@article{MosaviHosseiniImaniZalzaretal., author = {Mosavi, Amir and Hosseini Imani, Mahmood and Zalzar, Shaghayegh and Shamshirband, Shahaboddin}, title = {Strategic Behavior of Retailers for Risk Reduction and Profit Increment via Distributed Generators and Demand Response Programs}, series = {Energies}, volume = {2018}, journal = {Energies}, number = {11, 6}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/en11061602}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20180628-37546}, pages = {24}, abstract = {Following restructuring of power industry, electricity supply to end-use customers has undergone fundamental changes. In the restructured power system, some of the responsibilities of the vertically integrated distribution companies have been assigned to network managers and retailers. Under the new situation, retailers are in charge of providing electrical energy to electricity consumers who have already signed contract with them. Retailers usually provide the required energy at a variable price, from wholesale electricity markets, forward contracts with energy producers, or distributed energy generators, and sell it at a fixed retail price to its clients. Different strategies are implemented by retailers to reduce the potential financial losses and risks associated with the uncertain nature of wholesale spot electricity market prices and electrical load of the consumers. In this paper, the strategic behavior of retailers in implementing forward contracts, distributed energy sources, and demand-response programs with the aim of increasing their profit and reducing their risk, while keeping their retail prices as low as possible, is investigated. For this purpose, risk management problem of the retailer companies collaborating with wholesale electricity markets, is modeled through bi-level programming approach and a comprehensive framework for retail electricity pricing, considering customers' constraints, is provided in this paper. In the first level of the proposed bi-level optimization problem, the retailer maximizes its expected profit for a given risk level of profit variability, while in the second level, the customers minimize their consumption costs. The proposed programming problem is modeled as Mixed Integer programming (MIP) problem and can be efficiently solved using available commercial solvers. The simulation results on a test case approve the effectiveness of the proposed demand-response program based on dynamic pricing approach on reducing the retailer's risk and increasing its profit. In this paper, the decision-making problem of the retailers under dynamic pricing approach for demand response integration have been investigated. The retailer was supposed to rely on forward contracts, DGs, and spot electricity market to supply the required active and reactive power of its customers. To verify the effectiveness of the proposed model, four schemes for retailer's scheduling problem are considered and the resulted profit under each scheme are analyzed and compared. The simulation results on a test case indicate that providing more options for the retailer to buy the required power of its customers and increase its flexibility in buying energy from spot electricity market reduces the retailers' risk and increases its profit. From the customers' perspective also the retailers'accesstodifferentpowersupplysourcesmayleadtoareductionintheretailelectricityprices. Since the retailer would be able to decrease its electricity selling price to the customers without losing its profitability, with the aim of attracting more customers. Inthiswork,theconditionalvalueatrisk(CVaR)measureisusedforconsideringandquantifying riskinthedecision-makingproblems. Amongallthepossibleoptioninfrontoftheretailertooptimize its profit and risk, demand response programs are the most beneficial option for both retailer and its customers. The simulation results on the case study prove that implementing dynamic pricing approach on retail electricity prices to integrate demand response programs can successfully provoke customers to shift their flexible demand from peak-load hours to mid-load and low-load hours. Comparing the simulation results of the third and fourth schemes evidences the impact of DRPs and customers' load shifting on the reduction of retailer's risk, as well as the reduction of retailer's payment to contract holders, DG owners, and spot electricity market. Furthermore, the numerical results imply on the potential of reducing average retail prices up to 8\%, under demand response activation. Consequently, it provides a win-win solution for both retailer and its customers.}, subject = {Risikomanagement}, language = {en} } @article{HarirchianLahmerBuddhirajuetal., author = {Harirchian, Ehsan and Lahmer, Tom and Buddhiraju, Sreekanth and Mohammad, Kifaytullah and Mosavi, Amir}, title = {Earthquake Safety Assessment of Buildings through Rapid Visual Screening}, series = {Buildings}, volume = {2020}, journal = {Buildings}, number = {Volume 10, Issue 3}, publisher = {MDPI}, doi = {10.3390/buildings10030051}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200331-41153}, pages = {15}, abstract = {Earthquake is among the most devastating natural disasters causing severe economical, environmental, and social destruction. Earthquake safety assessment and building hazard monitoring can highly contribute to urban sustainability through identification and insight into optimum materials and structures. While the vulnerability of structures mainly depends on the structural resistance, the safety assessment of buildings can be highly challenging. In this paper, we consider the Rapid Visual Screening (RVS) method, which is a qualitative procedure for estimating structural scores for buildings suitable for medium- to high-seismic cases. This paper presents an overview of the common RVS methods, i.e., FEMA P-154, IITK-GGSDMA, and EMPI. To examine the accuracy and validation, a practical comparison is performed between their assessment and observed damage of reinforced concrete buildings from a street survey in the Bing{\"o}l region, Turkey, after the 1 May 2003 earthquake. The results demonstrate that the application of RVS methods for preliminary damage estimation is a vital tool. Furthermore, the comparative analysis showed that FEMA P-154 creates an assessment that overestimates damage states and is not economically viable, while EMPI and IITK-GGSDMA provide more accurate and practical estimation, respectively.}, subject = {Maschinelles Lernen}, language = {en} } @article{MousaviSteinkeJuniorTeixeiraetal., author = {Mousavi, Seyed Nasrollah and Steinke J{\´u}nior, Renato and Teixeira, Eder Daniel and Bocchiola, Daniele and Nabipour, Narjes and Mosavi, Amir and Shamshirband, Shahaboddin}, title = {Predictive Modeling the Free Hydraulic Jumps Pressure through Advanced Statistical Methods}, series = {Mathematics}, volume = {2020}, journal = {Mathematics}, number = {Volume 8, Issue 3, 323}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/math8030323}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200402-41140}, pages = {16}, abstract = {Pressure fluctuations beneath hydraulic jumps potentially endanger the stability of stilling basins. This paper deals with the mathematical modeling of the results of laboratory-scale experiments to estimate the extreme pressures. Experiments were carried out on a smooth stilling basin underneath free hydraulic jumps downstream of an Ogee spillway. From the probability distribution of measured instantaneous pressures, pressures with different probabilities could be determined. It was verified that maximum pressure fluctuations, and the negative pressures, are located at the positions near the spillway toe. Also, minimum pressure fluctuations are located at the downstream of hydraulic jumps. It was possible to assess the cumulative curves of pressure data related to the characteristic points along the basin, and different Froude numbers. To benchmark the results, the dimensionless forms of statistical parameters include mean pressures (P*m), the standard deviations of pressure fluctuations (σ*X), pressures with different non-exceedance probabilities (P*k\%), and the statistical coefficient of the probability distribution (Nk\%) were assessed. It was found that an existing method can be used to interpret the present data, and pressure distribution in similar conditions, by using a new second-order fractional relationships for σ*X, and Nk\%. The values of the Nk\% coefficient indicated a single mean value for each probability.}, subject = {Maschinelles Lernen}, language = {en} }