Journal of Energy Management and Technology
Volume:7 Issue: 4, Autumn 2023

Weather based power curtailments have a huge share in total customers outage. Hence, reliable-affordable exploitation of networks during the adverse weather condition is one grid operator’s main issue. This article addresses an approach for optimal operational by considering dynamic line outages rate during extreme weather condition. In this paper, resiliency modification is accomplished by probing influences of weather condition on line outages using embedded sources, power storages and feeder topology reconfiguration. This work addresses objectives associated with resiliency issue in order to minimize total operation cost from distribution Company’s viewpoint, reduce amount of outages and maximize private sector’s benefits by probing weather changes during operational time interval. In this regard, a multi-objective optimization problem including both economic and resiliency targets is proposed to model the behavior of distribution company and private sector. Also, a benefit sharing mechanism is applied to increase synergistic integration between these players. A hybrid genetic- ɛ constraint strategy employing fuzzy decision maker is applied to achieve optimum Pareto-front solution based on fair profit sharing. Results proves that the proposed method increase profits for all players due to reduction in energy not supplied penalty cost as well as it enhance resiliency during adverse weather conditions.

In the current study, a magnetic-boiling driven heat transport device has been introduced and modeled numerically. The numerical modeling of the problem has been carried out using Eulerian-Eulerian two phase model and control volume technique. The numerical results showed that a flow of magnetic nanofluid can be induced and drove inside a horizontal tube in the existence of magnetic field (MF) which is due to variations made in the magnetization of the ferrofluid by generation of the vapor bubbles during boiling process. The obtained results also showed that the simulated heat transport device powered based on magnetic-boiling induction is not only able to pump the ferrofluid through the tube, but also is able to transfer a considerable amount of heat generated in the electronic chip (heat source) as well. Furthermore, the flow rate of the induced flow inside the tube increases as the heat input of the heat source is increased. The heat source can be due to existence of a high heat flux electronic chip and the chip temperature (wall of the heated region) remains nearly unchanged during the flow boiling process in the heated region. The proposed magnetic- boiling driven heat transport device is usable in a closed circulating loop which can be extensively utilized in electronics cooling applications.

This study introduces an energy superstructure for waste management in a large-scale farm. It selects the optimal technologies by optimizing the productivity factor and greenhouse gas (GHG) emission functions. The optimization results show that the optimal solution to maximize the efficiency factor is to use a biogas engine that produces a significant amount of 1695.825 GWh of electricity and 1893.11 GWh of heat in a year. Also, one of the advantages of this scenario is that it is economical and has a good return on investment, which attracts investors to it. On the other hand, the optimal solution to minimize GHG emissions do by using combined heat and power based on gas turbine and carbon capture storage; this scenario emits 114.585 Kton of carbon dioxide per year. It is worth noting that this amount, based on waste management, as well as electricity and heat production, reveals the high value of bioenergy potential.

Cooling energy storage systems can be used, coupled with conventional building cooling systems. purpose of the study was to examine the performance of the cooling energy storage system (CESS) in partial storage mode (PSM). Objective functions were considered as the exergy efficiency and the total cost rate. The multi-objective technique in MOPSO and SEAP2 algorithms were used to optimize the objective functions. The results obtained from the multi-objective analysis indicated a difference in the optimal value of designing points relative to single-objective optimization, objective function 1 (exergy efficiency), and objective function 2 (total costs). The maximum exergy efficiency for the multi-objective mode in PSM was 39.12%, and the minimum total cost for the multi-objective mode in PSM was $ 1,152 × 105. Additionally, a study on the model showed that by using ice thermal energy storage (ITES), electricity consumption reduced by 11.83% in PSM. Furthermore, because of the transfer of cooling load from peak hours to low consumption hours and reduction of power consumption by 35.12%, there is a reduction in functional costs in PSM compared to a traditional air conditioning system. The results showed that the payback period for an ITES system in PSM is 3.43 years. Ultimately, one has to note that using the ITES system reduces co2 production, leading to a reduction in environmental pollution. Additionally, PCMs used in the construction industry have been introduced and compared with each other in terms of exergy efficiency. The results show that magnesium nitrate hexahydrate reaches the highest oxygen efficiency.

Nowadays, encouraging consumers to use renewable resources and generate electricity locally in a microgrid is very important that has attracted much attention. In this paper, an optimal strategy is proposed to model energy trading among the photovoltaic (PV) prosumers in a smart microgrid. A prosumer is considered to be able to exchange energy with other prosumers through a peer-to-peer (P2P) energy trading mechanism. Moreover, they could have contracts with the utility grid to purchase or sell electricity as well. For this purpose, first, a new energy pricing model based on the production and consumption of each prosumer is presented that shows how consumers interact with the utility grid as well as other consumers. The price-based demand response (DR) programs is used to increase the profitability of each consumer and reduce the microgrid dependency to the utility grid. The uncertainty of PV systems generation is taken into account through forecasting by deep learning method. For this purpose, the long short-term memory (LSTM) model based on time series information is used. Moreover, the risk associated with the generation uncertainties is modeled by downside risk constraint (DRC). The classical optimization method is employed to minimize the total incurred costs. Simulation analysis and results show that not only the costs of energy trading will be decreased using the proposed model, but also the willingness of the prosumers to participate in the P2P energy trading will be increased significantly.

The development of a reliable wind forecast model plays a vital role in describing the variability and analyzing the time-series of the offshore and onshore wind profiles. In this paper, the analysis of the offshore and onshore wind profiles from the lidar and meteorological measurements based on two autoencoding architectures are presented.The historical datasets of the selected station variables from the:1lidar measurements and 2meteorological masts at 5–min and 10–min intervals are obtained. Two autoencoding model architectures (Conv2D and GRU encoding-decoding networks) in an unsupervised predictive operation are used for the time-series multivariable forecasting (1-288 horizons) and analysis of the:wind speed and wind direction, sectorwise windrose, CNR and prevailing air temperature. At the sampling period of 48 timesteps, the time-series wind speed and direction variations are analyzed in determining the measurement height with the steadiest wind flows for optimal loading of the large-scale wind turbine. Studied finding results of the offshore wind profiles at different heights revealed that the steadiest wind flow above 128.8 m height prevails but driven by the atmospheric effects. Also, the experimental findings revealed that the dominant wind flows of the onshore (10-20m height) are impacted by the local surface irregularitiesand atmospheric effects. Finally, the autoencoders performance is reported for the experimental offshore and onshore wind flowfor different station heights with and without the feature noise removal. Upon the validation and evaluation of the autoencoders with actual models, the proposed model architectures proved to be a fundamental forecast tool

This study investigates the effects of ACH, students’ number, and wall thickness, as well as different semester starting dates and energy consumption reduction. The optimal academic timetabling for reducing energy consumption considers curricula’s rules for taking courses, departments’ specific instructions, existing classes, professors’ priorities, and other related factors. This research uses simulation and demand-side management models to determine the energy consumption of holding classes during a timeslot. They can quantify the factors’ effects on energy use. ACH is between 1.5 and 12, wall thickness is up to 1.6 of its normal value, and students are 10 to 40. There are three starting dates for the semester: conventional time, one-week and two-week earlier. As long as there is no need to change cooling/heating systems, the factors’ impacts on each timeslot from the energy reduction perspective when implementing optimal timetabling are investigated. The developed model revealed that the four factors do not change classes’ priorities from the energy viewpoint but noticeably affect energy use reduction. The optimal scheduling by keeping the semester’s starting date and classes’ operational conditions decreases energy consumption between 11.5 and 24.5 %. The results show that the semester’s early start has a substantial influence on energy consumption reduction in way that if the operational conditions are the same and classes begin two weeks earlier, energy consumption will be reduced between these two ranges: 36.7 - 52.2 % and 49.4 - 63.9 %.