Hydro One (Canada)

The use of graphene as a high-performance concrete additive is attractive; but, its cost and concerns about production scalability and dispersion efficiency in concrete are impediments to widespread use. This study explores the impact of ultra-low dosages ( ≤ 0.02 % by mass of binder) of two novel graphene types—fractal graphene (FG) and reactive graphene (RG)—produced through a cost-effective, environmentally friendly, and scalable process, on the rheological properties of 3D-printable concrete. Both FG and RG significantly enhance the dynamic and static yield stresses and viscoelastic properties of the binder, with RG-modified mixtures exhibiting slightly more pronounced enhancements due to the presence of functional groups. Temporal evolution of static yield stress (τ s ) and storage modulus (G’) reveal aspects relating to structural build-up facilitated by the graphene particulates (structuration parameter from τ s -t, and rate of structural build-up, and residual structural factor from G’-t relations), that are important in extrusion and shape stability. Experimental buildability tests on hollow cylinders reveal that the selected ultra-low graphene dosages more than double the achievable build heights at 30, 60, and 90 min of mixing. This enhancement is further corroborated by an analytical model for plastic collapse, which incorporates plastic yield stress derived from green compression testing. Finally, this paper introduces an approach wherein the storage modulus and its evolution—determined through oscillatory rheology experiments—serve as versatile indicators of key rheological properties essential for material delivery, extrusion, and layer build-up in concrete 3D printing. This methodology holds promise for paving the way toward a standardized rheological test for 3D-printable binders.

This paper proposes a new optimization method based on enhanced genetic algorithm (GA) and Monte Carlo simulation (MCS) techniques, which are simultaneously applied to size regular spare transformer (RST) and mobile unit substations (MUS) stocks for distribution substations. The aim is to serve a group of electrical energy distribution substations to mitigate possible losses caused by load curtailments due to major failures that affect the substation transformers. The proposed method includes the use of resources such as MUS and load transfer, in addition to representing the expansion of the transformers group in operation and the increase in power demand, over a specified planning horizon, considering all waiting times inherent to system actions, e.g.,: RST installation, MUS connection, stock replenishment, etc. Two real systems with different characteristics are used to illustrate the proposed method, allowing the analysis of results obtained from different scenarios and parameters.

This work studies the cost-effectiveness of vehicle-to-grid (V2G) in residential applications and presents a self-maintained and scalable solution for optimizing the scheduling of a cluster of V2G units distributed across multi-unit residential buildings (MURBs). The mathematical model takes into account both the revenue and costs associated with a self-committed V2G technique, serving electric markets for ancillary services and regulation. The proposed mixed-integer non-linear programming (MINLP) model establishes cost-effectiveness boundaries for V2G techniques, to maximize availability at minimal cost and minimize degradation of electric vehicle (EV) batteries. The outcomes demonstrate that utilizing V2G for the sole purpose of selling electricity to the grid may not be an optimal choice for households equipped with small-rated charging units. However, the prospect of V2G adoption becomes more promising in MURBs, where the deployment of larger V2G units with higher power outputs is well-suited for efficient utilization during peak hours. The study provides valuable insights into the potential benefits and limitations of V2G integration in diverse residential settings.

In recent years, two probabilistic methods based on Markov models were developed at Hydro One for evaluating the economic benefits of using mobile unit transformers (mobile unit substations) in redundant customer delivery systems. One method used two Markov models and the other used three Markov models. Each assessment method convolved its Markov models to compute the mobile unit transformer benefits. This paper extends those two methods of assessment to handle the general case where the mobile unit transformers are utilized at one and two transformer load stations. An example of a sample system is used to illustrate the two extended methods of assessment and to compare their results.

Operational asset management describes the asset management function which deals with the shortest time horizon, and relates to asset decision-making at its most granular level. Decisions revolve around prioritizing which devices to target with replacements, which maintenance activities to choose, and when is the best time to perform them. Operational asset management follows an ongoing process in which understanding failure modes, with their probabilities and consequences, is essential. Condition assessment is fundamental to deciding what mitigations to apply and when. A range of testing and data collection methods are necessary to determine which populations are in what condition. Failure modes and associated risks vary with asset type and severity and therefore require different types of remediation.Investment in gathering equipment condition and criticality data is necessary to provide a quantitative basis for prioritization of maintenance actions and investment. Operational asset management focuses on near-term asset investment planning, such as more versus less maintenance, time-based versus condition-based versus reliability centered maintenance (RCM) etc. and mixtures of these, asset repair versus end-of-life management, asset overloading vs. loss of life decision-making.Operational asset managers use asset registries and asset data management tools, asset condition assessment, asset monitoring data, and interpretation, health indices, criticality, to justify and rank near-term asset investment decisions, as well as spares and spare parts utilization/planning.

This chapter applies the criteria-based risk-rating methodology to a stabilised income producing real estate (IPRE) asset. In rating a commercial real estate entity, the assessment is not on the property owner but on the IPRE, and the building blocks of the borrower risk rating (BRR) scorecard reflect this key difference. The BRR scorecard consists of just Business Risk (defined by tenant quality, lease maturity, and condition) and Financial Risk (defined by debt service coverage and loan-to-value ratio). The chapter explains how to set up a spreadsheet to forecast net operating income and the balance sheet, and offers a framework to create the descriptors, which drive the BRR. The chapter explains valuation methods and the capitalisation rate, and shows the sensitivity of property valuations to various assumptions.

This chapter represents a big leap from expert-judgement modelling to purely quantitative/statistical modelling. The two approaches are vital and complementary tools in a bank’s risk assessment toolbox. The chapter examines the structure of the linear probability model and probit and logit analysis, shows the similarity and differences, and applies the methods to a sample of companies. It also provides step-by-step guidance to formulate a logit model, and explains how to perform a logit regression using actual data and interpret the logit regression results. As with all models, including expert-judgement models, the stability or reliability of the estimated parameters, descriptors, and weights is not a constant, which makes model validation necessary and essential. Poor validation can be costly to a lender.

This chapter shows that the fiber optic sensing has several advantages over the traditional measurement technologies. One of the most significant disadvantages of the distributed sensing technology is the cost. If there is no need to measure temperature at several different locations, the deployment of conventional thermocouples will be a fraction of the cost as compared to a FO system. One of the main reasons why power cable temperature measurement of is desirable is because it very strongly influences the transmission capacity as the cable ampacity is primarily determined by the allowable conductor temperature. Procurement, installation, and testing of DTS systems are certainly important and can be relatively complex, but at least equally important is deployment of the sensing fibers to obtain optimum results. Since the majority of new high‐voltage cables are equipped with fiber optic sensors, the question arises how to employ FOSs in the existing lines.

Power quality monitors are typically deployed for power quality standard compliance survey power quality troubleshooting triggered by customer complaint. However, the rich data collected by power quality monitors can provide additional value to asset owners and system operators for purposes further than power quality itself. This paper presents utility experiences in asset condition assessment and operation anomaly detection by using power quality data analytics. Transformer ferroresonance, renewable generator instability and breaker restrike are selected to exemplify the usefulness of power quality data in applications beyond passive power quality monitoring.