Managing and Minimizing Cost of Energy in Virtual Power Plants in the Presence of Plug-in Hybrid Electric Vehicles Considering Demand Response Program

Barati, Hassan,Ashir, Farshid The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.2

Virtual power plants can be regarded as systems that have entered the network after restructure of power systems. In fact, these plants are a set of consumers capable of consuming and generating power. In response to widespread implementation of plug-in hybrid electric vehicles, further investigation of energy management in this type of power plants seems to be of great value. In effect, these vehicles are able to receive and inject power from/into the network. Hence, study of the effects of these vehicles on management of virtual power plants seems to be illuminative. In this paper, management of power consumption/generation in virtual power plants has been investigated in the presence of hybrid electric vehicles. The objective function of virtual power plants problem management is to minimize the overall costs including not only the costs of energy production in power generation units, fuels, and degradation of batteries of vehicles, but also the costs of purchasing electricity from the network. Furthermore, the constraints on the operational of plants, loads and hybrid vehicles, level of penalty for greenhouse gas emissions ($CO_2$ and $NO_x$) produced by power plants and vehicles, and demand response to the immediate price of market have all been attended to in the present study. GAMS/Cplex software system and sample power system have been employed to pursue computer implementation and simulation.

Managing and Minimizing Cost of Energy in Virtual Power Plants in the Presence of Plug-in Hybrid Electric Vehicles Considering Demand Response Program

Hassan Barati,Farshid Ashir 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.2

Virtual power plants can be regarded as systems that have entered the network after restructure of power systems. In fact, these plants are a set of consumers capable of consuming and generating power. In response to widespread implementation of plug-in hybrid electric vehicles, further investigation of energy management in this type of power plants seems to be of great value. In effect, these vehicles are able to receive and inject power from/into the network. Hence, study of the effects of these vehicles on management of virtual power plants seems to be illuminative. In this paper, management of power consumption/generation in virtual power plants has been investigated in the presence of hybrid electric vehicles. The objective function of virtual power plants problem management is to minimize the overall costs including not only the costs of energy production in power generation units, fuels, and degradation of batteries of vehicles, but also the costs of purchasing electricity from the network. Furthermore, the constraints on the operational of plants, loads and hybrid vehicles, level of penalty for greenhouse gas emissions (CO₂ and NOx) produced by power plants and vehicles, and demand response to the immediate price of market have all been attended to in the present study. GAMS/Cplex software system and sample power system have been employed to pursue computer implementation and simulation.

Probabilistic Risk-Based Planning of Distributed Generation Units Using Multi Objective Hybrid Augmented Weighted ε-Constraint Approach

Ostvar Farzaneh,Barati Hassan,Mortazavi Seyed Saeidollah 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.5

Utilization of Distributed Generation (DG) resources has increased dramatically in power systems due to economic and environmental benefits. Risk-based planning methods could manage the operational and economic risks at acceptable levels. In this paper a new probabilistic multi-objective risk-based DG planning method is proposed. In the proposed method, operation and investment costs minimization, reliability improvement, losses minimization, and operational risks minimization are considered. Uncertainties of wind generation and demand are modeled by using scenario tree and two stage method. The multi-objective problem is solved by using a combination of lexicographic optimization technique and hybrid augmented weighted ε-constraint approach. In addition, fuzzy satisfying criterion is used for decision making among Pareto solutions. Analyzing the numerical results validated that the proposed method has an appropriate and accurate performance in achieving the set of Pareto solutions. By analyzing the Pareto solutions, it is proved that by more investment on DG allocation, reduction of loss and operation risk could be appropriately achieved. Comprehensive risk analysis of wind energy generation and the curtailment of wind power are conducted under different cases. It is concluded that when the wind power injection increases the negative effects on the power system increase. Moreover, in case of considering wind power curtailment, the risk factor significantly reduced.

Stochastic Multi-objective Short-term Hydro-thermal Self-scheduling in Joint Energy and Reserve Markets Considering Wind-Photovoltaic Uncertainty and Small Hydro Units

Behnamfar Mohammad Reza,Barati Hassan,Karami Mahdi 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.3

In this paper, a stochastic multi-objective structure is introduced in joint energy and reserve market to allow energy generation companies (GENCOs) participating in the short-term hydro-thermal self-scheduling with wind, photovoltaic uncertainty and small-hydro units. In addition, uncertainties including energy price, spinning and non-spinning reserve prices as well as the uncertainty of renewable energy resources such as output power of the wind, PV and small-hydro power plants are mentioned. One pivotal feature of this study is that two methods are used to generate stochastic multi-objective scenarios, namely lattice monte carlo simulation and roulette wheel mechanism. After that, the main purpose of the study is described, i.e., making GENCOs able to achieve the maximum profi t and the minimum emission by using a multi-objective function considering a stochastic process. To reach this aim, the mixed integer programming which includes a set of multi stage deterministic scenarios is employed. However, some special cases should be introduced in the formulation structure of the presented scheduling regarding hydro-thermal units to make the SMO-HTSS problem with wind, PV and SH units alike real time modeling. Since optimal Pareto solutions are produced in this method, one can allude to the application of the ε -constraint method. Nevertheless, in order to select one of the most appropriate solutions among Pareto solutions obtained, the utilization of fuzzy method has been presented. In the end, some tests are carried out on an IEEE 118-bus test system to verify the accuracy and validity of the proposed method.

Deciphering genotype-by-environment interaction in barley genotypes using different adaptability and stability methods

Pour-Aboughadareh Alireza,Barati Ali,Gholipoor Ahmad,Zali Hassan,Marzooghian Akbar,Koohkan Shir Ali,Shahbazi-Homonloo Kamal,Houseinpour Arash 한국작물학회 2023 Journal of crop science and biotechnology Vol.26 No.5

Dissection of genotype-by-environment interaction (GEI) effects has a key role in identifying stable high-yielding genotypes across various environments before commercial release. This study sought to assess the GEI effect and to identify superior barley genotypes using 32 parametric and non-parametric stability statistics. Eighteen new promising genotypes along with one improved cultivar (as check) were evaluated across ten environments during the 2019–2021 cropping seasons in the warm climate of Iran. The AMMI analysis of variance indicated that genotype, environment, and GEI effects were significant for grain yield. Furthermore, partitioning of the GEI effect showed that the first six interaction principal components (IPCA1–IPCA6) were highly significant. Multivariate analyses classified all measured statistics into five groups based on the dynamic and static concepts of stability. Among the stability statistics, HMGV, RPGV, HMRPGV, CV, NP(2), NP(3), NP(4), KR, S(3), and S(6) showed a dynamic concept of stability. Based on all methods, genotypes G2, G3, G9, G11, G14, and G17 were identified as stable high-yielding genotypes. In general, based on all of the used approaches, G9 and G11 were identified as the best genotypes for cultivation in the warm regions of Iran; hence, these genotypes can be considered for commercial release.

On nonlinear vibration behavior of piezo-magnetic doubly-curved nanoshells

Sayed Sajad Mirjavadi,Hassan Bayani,Navid Khoshtinat,Masoud Forsat,Mohammad Reza Barati,A.M.S. Hamouda 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.26 No.5

In this paper, nonlinear vibration behaviors of multi-phase Magneto-Electro-Elastic (MEE) doubly-curved nanoshells have been studied employing Jacobi elliptic function method. The doubly-curved nanoshell has been modeled by using nonlocal elasticity and classic shell theory. An exact estimation of nonlinear vibrational behavior of smart doubly-curved nanoshell has been obtained via Jacobi elliptic function method. This method can incorporate the influences of higher order harmonics leading to an exact estimation of nonlinear vibration frequency. It will be indicated that nonlinear vibrational frequency of doubly-curved nanoshell relies on nonlocal effect, material composition, curvature radius, center deflection and electro-magnetic field.