Yazar "Zare, Kazem" seçeneğine göre listele

Listeleniyor 1 - 5 / 5
  • Küçük Resim
    Öğe
    A Fair Risk-Averse Stochastic Transactive Energy Model for 100% Renewable Multi-Microgrids in the Modern Power and Gas Incorporated Network
    (Institute of Electrical and Electronics Engineers, 2023) Daneshvar, Mohammadreza; Mohammadi-Ivatloo, Behnam; Zare, Kazem
    The ever-growing multi-vector systems along with the penetration of renewable energy sources (RESs) entail significant changes for shifting from centralized, isolated, and passive structures to potentially distributed, hybrid, and autonomous modern multi-vector energy grids (MVEGs). This paper proposes a transactive energy (TE) solution for the techno-environmental-economic operation of multi-carrier multi-microgrids (MCMs) with 100% RESs by co-optimizing power and gas grids. Indeed, TE is advanced for making a fair economic model by developing the free multi-energy sharing area (MESA) for MCMs to allow them to exchange energy with the aim of pursuing their technical, environmental, and economic goals. As 100% RESs bring severe uncertainties in the energy production sector, appropriately modeling such stochastic variations is a necessary step for obtaining realistic results in exploring the overall system. Thereby, the stochastic conditional value at risk (CVaR) technique is developed to model the risk of MCMs presence in energy interactions, in which scenario generation and reduction are performed by applying the seasonal autoregressive integrated moving average and fast forward selection methods. The problem is cast into a tractable mixed-integer linear programming by properly linearizing AC power flow and nonlinear gas equations that allows the system to extract confident results. The coupled structure of the modified IEEE 33-bus and 14-node gas systems is used as the test system for verifying the effectiveness of the proposed model. The results show the applicability of the proposed model in reliably integrating 100% RESs as well as procuring a fair condition for MCMs in the hybrid structure of the energy grid.
  • Küçük Resim
    Öğe
    A novel transactive energy trading model for modernizing energy hubs in the coupled heat and electricity network
    (Elsevier Ltd, 2022) Daneshvar, Mohammadreza; Mohammadi-Ivatloo, Behnam; Zare, Kazem
    A high or full contribution of renewable energy resources (RERs) in future modern grids is inevitable due to a great need for developing an environmentally friendly society. In modernizing renewable-based energy hubs, optimal energy management has been rapidly challenged due to intermittences of RERs in multi-vector energy networks (MVENs) over recent years. This paper proposes an innovative peer-to-peer (P2P) energy trading model for energy management of multi-vector energy hubs to consider the unpredictability challenge of RERs. For this purpose, the transactive energy paradigm is used to advance a new sustainable energy sharing environment for creating time-to-time energy balance by enabling energy hubs to exchange energy with each other freely. The uncertainty quantification was conducted by applying an autoregressive integrated moving average for generating multiple scenarios and the fast forward selection method for reducing them to the plausible number in the stochastic programming process. The coupled IEEE 10-bus and 10-node district heating network was designated as the test system for analyzing the optimal energy management of the distributed hub energies. The results designated the effectiveness of the proposed transactive energy-based P2P energy trading model in providing momentous financial and technical benefits for community energy hubs in MVENs.
  • Küçük Resim
    Öğe
    Optimal scheduling of a self-healing building using hybrid stochastic-robust optimization approach
    (Institute of Electrical and Electronics Engineers Inc., 2022) Akbari-Dibavar, Alireza; Mohammadi-Ivatloo, Behnam; Zare, Kazem; Anvari-Moghaddam, Amjad
    This article provides a two-stage robust energy management method for a self-healing smart building that can handle contingencies that occur during real-time operation. Aside from an electrical link with the distribution network, the smart building is equipped with a diesel generator and photovoltaic solar power generating systems. The energy management system should be smart enough to plan different resources based on the situation. At first, bilevel programming identifies critical faults for affected components based on mean time to repair. After identifying major failures, the faults are described in operational scenarios, and a twostage hybrid robust-stochastic programming technique is used to determine the bid/offer in day-ahead and real-time energy markets, in which stochastic programming is responsible for considering the uncertainty of faults, and the robust optimization approach is used to cope with the uncertainty of real-time market prices. After linearization, the final optimization is modeled as mixed-integer linear programming in the GAMS optimization package. For the studied smart building, the daily operational cost is expected to increase from $ 25.794 (for the deterministic case) to $ 28.097 (for the most conservative case) due to the uncertainty of real-time market prices.Due to power shortages caused by the failure of components, the total expected not-supplied load is 6.72 kW (2.53%). A comparison between naive and self-healing scheduling indicated that naive energy management will charge an additional $ 2.75 without considering the probability of components failures under the deterministic case.
  • Küçük Resim
    Öğe
    Short-term electricity demand forecasting via variational autoencoders and batch training-based bidirectional long short-term memory
    (Elsevier, 2022) Moradzadeh, Arash; Moayyed, Hamed; Zare, Kazem; Mohammadi-Ivatloo, Behnam
    Electricity load forecasting is a key aspect for power producers to maximize their economic efficiency in deregulated markets. So far, many solutions have been employed to forecast the consumption load in power grids. However, most of these methods have suffered in modeling the time-series state of data and removing noise from real-world data. Thus, the forecasting results in most cases did not have acceptable accuracy due to the mentioned problems. In this paper, in order to short-term electricity load forecast in Tabriz, Iran, a hybrid technique based on deep learning applications called Variational Autoencoder Bidirectional Long Short-Term Memory (VAEBiLSTM) is presented. Pre-processing, noise cancellation, and time-series state modeling of the data are prominent features of the developed load forecasting model. In addition, in order to prevent overfitting problems in the process of training large amounts of data, the training process is developed in the form of batch training. Load forecasting is done using meteorological and environmental data of Tabriz city as well as historical information and days of the week as input variables. In the hybrid method structure, the Variational Autoencoders are applied to the data for data preprocessing and reconstruction. Then, the normalized, noise-free data is utilized as a dataset for training the Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed training method for BiLSTM is based on batch training. To present the effectiveness of the proposed technique in a comparative approach, the conventional LSTM and Support Vector Regression (SVR) algorithms are also applied to the data. Each network is trained with input data related to the years of 2017 and 2018 to predict the electricity load of the Tabriz city separately for each of the four seasons of the 2019 year. The forecasting results obtained from each method are evaluated by different statistical performance indicators. It can be seen that the proposed model forecasts the load with the correlation coefficients (R) of 99.78%, 99.57%, 99.33%, and 99.76% for spring, summer, autumn, and winter, respectively. The presented results show that the proposed VAEBiLSTM method with the highest R values and minimum forecasting errors compared to the LSTM and SVR methods has high effectiveness and performance.
  • Küçük Resim
    Öğe
    A two-point estimate approach for energy management of multi-carrier energy systems incorporating demand response programs
    (Elsevier Ltd, 2022) Ghahramani, Mehrdad; Nazari-Heris, Morteza; Zare, Kazem; Mohammadi-Ivatloo, Behnam
    Gas-based power plants have attracted more attention in providing electrical energy worldwide because of their lower costs and air pollution. In addition, the use of multi-carrier energy systems has several advantages, such as sustainability benefits and improving performance in supplying the energy demand. This study aims to optimize the total operation cost of multi-carrier energy systems considering the uncertain parameters. The storage technology and consumption side assist the operator in achieving lower costs based on conceptions of demand response programs. Therefore, this study presents a comprehensive mathematical model for the coordinated operation of integrated multi-carrier energy systems while the operational constraints of both gas and power networks are considered. Furthermore, this paper utilizes a new uncertainty modeling method based on Hong's two-point estimate method for addressing the uncertainties of load consumption and wind generation. The proposed model is applied to a gas and power multi-carrier energy system through four case studies. The results affirm the high performance of the presented method and investigate the influence of demand response programs in both sides of energy carriers.