therefore, a necessary and practical approach to easing the excessive external dependence on oil and natural gas. Based on the perspective of energy security, this paper proposes a technical framework for defining the substitution of oil and natural gas with coal in China. In this framework, three substitution classifications and 11 industrialized technical routes are reviewed. Then, three scenarios (namely, the cautious scenario, baseline scenario, and positive scenario) are developed to estimate the potential of this strategy for 2020 and 2030. The results indicate that oil and natural gas replaced by coal will reach 67 to 81 Mt and 8.7 to 14.3 Gm³ in 2020 and reach 93 to 138 Mt and 32.3 to 47.3 Gm³ in 2030, respectively. By implementing this strategy, China’s external dependence on oil, natural gas, and primary energy is expected to be curbed at approximately 70%, 40%, and 20% by 2030, respectively. This paper also demonstrates how coal, as a substitute for oil and natural gas, can contribute to carbon and pollution reduction and economic cost savings. It suggests a new direction for the development of the global coal industry and provides a crucial reference for energy transformation in China and other countries with similar energy situations.