Standalone DC microgrids (SDCMGs) are emerging as prominent solutions to remote customers. As these SDCMGs mainly depend on renewable energy sources to curb carbon emission, reliability of the system is lessened due to their intermittent nature. The battery could offer a solution to this problem as it is inherently enclosed by DC grid for power balancing, but the purpose may not be served completely due to high capital cost and maintenance. Thus, the diesel generator (DG) becomes a major alternative to overcome this issue because of low investment, high compatibility, and flexibility. However, DG inhibits with cranking delay, sluggish response and low fuel efficiency under frequent switching and variable loading scenarios. To suppress the issues, a new power management strategy (PMS) is developed to ensure proper coordination between different sources, storage devices and loads in SDCMG. In addition to this, an effective control scheme is also proposed to achieve seamless regulation of DC bus voltage even under extreme conditions. In this study, different SDCMG configuration is considered for testing and simulation of system is carried out in real-time digital simulator to prove their viability. A scaled prototype is developed to validate simulation results and authenticate proposed PMS and control scheme.
This paper addresses voltage control and energy management strategy of active distribution system with grid-connected islanded DC microgrid with hybrid energy resources. In islanded mode, control and management strategy using backup diesel generator (DG), renewable energy source (RES) and energy storage system plays a vital role in maintaining the microgrid bus voltages within the limits. However, operating backup diesel generator (DG) has its own challenges including startup delay, frequent switching, and uneven loading when operated along with RES. Additionally, fuel efficiency and emission characteristics vary with loading since most of DGs are driven by constant speed diesel engines. Hence, an exhaustive power management scheme (PMS) is proposed by utilizing the hybrid energy storage system. Real time simulation and experimental validation of proposed scheme are provided using real time digital simulator (RTDS) and laboratory scale prototype respectively. Extreme scenarios including DG failure/scheduled maintenance, low power generation and battery charge are analyzed in islanded mode. Further, DC microgrid is connected to IEEE active distribution system feeder to analyze control and management challenges for grid connected mode a) with contribution from microgrid and b) with no contribution from microgrid. These scenarios resemble more realistic unbalanced utility grid conditions. A centralized optimization problem is formulated at advanced distribution management system (ADMS) level to maintain all the node voltages within limits in IEEE test system. RTDS is used to simulate DC microgrid connected with IEEE test system and optimization algorithm is implemented in MATLAB. Superior performance of the developed algorithms are demonstrated and validated for coordination between centralized optimization at ADMS and MEMS.
Abstract:The impact of uncoordinated charging of electrical
vehicles (EVs) under high penetration on distribution
transformers is studied. It is shown that EV charging may cause
prolonged overload condition leading to accelerated assets loss of
life and increased hazard of failure. To mitigate the impact, a
fuzzy logic-based system for determining EV charging schedule is
devised. It uses four main inputs: 1) EV battery state of charge; 2)
required state of charge for the next trip; 3) estimated time of EV
departure; and 4) customer comfort level. The resulting output is
a performance index that the distribution system operators can
utilize in a decision-making tool to determine whether to delay the
charging of given EV and pay the incentive to the EV owner. The
data for the city of College Station, Texas, USA including
temperature, price of electricity and load profile are collected
from various sources to simulate different use cases. The example
illustrates how the proposed EV management approach could
mitigate the impact of EV charging on the transformer loss of life
and hazard of failure. The main advantage of the proposed
approach is the low cost due to simple design implementation. The
information that needs to be sent from the consumer to the
distribution system operator is minimized, which helps in
maintaining costumers’ privacy.
Power electronics interface of renewable energy to system is now the trend in both transmission and distribution segments of power network. Unlike synchronous generators, the fault feeding and control characteristic of these renewable generators are different and mostly influenced by the topology, switching, and control deployed in power electronics interface. So, the network protection design and operational requirements are now challenged in the absence of large fault current. Although the differential current principle still works, its implementation is limited by the significant cost associated to its communication system. This paper proposes a differential line protection scheme based on local fault detection and comparing binary state outputs of relays at both ends of the line thus requiring a simple, flexible and low bandwidth communication system. The performance of the proposed scheme is assessed through simulation of an example system with several scenarios.
Abstract:Measuring and enabling resiliency of electric distribution systems with increasing weather and cyber events are important. Some of the extreme events (e.g. Earthquakes, Hurricanes) and associated paths are predicted and monitored closely in advance and allow to take pre-event proactive control actions. The Distribution Phasor Measurement Units (D-PMUs) provide new opportunities and supporting such proactive actions. A synchrophasor based resiliency driven pre-event reconfiguration can ensure minimizing impact of the expected event on the power distribution system and associated performance. However, the D-PMUs will also face challenges in terms of data quality similar to the transmission PMUs. The focus of this paper is to provide data mining approaches for anomaly detection in D-PMUs and proposing resiliency-driven pre-event reconfiguration with islanding as a proactive mechanisms to minimize the impact of adverse events on system using processed synchrophasors data. Results are validated for real industrial feeders and test cases with satisfactory response.
Abstract:Modern active distribution grids are characterized by the increasing penetration of distributed energy resources (DERs). Proper coordination and scheduling of these DERs requires a local retail market which can operate at the distribution grid level. In this paper, we propose a retail market for optimally managing and scheduling DERs, and coordinating ancillary services in a distribution grid. Our proposed retail market leverages a recently proposed distributed proximal atomic coordination (PAC) algorithm which has several advantages over other distributed algorithms, with reduced local computational effort and enhanced privacy. We describe how the market can be implemented using a Distribution System Operator (DSO), whose representatives are located at the primary feeder level and workers are located at the substation level, and how the DSO will interact with the Wholesale Electricity Market. Finally, we extensively validate the performance of the proposed retail market via simulations of three networks: a real distribution grid in Tokyo, a balanced IEEE 123-bus distribution grid, and a modified IEEE 13-bus network. Our results show that the proposed market is practical and can be easily implemented in distribution grids, resulting in optimal real-time scheduling of DERs and compensation in the form of distributed locational marginal prices.
Abstract:The growing number of consumer-grade network-enabled Distributed Energy Resources (DER) installations introduces new attack vectors that could impact grid operations through coordinated attacks. This work presents a cyber-physical model and risk assessment methodology for analyzing the emerging nexus between Internet of Things-based energy devices and the bulk transmission grid. The cyber model replicates the device-level interconnectivity and software components interaction found within these architectures to understand the feasibly of coordinated attacks, while the physical model is used to assess the attack's impacts on the grid. The manuscript questions the validity of previous papers' claims regarding IoT-based grid attacks by addressing key limitations in both the power grid and cyber infrastructure models of those works. The resulting methodology is then evaluated using the Western Electricity Coordinating Council (WECC) electrical model coupled with DER's operational statistics from California. The results suggest that current DER penetration rates are not yet significant enough to present serious risk, but continued DER growth may be problematic. Furthermore, the work identifies policies that mitigate these risks through increased device diversity and cybersecurity requirements.
Abstract:The relative share of renewable energy, specifically the
solar photovoltaic (PV), is increasing exponentially in the world
electric energy sector. This is a cumulative result of reduction in
the cost of solar panels, improvement in the panel efficiency, and
advancement in the associated power electronics. Among different
types of PV plants, installation of small-scale rooftop PV is growing
rapidly due to direct end-user benefits and lucrative governmental
incentives. There are various standards developed in regards to
grid integration of PVs and other distributed generations (DGs).
Different power converter topologies are developed to interface the
PV panel with the utility grid. To keep up with the stringent regulations imposed by the standards, various control strategies and grid
synchronization methods have been developed. This review article
amalgamates and summarizes all of the aforementioned aspects
of a grid-integrated PV system including various standards, power
stage architectures, grid synchronization methods, operation under
extreme events, and control methodologies, pertaining to smallscale PV plants. This article will help freshman researchers to gain
some familiarity with the topic and introduce them to some of the
key issues encountered in this field.
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