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This paper proposes a novel metrics analysis of the control and management system for an efficient and resilient post-hurricane integrated recovery framework. To enhance the resiliency of networked-microgrids (MGs), resources such as mobile emergency resources (MERs), electrical vehicles (EVs), and energy storage systems (ESSs) associated with the reconfiguration strategy are utilized to decrease blackouts to a minimum time. In the proposed method, the local power-flow path is actively altered by the network reconfiguration. This capability allows restoring critical loads and minimizing the energy not supplied (ENS) to the local consumers. Islanded loads that need truck-mounted MERs are simultaneously determined through optimal scheduling. The role of MERs is to deliver power to the islanded load regarding optimal routes. To produce optimal routes and to avoid out-of-service roads, the well-known Dijkstras algorithm is used in this study. The proposed method aims to maximize system resiliency through the integrated single-objective optimization problem, associated with evaluating the metrics, to assess the control system performance from a resiliency index perspective. Simulation results on a 69-bus test system indicate satisfactory performance and effectiveness of the proposed method.

Fast and accurate methods of fault detection and isolation are a pre-emptive measure in multiterminal dc (MTdc) systems. Zonal isolation of faults is necessary to prevent any misopertation of the dc breakers that can lead to a shutdown of the network. Existing techniques require fast-communication or data synchronization methods have their own disadvantages. This article proposes a method for efficient fault zone isolation without the need of a communication link that prevents any misoperation of the dc breakers in a radial MTdc. This method provides individual local measurement-based control to the hybrid dc circuit breakers (dcCB). Faults created outside the zone of protection for a breaker create a change in the rate of change of current or voltage leading to misoperation. To avoid detection using a fixed threshold, an adaptive threshold based approach is suggested that updates the threshold based on the present operating status. Sensitivity analysis by varying the current limiting inductance and fault location is performed. A three terminal radial model of an MTdc is used for zonal isolation using power system computer aided design/electromagnetic transients including dc.

The increasing penetration of renewables tends to demand more flexibility from gas-fired units to maintain system reliability, which significantly strengthens the interdependency between the electricity and the natural gas markets. To this end, intra-day gas market trading may be necessary, and a completely deregulated natural gas market may emerge in the future. As the interdependency between the electricity and the natural gas markets keeps growing, this paper proposes a bi-level strategic bidding model to study the market behaviors of the gas-fired unit, which simultaneously acts as a strategic power producer in the electricity market and a gas buyer in the natural gas market. The upper-level problem maximizes the profit of the gas-fired unit, and the two lower-level problems respectively model the market-clearings of the electricity and the natural gas markets. The upper and lower levels interact through locational marginal prices of electricity and natural gas. The proposed bi-level optimization model is converted to a mathematical problem with equilibrium constraint (MPEC). Then it is transformed into a mixed-integer second-order-cone programming (MISOCP) for efficient calculation. Numerical results demonstrate the proposed methodology is capable of mimicking the market-clearing process and strategic behaviors of the gas-fired unit in the interdependent energy markets effectively.

The recent increase in Renewable Generation (RG) has prompted many states, utilities, and other stakeholders to improve the methods used to determine the value of RG in efforts of replacing the Net Metering approach. However, these studies result in a wide range of values. This paper proposes a methodology based on the RG valuation studies conducted in recent years. The method includes the most common cost and benefit components considered in these studies and adopts a comprehensive method to calculate each component. The main categories include avoided energy, avoided generation capacity, avoided transmission capacity, avoided system losses, price hedging benefits, environmental benefits, and grid integration costs. A realistic case study emulating a large utility is also conducted to illustrate the application of the proposed method. The results show that all the main components can be estimated based on detailed system models or simulations. The results also illustrate some of the data challenges associated with such a study.

Distribution-level electricity market provides a platform for trading energy and grid services from large-amount of small-scale distributed energy resources (DERs) located on distribution grids. The behavior of the DERs in the distribution electricity market may ultimately impact the market-clearing and locational marginal prices (LMPs) in the wholesale market. This paper proposes a bi-level optimization model for distribution market clearing and distribution locational marginal pricing (DLMP) considering the interactions between distribution and transmission wholesale markets. In the proposed model, the upper-level model represents the distribution system operator (DSO) market-clearing and the lower-level model represents the wholesale market-clearing by the independent system operator (ISO). The LMP at the substation will impact the DER dispatch and power demands of the DSO as well as the DLMP. In turn, the power demands of the DSO will further impact the ISO market and its LMPs. The equilibrium problem with equilibrium constraints (EPEC) approach is applied to find the equilibria of multiple DSOs and the ISO. The EPEC problem is transformed into a single-level mixed-integer convex problem in order to allow for efficient solving. The effectiveness of the proposed model and solution method are demonstrated through case studies.

A major challenge in a multiterminal high-voltage direct current (MT-HVdc) system is the detection and subsequent removal of dc faults, i.e., line–line or line–ground faults. This technology is crucial to make emerging MT-HVdc systems reliable and stable. Current detection solutions are either too sensitive to load variations causing false positives or are too slow to detect faults before the equipment is damaged. This paper proposes a framework and case examples for fault detection based on features derived from the wavelet transform of the grid current. The algorithm is intended to control new high-speed dc breakers (such as assembly HVdc breaker) for improved fault protection. This paper customizes the detection strategy to specific fault frequencies using multiresolution analysis, whose magnitudes are significant in the vicinity of the fault, and are attenuated at remote grid terminals. A model of a three-terminal MT-HVdc system is developed in power systems computer aided design/electromagnetic transients including dc. The results have been post processed in matrix laboratory and simulation results verify the effectiveness of the proposed solution for preventing dc faults on an HVdc system.

The advanced distribution management system (ADMS) integrates the existing distribution management system and outage management system components with a distributed energy resource management system. Compilation of these components provides more flexibility for maximizing energy efficiency and reliability. In this paper, two major ADMS functions: conservation voltage reduction and demand response are integrated for harnessing the energy efficiency. The integration of these two functionalities considers all the constraints for maintaining system reliability. The proposed energy efficiency model offers the most suitable demand response plan to each customer for minimizing the consumption cost in the day ahead market. The model also considers the generation from customer-end distributed energy resources (DER), such as photovoltaic and battery energy storage systems. Moreover, different scenarios are considered in terms of the DER and load stochasticity in the integrated framework. Finally, the proposed model is validated on a test feeder with the consideration of time-varying voltage sensitive loads.

This paper presents a novel data-driven approach for predicting lightning-related outages that occur in power distribution systems on a daily basis. In order to develop an approach that is able to successfully fulfill this objective, there are two main challenges that ought to be addressed. The first challenge is to define the extent of the target area. An unsupervised machine learning approach is proposed to overcome this difficulty. The second challenge is to adequately identify characteristics of lightning-related outages and to explore the relationship between these outages and weather-related variables (thunderstorm events). In this paper, these outages are clustered into a few manageable groups. Then, a probabilistic model is presented to estimate the likelihood of each group of outages. Finally, a machine learning classification algorithm that can handle the imbalanced problem is developed to predict what group will the outage belong to on a specific day in a specific area of the system under study. Actual outage data, obtained from a major utility in the U.S., in addition to radar weather forecast data are utilized to build the proposed approach. Also, three case studies are provided to show several issues associated with predicting lightning-related outages, and to demonstrate how the proposed approach can address those problems adequately.

Cyber-physical systems (CPSs) and the Internet of Things (IoT) are converging towards a hybrid platform that is becoming ubiquitous in all modern infrastructures. The massive deployment of CPS requires comprehensive, secure, and reliable communication. The integration of complex and heterogeneous systems makes enormous space for the adversaries to get into the network and inject malicious data. To obfuscate and mislead the attackers, we propose DDAF, a deception defense-based data acquisition framework for a hierarchical communication network of CPSs. Each switch in the hierarchical network generates a random pattern of addresses/IDs by shuffling the original sensor IDs reported through it. Later, while sending the measurement data from remotely located sensors to the central controller, the switches craft the network packets by replacing the sensors’ original IDs with pre-generated deceptive IDs. Due to the deception, any stealthy attack turns into a random data injection and ends up as an outlier during the bad data detection process. By analyzing the outlier data, DDAF detects and localizes the attack points and the targeted sensors. DDAF is generic and highly scalable to be implemented in any size of networked control systems. Experimental results on the standard IEEE 14, 57, and 300 bus power systems show that DDAF can detect, mitigate, and localize almost 100% of the stealthy cyber-attacks. To the best of our knowledge, this is the first framework that implements complete randomization in the data acquisition of a networked control system.

This review describes the offshore power system (OffPS), a research area of electricity that—despite being deeply investigated in scattered fragments—has not been comprehensively conceptualized as an integral system of interrelated components, equipment, functions and subsystems. Part of this lack of systemic approach is due to the fact that this system normally does not subsist by itself. It depends on structural elements for protection and support. OffPS gathers all the characteristics of a system and has several configurations, subsystems, and functions. In many cases, OffPS is subjected to extreme forces imposed by offshore weather conditions. For this reason, it is duly enclosed, protected and supported by several offshore-resistant mechanical machines such as ships, submarines, offshore platforms, floating ports and power plants. The prevalence of OffPS has been geographically expanding in recent years and it is spreading over vast portions of the oceans. On a larger extent, it is evolving towards an offshore power grid, a network that connects countries and even entire continents. It is also adjusting to multi-agent power access, in what is called multi-terminal OffPS. In order to encompass such a variety of topologies and applications, a generalized architecture of OffPS is proposed. It establishes a basic framework for this review on the latest research advances in offshore power generation, transmission, distribution, consumption, energy storage, offshore intelligence and environment. Nuances between specific offshore power systems, influences of the surrounding environment, interactions between OffPS subsystems and other research areas are then described within this framework. The aim of this review is to facilitate a better systemic understanding of the major challenges and importance of offshore power system research in contemporary society.

Electrical conductors are, arguably, the most important component of industrial and commercial electrical distribution systems as electrical conductors, or just conductors, facilitate the interconnections between a source (e.g., generator, renewable energy source, utility, electrical distribution panel, etc.) and load (e.g., wall outlet, control panel, motor, etc.). However, for typical electrical system installations, electrical conductors are rarely given the attention deserved. In general, the selection of the conductors becomes a fine balancing act of meeting the minimum National Electric Code (NFPA 70) requirements for minimum ampacity and the economics of choosing aluminum versus copper. This can lead to conditions where conductors are not adequately sized for the conditions they are exposed to, which can result in premature failure of cable insulation. This paper presents a simple approach to proper conductor selection, with an emphasis on maximizing the thermal life of cable insulation.

There is now an increased interest among electric utility companies to implement advanced distribution management system (ADMS) software platform in their regular operational activities. ADMS provides smart grid functionalities, such as conservation voltage reduction, demand response, fault location, isolation, and service restoration (FLISR), etc. The ADMS platform improves power quality, increases reliability and resiliency, and manages the integration of distributed energy resources (DERs) effectively. This paper proposes a fault location scheme as a part of the FLISR program based on measurement from wireless sensors and smart meters deployed throughout the feeder. Sensors and smart meters use wireless sensor network and advanced metering infrastructure (AMI), respectively. The fault location scheme also considers the bi-directional power flow complexities with the integration of PV-based DERs at the edges of the grid. Initially, a modified depth first search is utilized to reduce the search space to find the faulty zone(s) bounded by two sensor buses; next, a voltage profile analysis is conducted in the specified zone(s) to find the actual faulty section(s). To demonstrate the robustness, the entire algorithm is tested in a utility scale feeder with various loading, DER penetration, and fault resistance condition using Monte-Carlo method.

A Battery Energy Storage System (BESS) is a reliable resource to provide energy for various power system applications. The BESS can increase the flexibility and reliability of the renewable energy dispatch. Wind energy has the largest contribution among renewable energy resources and its control has become a research focus in power systems area. This paper introduces a novel BESS control to manage the net energy exchange between a wind farm and the grid in an electricity market. A Receding Horizon Control (RHC) scheme is proposed for optimal operation of the BESS in the presence of operational constraints. The proposed method seeks a decision policy to manage operation of the BESS to increase daily profits. Utilizing short-term wind and price forecasts provide valuable information for the BESS controller to obtain the best times to charge batteries, discharge the stored energy, or purchase energy from the DA market. An optimization problem is formulated considering BESS costs and operational constraints. This optimization problem, at each time step, is solved using the RHC scheme. All wind and electricity price data and case studies in this paper are based on MISO energy market data.

Assessment of conservation voltage reduction (CVR) deployment largely depends on load-to-voltage sensitivity (LTV). Therefore, two quantifiers—voltage and the load consumption—are truly responsible for measuring the CVR factor (CVRF), which is the primary index for calculating the load or energy consumption reduction. Simultaneously, CVR deployment improves the voltage profile by providing line relief due to the reduction in consumption. Hence, it is also important for the utilities to know the voltage sensitivity factor (VSF) during the CVR deployment period. This paper provides a comprehensive comparison of two different voltage sensitive load models—ZIP and exponential model—to estimate the CVRF and the VSF. The comparison is done in a time varying manner since the parameters of load models vary with time as the voltage changes. Two different sets of dual-stage filtering methods are used to estimate the load parameters and observe which load model accurately identifies the CVRF and the VSF. Stochasticity in load variation is also considered in the estimation process. This process helps the utilities to assess the CVRF and VSF for a target feeder using the most accurate load model. The entire estimation is done using actual data from a 22.86 kV distribution feeder, provided by duke energy progress. Moreover, the validation of the estimated parameters is done using the original circuit associated with the measurements in OpenDSS Software.

The growing integration level of wind and solar energy resources introduces new regulating and operating challenges in the electric grid. Ramp-rate limits of conventional power plants in the generation mix impose an operating constraint on renewable energy sources to the point that, at high integration levels, the ramp-rates of wind and solar resources must be managed by situational awareness tools that are based on forecasts, especially the ramp events forecasts. To leverage such tools, a post-processing adjusting approach is developed in this paper for improving the capability of hour-ahead combined forecasts of solar power to capture ramp events. The performance evaluation is conducted with several evaluation metrics that consider the accuracy of forecasts in terms of ramp events. The results of a case study demonstrate the efficacy of the adjusting approach. Probabilistic forecasts are also generated to quantify the uncertainty associated with the solar power ramp event forecasts and the uncertainty analysis is carried out.

A novel model-free method for detecting coherency of generators in power systems by means of the Wavelet Packet Decomposition (WPD) and the Recurrence Quantification Analysis (RQA) is developed. The time-series rotor angles of generators are used in this approach to detect coherent group of generators. Noise is a critical issue that needs to be considered when evaluating the performance of the coherency detection methods based on monitored data. Coherent groups of generators are detected under different disturbances and the results appear to be robust in the presence of noise. Simulation data from a 29-machine 179-bus system model are applied and results based on this method presented.