US-India CollAborative For Smart DiStribution System WIth STorage

Publications(U.S.)

Papers Published/Accepted in Refereed Journals

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 method"


UI-ASSIST COLLABORATOR-India
UI-ASSIST COLLABORATOR-US