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This paper presents an optimization framework for sequential reconfiguration using an assortment of switching devices and repair process in distribution system restoration. Compared to existing studies, this paper considers types, capabilities and operational limits of different switching devices, making it applicable in practice. We develop a novel multi-phase method to find the optimal sequential operation of various switching devices and repair faulted areas. We consider circuit breakers, reclosers, sectionalizers, load breaker switches, and fuses. The switching operation problem is decomposed into two mixed-integer linear programming (MILP) subproblems. The first subproblem determines the optimal network topology and estimates the number of steps to reach that topology, while the second subproblem generates a sequence of switching operations to coordinate the switches. For repairing the faults, we design an MILP model that dispatches repair crews to clear faults and replace melted fuses. After clearing a fault, we update the topology of the network by generating a new sequence of switching operations, and the process continues until all faults are cleared. To improve the computational efficiency, a network reduction algorithm is developed to group line sections, such that only switchable sections are present in the reduced network. The proposed method is validated on the IEEE 123-bus and 8500-bus systems. [ABSTRACT FROM AUTHOR]

Customer-level rooftop photovoltaic (PV) has been widely integrated into distribution systems. In most cases, PVs are installed behind-the-meter (BTM), and only the net demand is recorded. Therefore, the native demand and PV generation are unknown to utilities. Separating native demand and solar generation from net demand is critical for improving grid-edge observability. In this paper, a novel approach is proposed for disaggregating customer-level BTM PV generation using low-resolution but widely available hourly smart meter data. The proposed approach exploits the strong correlation between monthly nocturnal and diurnal native demands and the high similarity among PV generation profiles. First, a joint probability density function (PDF) of monthly nocturnal and diurnal native demands is constructed for customers without PVs, using Gaussian mixture modeling (GMM). Deviation from the constructed PDF is utilized to probabilistically assess the monthly solar generation of customers with PVs. Then, to identify hourly BTM solar generation for these customers, their estimated monthly solar generation is decomposed into an hourly timescale; to do this, we have proposed a maximum likelihood estimation (MLE)-based technique that utilizes hourly typical solar exemplars. Leveraging the strong monthly native demand correlation and high PV generation similarity enhances our approach's robustness against the volatility of customers’ hourly load and enables highly-accurate disaggregation. The proposed approach has been verified using real native demand and PV generation data. [ABSTRACT FROM AUTHOR]

In modern distribution systems, load uncertainty can be fully captured by micro-PMUs, which can record high-resolution data; however, in practice, micro-PMUs are installed at limited locations in distribution networks due to budgetary constraints. In contrast, smart meters are widely deployed but can only measure relatively low-resolution energy consumption, which cannot sufficiently reflect the actual instantaneous load volatility within each sampling interval. In this paper, we have proposed a novel approach for enriching load data for service transformers that only have low-resolution smart meters. The key to our approach is to statistically recover the high-resolution load data, which is masked by the low-resolution data, using trained probabilistic models of service transformers that have both high- and low-resolution data sources, i.e., micro-PMUs and smart meters. The overall framework consists of two steps: first, for the transformers with micro-PMUs, a Gaussian Process is leveraged to capture the relationship between the maximum/minimum load and average load within each low-resolution sampling interval of smart meters; a Markov chain model is employed to characterize the transition probability of known high-resolution load. Next, the trained models are used as teachers for the transformers with only smart meters to decompose known low-resolution load data into targeted high-resolution load data. The enriched data can recover instantaneous load uncertainty and significantly enhance distribution system observability and situational awareness. We have verified the proposed approach using real high- and low-resolution load data. [ABSTRACT FROM AUTHOR]

Mobile emergency generators (MEGs) can effectively restore critical loads as flexible backup resources after power network disturbance from extreme events, thereby boosting the distribution system resilience. Therefore, MEGs are required to be optimally allocated and utilized. For this purpose, a novel three-stage stochastic planning model is proposed for MEG allocation of resilient distribution systems in consideration of planning stage (PLS), preventive response stage (PRS) and emergency response stage (ERS). Moreover, the nonanticipativity constraints are proposed to guarantee that the MEG allocation decisions are dependent on the stage-based uncertainties. Specifically, in the PLS, the intensity uncertainty (IU) of disasters and the outage uncertainty (OU) incurred by a given disaster are considered with probability-weighted scenarios for the effective MEG allocation. Then, with the IU that can be observed in the PRS, the MEGs are pre-positioned in the consideration of OU. It is noted that the pre-position decisions should only correspond to the IU realizations, according to nonanticipativity constraints. Last, with the further realization of OU in the ERS, the MEGs are re-routed from the pre-position to the target location, so that the provisional microgrids can be formed to restore critical loads. The proposed planning model can be large-scale due to multiple scenarios. Therefore, the progressive hedging algorithm (PHA) is customized to reduce the computational burden. The simulation results in 13 and 123 node distribution systems show the effectiveness and superiority of the proposed three-stage MEG planning model over the traditional two-stage model. [ABSTRACT FROM AUTHOR]

Rooftop solar photovoltaic (PV) power generator is a widely used distributed energy resource (DER) in distribution systems. Currently, the majority of PVs are installed behind-the-meter (BTM), where only customers’ net demand is recorded by smart meters. Disaggregating BTM PV generation from net demand is critical to utilities for enhancing grid-edge observability. In this paper, a data-driven approach is proposed for BTM PV generation disaggregation using solar and demand exemplars. First, a data clustering procedure is developed to construct a library of candidate load/solar exemplars. To handle the volatility of BTM resources, a novel game-theoretic learning process is proposed to adaptively generate optimal composite exemplars using the constructed library of candidate exemplars, through repeated evaluation of disaggregation residuals. Finally, the composite native demand and solar exemplars are employed to disaggregate solar generation from net demand using a semi-supervised source separator. The proposed methodology has been verified using real smart meter data and feeder models. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic optimization approach for disaster preparation in distribution systems. For an upcoming storm, utilities should have a preparation plan that includes warehousing restoration supplies, securing staging sites (depots), and prepositioning crews and equipment. Pre-storm planning enables faster and more efficient post-disaster deployment of crews and equipment resources to damage locations. To assist utilities in making this important preparation, this paper develops a two-stage stochastic mixed integer linear program. The first stage determines the depots, number of crews in each site, and the amount of equipment. The second stage is the recourse action that deals with acquiring new equipment and assigning crews to repair damages in realized scenarios. The objective of the developed model is to minimize the costs of depots, crews, equipment, and penalty costs associated with delays in obtaining equipment and restoration. We consider the uncertainties of damaged lines, number and type of equipment required, and expected repair times. The model is validated on modified IEEE 123-bus distribution test system. [ABSTRACT FROM AUTHOR]

Conservation voltage reduction (CVR) is widely adopted by utilities for peak demand reduction and energy savings through reducing the voltage level of the electrical distribution system. This paper presents an in-depth review on implementing and assessing CVR. The methodologies to quantify CVR effects are categorized into comparison-based, regression-based, synthesis-based and simulation-based methods. The implementation strategies for voltage reduction are classified into open-loop and closed-loop methods. The impacts of emerging smart-grid technologies on CVR are also discussed. The paper can provide researchers and utility engineers with further insights into the state of the art, technical barriers and future research directions of CVR technologies. [ABSTRACT FROM PUBLISHER]