US-India CollAborative For Smart DiStribution System WIth STorage

Papers Published/Accepted in Conference Proceedings

Abstract:Renewable based isolated DC microgrids(IDCMG) faces reliability issues due to intermittent nature of sources. Hence non-renewable source (diesel generators, gas generators, etc) are generally used to increase the reliability of isolated DC microgrids. Power control and management techniques plays crucial role in achieving optimal utilization of renewable and non-renewable sources. A power control algorithm (PCA) is developed to attain effective energy management between renewable and non-renewable sources by utilizing the hybrid storage systems (battery and supercapacitor). Proposed strategy also covers the extreme scenarios of battery storage and IDCMG. Strategy is simple, reliable and efficient in utilizing the renewable source power. Strategy also improves the life time of battery by diverting high frequency power oscillations to supercapacitor. Simulation of proposed scheme is executed on IDCMG in real time simulator RSCAD/RTDS platform to validate the effectiveness.

Abstract:High penetration of plug-in electric vehicles (PEVs) can potentially put the utility assets such as transformer under overload stress causing decrease in their lifetime. The decrease in PV and battery energy storage system (BESS) prices has made them viable solutions to mitigate this situation. In this paper, the economic aspect of their optimal coordination is studied to assess transformer hottest spot temperature (HST) and loss of life. Monte Carlo simulation is employed to provide synthetic data of PEVs load in a residential complex and model their stochastic behavior. For load, temperature, energy price and PV generation, real data for City of College Station, Texas, USA in 2018 is acquired and a case study is developed for one year. The results illustrate using BESS and PV is economically effective and mitigates distribution transformer loss of life.

Abstract:Energy storage (ES) is becoming used more and more in power distribution system due to the decrease in the cost. ES can be stationary inside buildings or mobile as a part of plug-in electric vehicles (PEV). The use of ES have merits such as peak shaving, load shifting, and backup power supply in the case of loss of power from the grid. The mobile ES poses challenges to the system since the connection location and time may be random. In this paper, the impacts of the stationary and mobile ES on the distribution transformers loss of life are quantified employing a probabilistic approach. Monte Carlo simulation is utilized to model the stochastic behavior of PEVs. For residential demand, ambient temperature and photovoltaic (PV) generation real data from College Station, Texas is used. Using the historical data of one year in this area, the impacts of different ES penetration level in the presence of PEV and PV are studied. This paper provides a better understanding of the negative effects of PEVs on transformers aging and how ES can be employed to mitigate the loss-of-life risk.

Abstract:The complexity and vulnerability of the bulk power system are well known. Among all the factors that are important for maintaining the continuity and security of the power system, the importance of the power system operator lies at the top in the hierarchy. It is important to ascertain that the operator has the critical tools, knowledge, access to training, cognitive and technical skills and the experience for extreme contingency condition that may arise. In this paper, decision making ability of power system operators in extreme events with and without advanced power system tools has been analyzed by relating to their measured cognitive flexibility.

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Abstract:High penetration of renewable energy (RE) is highly expected for sustainable green power system. Photovoltaic (PV) is the most suitable form of renewable generation in present distribution system. However, in an existing feeder, the amount of PV accommodation is limited because of utility-established acceptable voltage limit, voltage unbalance, transformer rating, line thermal overloading limit, regulation equipment, protection co-ordination, feeder configuration, load profile and more. It is important for feeder operation and planning to calculate the amount of PV that can be hosted inside an existing feeder subject to satisfy voltage limit, thermal limit, and protection criteria - often referred to as feeder hosting capacity (FHC) or PV hosting capacity. PV has uncertainty due to inherent nature and further, PV ramp rate is much faster than regulator response time. Therefore, it is common practice to consider worst-case scenario. Usually FHC is a complex power system optimization problem using steady state calculations. It is not possible to explore all possible scenarios in a practical timeframe. Therefore, multiple pre-defined scenarios are generated from random Monte Carlo simulation. However, the authors propose a swarm based intelligent scenario (location) selection from local and global search experiences for faster and better solution. Simulation results show effectiveness of the proposed method.

Abstract:This paper studies the problem of \em answering Why-questions for graph pattern queries. Given a query Q, its answers $Q(G)$ in a graph G, and an exemplar $\E$ that describes desired answers, it aims to compute a query rewrite $Q'$, such that $Q'(G)$ incorporates relevant entities and excludes irrelevant ones wrt $\E$ under a closeness measure. (1) We characterize the problem by \em Q-Chase. It rewrites Q by applying a sequence of applicable operators guided by $\E$, and backtracks to derive optimal query rewrite. (2) We develop feasible Q-Chase-based algorithms, from anytime solutions to fixed-parameter approximations to compute query rewrites. These algorithms implement Q-Chase by detecting picky operators at run time, which discriminately enforce $\E$ to retain answers that are closer to exemplars, and effectively prune both operators and irrelevant matches, by consulting a cache of star patterns (called \em star views ). Using real-world graphs, we experimentally verify the efficiency and effectiveness of \qchase techniques and their applications.

Abstract:In this paper, a review of different frequency control methods for microgrid is presented. Also, the variables considered for choosing an optimal size of energy storage associated with renewable resources are discussed. Conditions of droop control technique used for inverters operating in parallel are analyzed. Simulation investigations using MATLAB/Simulink are conducted to demonstrate the power sharing between inverters and other distributed generators in the microgrids.

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Abstract:In this paper, contingency ranking (CR) method is introduced into the reliability evaluation of line switching operations as a pre-selection method to provide quick and basic guidance on line categorization. Two case studies were conducted on RTS and IEEE 118-bus system to test the efficiency of the method. Both show reasonable accuracy in picking up critical lines and a drastic improvement of calculation speed.

Abstract:This paper presents short term load forecasting using multi-variable linear regression (MLR) for big data. Load forecasting is very important for planning, operation, resource scheduling and so on in power system. Total electric demand dynamically changes in a power system and mainly depends on temperature, humidity, wind speed, human nature, regular activities, events, etc. input variables. For the help of sensors and data science, enough historical and future input data with good accuracy are easily available. On the other hand, linear regression is a proven method, widely used in industries for forecasting. It is deterministic and robust. However, it is slow for big data because it needs large size matrix operations. In this paper, linear regression is formulated for small number of variables with big data and multi-core parallel processing is applied in all matrix operations that allow unlimited historical big data and unlimited scenarios in acceptable execution time limit. Mean absolute percent error is 3.99% of real field recorded data shown in Simulation and Result section.