Investment Optimization to Improve Power Distribution System Reliability Metrics.
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The paper focuses on parameter identification for time varying voltage dependent polynomial load models, namely the constant impedance (Z), constant current (I), and constant power (P), in the distribution systems. Typically, parameter identification has been done for aggregated loads at substations for the transmission planning studies. Given the higher voltage volatility faced by active distribution systems, this paper takes the load model estimation to the distribution networks. The paper assumes the presence of measurements at the distribution nodes from various sources, each at varying temporal resolutions. Least squares estimation (LSE) method with bounded variables is used to convert the measurements into ZIP parameter estimations for loads at various locations of the feeder. The results and discussions focus on some of the estimation issues faced in a distribution system due to varying voltage sensitivities of loads along a feeder, measurement resolution, impact of window size and sampling rate of measurement data, and the use of dynamic simulations to create synthetic measurement data in the absence of real ones.
The distribution system is going through significant changes. One of them is the high penetration of inverter-based distributed energy resources in the distribution system. Due to this high penetration, there is a need for a better protection system. Multiple organizations are working on creating novel protection techniques for distribution systems with high penetration of inverter-based DERs. One such innovative method is the use of traveling waves to detect and locate faults. Transmission system protection utilizes this approach, and commercial solutions exist for the transmission system. Using this approach in the distribution system is not straightforward and presents some challenges for implementation. This presentation will discuss the need for using wide-band models to model the traveling wave phenomenon in the distribution system.
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Quasi-static time-series (QSTS) simulation is used to simulate the behavior of distribution systems over long periods of time (typically hours to years). The technique involves repeatedly solving the load-flow problem for a distribution system model and is useful for distributed energy resource (DER) planning. When a QSTS simulation has a small time step and a long duration, the computational burden of the simulation can be a barrier to integration into utility workflows. One way to relieve the computational burden is to simplify the system model. The segment substitution method of simplifying distribution system models introduced in this paper offers model bus reduction of up to 98% with a simplification error as low as 0.2% (0.002 pu voltage). In contrast to existing methods of distribution system model simplification, which rely on topological inspection and linearization, the segment substitution method uses black-box segment data and an assumed simplified topology.
Due to the increasing penetration of distributed energy resources (DERs) required for the sustainable distribution system, new voltage control strategy is needed by utilities. Traditional voltage control strategy can not support the increasing number of DERs in a coordinated and scalable manner to meet the operational voltage regulation requirement. Supported by the power electronics converter, the energy storage system can provide fast, smooth, and flexible voltage control services. In this paper, an effective and easy to implement sensitivity-based voltage control strategy is developed for the energy storage system. The developed control strategy is validated using an industrial feeder data in Northwest Washington. The proposed strategy can mitigate the voltage unbalance issue, improve the voltage profile, and correct power factors while supporting sustainable distribution system operation.
The application of machine learning (ML) to power and energy systems (PES) is being researched at an astounding rate, resulting in a significant number of recent additions to the literature. As the infrastructure of electric power systems evolves, so does interest in deploying ML techniques to PES. However, despite growing interest, the limited number of reported real-world applications suggests that the gap between research and practice is yet to be fully bridged. To help highlight areas where this gap could be narrowed, this article discusses the challenges and opportunities in developing and adapting ML techniques for modern electric power systems, with a particular focus on power distribution systems. These systems play a crucial role in transforming the electric power sector and accommodating emerging distributed technologies to mitigate the impacts of climate change and accelerate the transition to a sustainable energy future. The objective of this article is not to provide an exhaustive overview of the state-of-the-art in the literature, but rather to make the topic accessible to readers with an engineering or computer science background and an interest in the field of ML for PES, thereby encouraging cross-disciplinary research in this rapidly developing field. To this end, the article discusses the ways in which ML can contribute to addressing the evolving operational challenges facing power distribution systems and identifies relevant application areas that exemplify the potential for ML to make near-term contributions. At the same time, key considerations for the practical implementation of ML in power distribution systems are discussed, along with suggestions for several potential future directions.
The Information and Communications Technology (ICT) for control and monitoring of power systems is a layer on top of the physical power system infrastructure. The cyber system and physical power system components form a tightly coupled Cyber–Physical System (CPS). Sources of vulnerabilities arise from the computing and communication systems of the cyber–power grid. Cyber intrusions targeting the power grid are serious threats to the reliability of electricity supply that is critical to society and the economy. In a typical Information Technology environment, numerous attack scenarios have shown how unauthorized users can access and manipulate protected information from a network domain. The need for cyber security has led to industry standards that power grids must meet to ensure that the monitoring, operation, and control functions are not disrupted by cyber intrusions. Cyber security technologies such as encryption and authentication have been deployed on the CPS. Intrusion or anomaly detection and mitigation tools developed for power grids are emerging. Furthermore, this survey paper provides the basic concepts of cyber vulnerabilities of distribution systems and CPS security. The important ICT subjects for distribution systems covered in this paper include Supervisory Control And Data Acquisition, Distributed Energy Resources, including renewable energy and smart meters.
A software process is developed to convert distribution network models from a quasi-static time-series tool (OpenDSS) to a real-time dynamic phasor simulator (ePHASORSIM). The description of this process in this paper would be helpful for researchers who intend to perform similar conversions. The converter could be utilized directly by users of real-time simulators who intend to perform software-in-the-loop or hardware-in-the-loop tests on large distribution test feeders for a range of use cases, including testing functions of advanced distribution management systems against a simulated distribution system. In the future, the developers intend to release the conversion tool as open source to enable use by others.
Distributed generations (DGs) can act as emergency power supplies when distribution systems suffer from outages. However, the generation capabilities of DGs are generally limited by a number of factors including weather conditions, fuel limitations, etc. In this context, mobile resources that are able to reallocate resources to desired locations are regarded as important complements to conventional fixed DGs in assisting distribution system restoration. In this paper, a distribution system restoration model with DGs and mobile resources is proposed. Firstly, the dispatch and allocation of mobile resources are modeled with respect to the characteristics of the traffic network. Then the developed mobile resource models are integrated into the distribution system restoration model to co-optimize the scheduling of DGs and mobile resources. Uncertainty factors are managed by a model predictive control approach so that system operators can dynamically adjust the restoration strategy with the up-to-date information. The effectiveness of the proposed method is validated through an IEEE 13-bus test system.
Capacitor banks have been generally installed and utilized to support distribution voltage during period of higher load or on longer, higher impedance, feeders. Installations of distributed energy resources in distribution systems are rapidly increasing, and many of these generation resources have variable and uncertain power output. These generators can significantly change the voltage profile across a feeder, and therefore when a new capacitor bank is needed analysis of optimal capacity and location of the capacitor bank is required. In this paper, we model a particular distribution system including essential equipment. An optimization method is adopted to determine the best capacity and location sets of the newly installed capacitor banks, in the presence of distributed solar power generation. Finally we analyze the optimal capacitor banks configuration through the optimization and simulation results.
It is essential to improve the resiliency of power distribution systems (PDS) with the increase in extreme weather events, number of malicious threats and consumers’ need for higher reliability. Here, this paper provides a formal approach to evaluate the operational resiliency of PDS, and quantify the resiliency of a system using a code-based metric. A combination of steady state and dynamic simulation tools is used to determine the resiliency metric. Dynamic simulation tools help with analyzing impact of short-term events, which might affect operational resiliency in long term. A dynamic optimization algorithm for changing operating criteria to increase the sustainability of the most critical loads has been proposed. The proposed theoretical approach is validated using a simple power distribution system model and simulation results demonstrate the ability to quantify the resiliency using the proposed code-based metric. The time-dependent quantification of resiliency has been demonstrated on a test system of two connected CERTS microgrids.
Historically, distribution system planning studies mainly focused on steady state and quasi-steady state analysis, with limited attention paid to dynamic analysis. This paper develops three-phase, electromechanical models for both grid-forming and grid-following inverters, and integrates them into an open source, three-phase distribution network solver, thereby enabling dynamic simulation of large-scale, three-phase unbalanced distribution systems with high penetration of inverter-based DERs. The proposed inverter models are validated against electromagnetic simulation and field test data from the CERTS/AEP microgrid testbed, and simulated in an islanded 5252 node distribution system in the GridLAB-D simulation environment. Simulation verifies the effectiveness of the proposed inverter models for large-scale distribution system analysis. Finally, study results show that compared to traditional grid-following inverters, the high penetration of grid-forming inverters can improve the voltage and frequency stability of islanded distribution systems.
High impact low probability (HILP) events such as hurricanes, heat waves, and floods have instigated widespread power outages and blackouts around the globe in the past decade. With the increasing challenges concerning the threats to the power distribution systems and the growing need to mitigate the impacts of the HILP events, resilience has become a crucial requirement for the power grid infrastructures. Numerous efforts have been made to define, measure, and characterize the resilience of power distribution systems. This study thoroughly reviews the state-of-the-art methods on the existing resilience evaluation framework and metrics. Here, the desirable characteristics of resilience metrics are highlighted, and the challenges associated with formulating, developing, and calculating such metrics are discussed. Next, we detail the state-of-the-art literature on planning solutions to ensure distribution system resilience. This paper aims to extract a deep insight into this challenging and critical research area and envision future opportunities that can guide the power distribution system operators/planners to formulate more effective mitigation strategies to enhance the resilience of power distribution systems.
This paper studies the coordinated use of smart inverters with legacy voltage regulating devices to help increase the energy savings from conservation voltage reduction (CVR) in distribution systems with high photovoltaic penetration. Two voltage optimization algorithms are developed to evaluate the CVR benefit by considering two types of smart inverter functions: aggregated reactive power control and autonomous volt/VAR control. In each algorithm, smart inverters are coordinately controlled with the operation of legacy voltage regulating devices including load tap changers and capacitor banks. Both algorithms are tested on representative utility distribution system models. In conclusion, simulation results demonstrate that the proposed algorithms can achieve around 1.8-3.6% energy savings when only using legacy voltage regulating devices and an additional 0.3-0.9% when adding smart inverters.
This poster provides a high-level overview of evaluating the impacts of battery energy storage system functionalities on distribution systems using power hardware-in-the-loop simulation.
Modern distribution systems have increasing integration of distributed energy resources (DERs). A large number of distribution system DERs are interfaced through a power electronic converter. During grid contingency scenarios these resources have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid's restoration after an adverse event and hence enhancing resilience. In distribution systems, system restoration is an important problem, especially in face of high-impact low-frequency events. This paper proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in DERs for mitigating voltage problems. Simulation studies are performed on the IEEE-123 node feeder system to demonstrate the effectiveness of the proposed method for various cases.
Due to the newly issued standards on distributed energy resources (DERs), the droop-based operation modes (Q-V and P-V droops) become a requirement for all the interconnected DER units at the distribution level. Voltage references of the DER units can be assigned to improve the operational behavior of the distribution system in the smart grid context. This paper presents a novel formulation for optimally setting voltage reference of the droop-based DER units in distribution systems with the objective of minimizing the system ohmic loss. This problem is formulated as a mixed-integer quadratic convex optimization problem in MATLAB and solved by the MATLAB and CPLEX solvers. The IEEE 123-bus system and the EPRI test system Ckt5 are used to validate the proposed formulation. The result obtained from the proposed formulation is compared with the result of the original problem solved by the heuristic method. Based on the numerical study results, system loss can be reduced after the proposed voltage reference assignment. The computational expense of the new formulation is also much lower than that of the heuristic approach.