Abstract: The development of information and communication technologies and the deregulation of power systems have made the flexible demand participation in bidirectional interaction with power grid possible. The flexible demand can be represented as the flexible reserve provider (FRP) to provide operating reserve through load curtailment and shifting for assisting power system operation. However, the chronological characteristics of curtailment and shifting of FRP may impact the reliability of power systems. Moreover, the uncertainties from customers' participation performances, random failures of information and communication system, and different load types may also influence system operation. In this paper, a novel operating reliability evaluation model for power systems with FRP is proposed utilizing reliability network equivalent (RNE) and time-sequential simulation (TSS) techniques. The RNE technique is developed to include the reserve capacities of FRP incorporating both chronological characteristics and uncertainties. Optimal operation dispatch for system contingencies considering co-optimization of generation and FRP deployment amount is formulated over the entire study period. The TSS method is utilized to assess the operating reliability of restructured power systems. The proposed approaches are validated using the modified IEEE RTS.
Abstract:Resource additions to electric power grids are planned in advance to maintain reliability of electric power supply. This is achieved by performing reliability studies for calculating reliability indices at the planning stage to ensure that the required levels of reliability will be met. This article proposes a new state classification approach to calculate power system reliability indices using a combination of multilabel radial basis function (MLRBF) networks and importance sampling (IS) within the framework of Monte Carlo simulation process. Multilabel classification algorithms are different from single-label approaches, in which each instance can be assigned to multiple classes. This characteristic gives MLRBF the capability to classify composite power system states (success or failure), at the bus as well as system level. Bus level indices provide useful reliability information for locational reliability, which allows more rational and equitable distribution of resources. MLRBF classification does not require optimal power flow (OPF) analysis; however, OPF is required for the training and cross-entropy optimization phases. This article shows that the scope and computational efficiency to evaluate reliability indices can be significantly increased if the proposed MLRBF classifier is used together with well-known variance reduction technique of IS. As the classifier is trained to recognize the reliability status of a system state, it can also be used in operational planning when a number of scenarios need to be evaluated in a short time for their ability to satisfy load. The proposed method is illustrated using the IEEE reliability test system for different load levels. The outcomes of case studies show that MLRBF algorithm together with IS provides excellent classification accuracy in reliability evaluation while substantially reducing computation time and enhancing the scope of evaluation.
Abstract:The high penetration of renewable energy means the grid is increasingly dependent on consumer-owned devices operation, providing a growing nexus between the Internet of Things (IoT) and the smart grid. However, these devices are much more vulnerable as they are connected, through interconnections to utility, manufacturers, third-party operators, and other consumer IoT devices. Therefore, novel security mechanisms are needed to protect these devices, especially ensuring the integrity of critical measurements and control messages. Fortunately, the growing prevalence of hardware-enforced trusted execution environments (TEEs) provides an opportunity to utilize their secure storage and cryptographic functions to provide enhanced security to various IoT platforms. This paper will demonstrate a TEE-based architecture for smart inverters that utilizes hardware and software-based isolation to prevent tampering of inverter telemetry data. Furthermore, it provides an implementation of the proposed architecture on an ARM TrustZone-enabled platform using open portable TEE (OP-TEE) on a Raspberry-Pi. The developed implementation is evaluated under a set of cybersecurity metrics.
Abstract:"This paper presents a distributionally robust planning model to determine the optimal allocation of wind farms in a multi-area power system, so that the expected energy not served (EENS) is minimized under uncertain wind power and generator forced outages. Unlike conventional stochastic programming approaches that rely on detailed information of the exact probability distribution, the proposed method attempts to minimize the expectation term over a collection of distributions characterized by accessible statistical measures, so it is more practical in cases where the detailed distribution data is unavailable. This planning model is formulated as a two-stage problem, where the wind power capacity allocation decisions are determined in the first stage, before the observation of uncertainty outcomes, and operation decisions are made in the second stage under specific uncertainty realizations. In this paper, the second-stage decisions are approximated by linear decision rule functions, so that the distributionally robust model can be reformulated into a tractable second-order cone programming problem. Case studies based on a five-area system are conducted to demonstrate the effectiveness of the proposed method.
a multi-area power system, so that the expected energy not served
(EENS) is minimized under uncertain wind power and generator
forced outages. Unlike conventional stochastic programming approaches that rely on detailed information of the exact probability
distribution, the proposed method attempts to minimize the expectation term over a collection of distributions characterized by
accessible statistical measures, so it is more practical in cases where
the detailed distribution data is unavailable. This planning model
is formulated as a two-stage problem, where the wind power capacity allocation decisions are determined in the first stage, before
the observation of uncertainty outcomes, and operation decisions
are made in the second stage under specific uncertainty realizations. In this paper, the second-stage decisions are approximated
by linear decision rule functions, so that the distributionally robust model can be reformulated into a tractable second-order cone
programming problem. Case studies based on a five-area system
are conducted to demonstrate the effectiveness of the proposed
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.
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.
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.