The green wall systems have numerous benefits for buildings and urban spaces. In this study, the experimental analysis was conducted on an office building with a green wall in winter and summer times to assess the thermal effect in comparison with the bare wall through temperature and humidity data logger devices. The existing office building, which had a suitable condition, was used for evaluation. The purpose of this study was to investigate the thermal effect on the green wall. Moreover, the annual simulation was carried out to assessing energy efficiency on the green and bare walls using DesignBuilder V. 4.5 with actual office building specifications, in Rasht. The results showed that the heat transfer on the green and bare walls were measured at about 259.2 w/m2 and 241.92 w/m2, respectively, and in the summertime, about 66.5 w/m2 and 48.4 w/m2, respectively. Based on the simulation results, the green wall could reduce the heat transfer between the interior and exterior walls by about 7% and for winter, and about 27% for summertime. Further, it was observed that the green wall had about 42% have been more effective in reducing heat loss in the summertime. Eventually, the simulation indicated that the green wall has a uniform amount of consumption, which is estimated at a bare wall, and the green wall was 343 and 67 kWh per annum respectively and could have a better effective performance by about 80% on the energy efficiency in the humid climate.
Today, the process of globalization and globalization of the economy is one of the most important issues in the world. In this process, the manner and rules of trade of some goods with a strategic international trade position are more important. Energy carriers, especially crude oil, are among these commodities that are considered strategic commodities in foreign trade. The inclusion of crude oil among the World Trade Organization commodities has raised various issues. Since the oil price in the market is affected by factors such as psychological factors and supply and demand, the inclusion of oil in the World Trade Organization will, directly and indirectly, affect these factors. Principles such as national behavior can directly affect crude oil demand. Also, the principle of quantitative restriction can directly affect the supply of crude oil. Besides, agreements such as the General Tariff and Trade Agreement, the Service Trade Agreement, and the Technical Barriers to Trade Agreement can influence engineering service providers' roles at various stages and move them toward competition.
High-Impact Low-Probability (HILP) events are the ever-increasing threats against distribution networks (DNs). Boosting DN resilience can be achieved by such measures as redundancy increment by substation allocation, feeder routing from less affected area, design of reconfigurable multi-MicroGrid based (MGs) DN and deployment of different distributed generation sources (DGs). This paper proposes an approach for designing resilient DNs by posing the problem as a dynamic modeling of bi-level resilient DN expansion planning (RDEP) program. In the upper level of the RDEP, distribution company (DisCo) identifies the optimal eco-reliable planning and operation of different assets, while in the lower level, DisCo determines the optimal operation point of the expanded DN against the specified HILP. The uncertainties of renewable energy resources, electric load and its market price and ZIP modeling, well incorporate into the problem. To evaluate the effectiveness of RDEP, computer simulation is done on a large scale 138-bus DN and the results are discussed.
Determination of the optimal reserve requirement of clean energy systems is one of the main challenges of system scheduling. Electric vehicles (EVs) could reduce public transportation emission due to fossil fuels in the cities, although power systems confront uncertainties in the presence of EVs. Adiabatic compressed air energy storage (A-CAES) also has merits like no fossil fuel consumption, low costs, and fast start-up. It could provide various applications like energy and reserve to reduce power system costs. This paper presents a probabilistic method for optimal determining of spinning reserve in the presence of wind, A-CAES, and EVs for the day-ahead market. The optimal reserve level will determine via simultaneously optimizing the total operation cost and total expected energy not supplied. For the A-CAES facility, we consider air pressure limitations, thermal storage capacity limitations, and power output limitations. Besides, the availability, responsibility, driving patterns, and the variety of electric vehicles are also considered. The impact of incentive on system cost is analyzed either. Dc power flow is used to model the transmission flow limits. The problem is formulated as mixed-integer linear programming. Finally, the well-known 24 bus test system is used to verify the efficiency of the proposed model. At the end we found A-CAES is not suitable for participation in the reserve market and it’s better to use them for peak shaving. Most of EVs will participate in reserve market and 30 percent of incentive cost will cause the optimal cost of the system.
The interest in utilizations of electric vehicles (EVs) causes to increase the penetration of intelligent parking lots (IPL). Also, vehicle-to-grid (V2G) technology next to grid-to-vehicle (G2V) technology improves the profitability of the IPLs. In addition to vehicle charge and discharge management, the IPLs can gain more profit by installing and accessing loads and resources. In this paper, the IPLs next to the hydrogen storage system (HSS) containing electrolysis, fuel cell and hydrogen storage tanks are considered to serve the loads in the existence of the upstream grid that is modeled through a scenario approach based on stochastic optimization. The uncertainties related to the electrical load, market price, vehicle's arrival and departure time, initial State-of-charge (SOC), and desired SOC of the car are considered in the proposed model. Besides, the financial risks related to the proposed hydrogen storage-based intelligent parking lot's uncertain parameters are modeled by the conditional value-at-risk (CVaR) method to get the risk-averse and risk-neutral strategies during the system operation horizon. The obtained results demonstrate that the uncertainties have a significant impact on smart parking operators' profitability, so considering uncertainty is a critical issue for parking lots. Risk results also represent that the variation of financial risk in the higher deviation of uncertain parameters is more than risk variation in lower deviations.
