Payman Dehghanian

Bio: Payman Dehghanian is an academic researcher from George Washington University. The author has contributed to research in topics: Electric power system & Grid. The author has an hindex of 23, co-authored 140 publications receiving 2234 citations. Previous affiliations of Payman Dehghanian include Sharif University of Technology & Texas A&M University.

Multiagent Genetic Algorithm: An Online Probabilistic View on Economic Dispatch of Energy Hubs Constrained by Wind Availability

Abstract: Multiple energy carriers (MECs) have been broadly engrossing power system planners and operators toward a modern standpoint in power system studies Energy hub, though playing an undeniable role as the intermediate in implementing the MECs, still needs to be put under examination in both modeling and operating concerns Since wind power continues to be one of the fastest-growing energy resources worldwide, its intrinsic challenges should be also treated as an element of crucial role in the vision of future energy networks In response, this paper aims to concentrate on the online economic dispatch (ED) of MECs for which it provides a probabilistic ED optimization model The presented model is treated via a robust optimization technique, ie, multiagent genetic algorithm (MAGA), whose outstanding feature is to find well the global optima of the ED problem ED once constrained by wind power availability, in the cases of wind power as one of the input energy carriers of the hub, faces an inevitable uncertainty that is also probabilistically overcome in the proposed model Efficiently approached via MAGA, the presented scheme is applied to test systems equipped with energy hubs and as expected, introduces its applicability and robustness in the ED problems

Critical Component Identification in Reliability Centered Asset Management of Power Distribution Systems Via Fuzzy AHP

Abstract: Confronted with the power system restructuring trend reforming the past-regulated power systems, the need for a narrower insight on the costly maintenance strategies seems imperative. It falls within the realm of reliability centered maintenance to enhance the cost effectiveness of power distribution maintenance policies. From a practical point of view, this paper devises a novel approach on the basis of the analytical hierarchical process (AHP) accompanied by fuzzy sets theory to determine the most critical component types of distribution power systems to be prioritized in maintenance scheduling. In the presence of many qualitative and quantitative attributes, fuzzy sets can effectively help to deal with the existent uncertainty and judgment vagueness. As demonstrated in a practical case study, the proposed fuzzy AHP method introduces its applicability and efficiency in the asset management procedure.

Optimal Allocation of PV Generation and Battery Storage for Enhanced Resilience

Abstract: This paper proposes an optimal sizing and siting scheme for the battery storage and photovoltaic generation aiming at improving power system resilience. The concept of capacity accessibility for both electricity demand and non-black-start (NB-S) generating units is proposed to evaluate the reachability to the power and energy capacity during extreme events. Priority of the NB-S generating units, characterized by their different importance during the black start process, is also taken into account. The unknowable nature of the extreme events is captured and modeled through a multi-objective optimization formulation to balance three main objectives: 1) the investment and operation costs; 2) the capacity accessibility for electricity demand; and 3) the capacity accessibility for NB-S generating units. The proposed approach is validated through numerical experiments, which illustrate how the new planning approach can help enhance the grid resilience.

Energy Storage Planning for Enhanced Resilience of Power Distribution Networks Against Earthquakes

Abstract: Energy infrastructures are perceived continuously vulnerable to a range of high-impact low-probability (HILP) incidents—e.g., earthquakes, tsunamis, floods, windstorms, etc.—the resilience to which is highly on demand. Specifically suited to battery energy storage system (BESS) solutions, this paper presents a new resilience-driven framework for hardening power distribution systems against earthquakes. The concept of fragility curve is applied to characterize an earthquake hazard, assess its impact on power distribution systems, and estimate the unavailability of the network elements when exposed to extreme earthquakes. A new metric is defined to quantify the network resilience taking into account the uncertain nature of such HILP events. A linear programming optimization problem is formulated to determine the capacity and location of the BESSs for enhanced resilience against earthquakes. Efficacy of the proposed framework is numerically analyzed and verified through application to a real-world distribution power grid.

Smart Households’ Aggregated Capacity Forecasting for Load Aggregators Under Incentive-Based Demand Response Programs

Abstract: The technological advancement in the communication and control infrastructure helps those smart households (SHs) that more actively participate in the incentive-based demand response (IBDR) programs. As the agent facilitating the SHs’ participation in the IBDR program, load aggregators (LAs) need to comprehend the available SHs’ demand response (DR) capacity before trading in the day-ahead market. However, there are few studies that forecast the available aggregated DR capacity from LAs’ perspective. Therefore, this article proposes a forecasting model aiming to aid LAs forecast the available aggregated SHs’ DR capacity in the day-ahead market. First, a home energy management system is implemented to perform optimal scheduling for SHs and to model the customers’ responsive behavior in the IBDR program; second, a customer baseline load estimation method is applied to quantify the SHs’ aggregated DR capacity during DR days; third, several features which may have significant impacts on the aggregated DR capacity are extracted and they are processed by principal component analysis; and finally, a support vector machine based forecasting model is proposed to forecast the aggregated SHs’ DR capacity in the day-ahead market. The case study indicates that the proposed forecasting framework could provide good performance in terms of stability and accuracy.

IEEE transactions on pattern analysis and machine intelligence

Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

Energy function analysis for power system stability

Abstract: (1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Research on Resilience of Power Systems Under Natural Disasters—A Review

Abstract: Natural disasters can cause large blackouts. Research into natural disaster impacts on electric power systems is emerging to understand the causes of the blackouts, explore ways to prepare and harden the grid, and increase the resilience of the power grid under such events. At the same time, new technologies such as smart grid, micro grid, and wide area monitoring applications could increase situational awareness as well as enable faster restoration of the system. This paper aims to consolidate and review the progress of the research field towards methods and tools of forecasting natural disaster related power system disturbances, hardening and pre-storm operations, and restoration models. Challenges and future research opportunities are also presented in the paper.

Online Short-term Solar Power Forecasting

Abstract: This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.