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The first part of this two-part paper developed the framework for characterizing the feasibility regions of power systems in the demand space. This characterization, however, leads to the generation of a large number of extraneous constraints as an unwelcome byproduct. This shortcoming motivates the second part of this paper series, whose objective is the achievement of a minimal representation for loadability sets through an offline process. Thus, in this paper, we set forth to eliminate efficiently redundant constraints with the proposal of an enhanced umbrella constraint discovery (E-UCD) problem formulation. The use of E-UCD in this paper is fourfold: 1) It serves to identify redundant line flow constraints not potentially shaping the feasibility regions of power systems in the generation-demand space. 2) It serves to determine the maximum number of line flow limits that could ever become active simultaneously in a given power system. 3) It pinpoints the generators who have the ability to become pivotal in relieving network congestion. 4) It is used to identify redundant constraints generated, while generation dispatch variables are projected from the generation-demand space onto the demand space. Experiments are carried out on the standard IEEE test systems to show that the minimal representations of loadability sets contain a reasonable number of constraints. Thus, the application of loadability sets to operation and planning problems will result in computational savings. [ABSTRACT FROM AUTHOR]

There are few systematic methodologies capable of predicting and leveraging the reserve capacity potential of large populations of thermostatically-controlled loads (TCLs). For such reserves to have economic and technical value, it is essential that demand-side flexibility aggregators and system operators be able to do so quickly and robustly. In addition, aggregators need to ensure TCL users' comfort and end use satisfaction. Part I of this two-part paper introduces an analytical approach to characterize and control the statistical bounds on the potential aggregated response of a population of TCLs, while ensuring users' comfort satisfaction. First, the uncertainty associated with the instantaneous power consumption of a typical TCL is described by a set of random variables and their statistics. TCL statistics are then employed to characterize the exploitable flexibility from a large population of similar devices. From this, a control strategy and parameters are introduced for sporadic (i.e., contingency-type) reserve provision by the population. In Part II, the proposed analytical approach and control strategy are validated for the special case of electric water heaters (EWHs). Further, the trade-off between demand response capacity and EWH users' comfort satisfaction is investigated through several case studies. [ABSTRACT FROM AUTHOR]

The emergence of smart grid technologies in terms of advanced communication infrastructure, embedded intelligence, diagnostics and monitoring capabilities offers new opportunities for improved transmission asset management strategies (TAMS). Accordingly, power system operators are currently looking for analytics that can make use of transmission asset condition monitors and data already available to make better-informed decisions. This two-part paper introduces a two-stage maintenance scheduler for power transmission assets. Part I begins with the motivation for TAMS and then continues with a two-stage maintenance management model that incorporates joint midterm and short-term maintenance. The first stage involves a midterm asset maintenance scheduler that explicitly considers the asset condition dynamics in terms of failure rate. The second stage introduces a short-term maintenance scheduler with N-1 reliability that schedules the output of the midterm maintenance scheduler in the short run. The midterm and short-term stages are completely decoupled schemes to make the problem computationally tractable. For the sake of exposition here, we focus on the maintenance of grid transformers. The proposed methodology is general, however, and can be extended to other network equipments as well. The characteristics of the proposed model and its benefits are investigated in Part II through several case studies. [ABSTRACT FROM PUBLISHER]

A two-stage framework for transformer maintenance management is introduced and formulated in Part I of this two-part paper in the context of transmission asset management strategies (TAMS). The proposed model optimizes maintenance outage schedule over a predefined period of time by taking into account the actual and expected transformer assets' condition dynamics in terms of failure rate and resource limitations in midterm horizons, as well as operating constraints, economic considerations and N-1 reliability in the shorter term. In Part II, a small six-bus system is first used to demonstrate how the two-stage maintenance framework works using a step-by-step procedure. Then, IEEE-RTS is used to investigate the performance of the proposed model in more detail. In addition, the impacts of varying the characteristics of the proposed midterm and short-term maintenance schedulers, such as flexibility in time horizon selection, on maintenance scheduling results and computational efficiency are investigated on IEEE-RTS. The numerical studies indicate that the proposed framework gives appropriate results in terms of economics and technical constraints at a reasonable computational cost. [ABSTRACT FROM PUBLISHER]

Automation is acknowledged by distribution utilities as a successful investment strategy to enhance reliability and operation efficiency. However, practical approaches that can handle the complex decision-making process faced by decision makers to justify the long-term financial effects of distribution automation have remained scarce. An automated and remote-controlled sectionalizing switch play a fundamental role in an automated distribution network. This paper introduces a new optimization approach for distribution automation in terms of automated and remotely controlled sectionalizing switch placement. Mixed-integer linear programming (MILP) is utilized to model the problem. The proposed model can be solved with large-scale commercial solvers in a computationally efficient manner. The proposed sectionalizing switch placement problem considers customer outage costs in conjunction with sectionalizing switch capital investment, installation, as well as annual operation and maintenance costs. The effectiveness of the proposed approach is tested on a reliability test system and a typical real size system. The presented results indicate the accuracy and efficiency of the proposed method. [ABSTRACT FROM PUBLISHER]

The concerns of regulatory authority for the cost efficiency and service quality of distribution utilities has led them to implement monetary schemes, such as penalty/reward mechanism (PRM). PRM provides explicit financial incentives for distribution system operators (DSO) to maintain or improve their efficiency and quality of service. Although the effectiveness of the PRM is proven, analytical tools that can handle the complex decision-making process faced by decision makers to respond to PRM are scarce. This paper presents an approach for distribution system maintenance management in the presence of the PRM. The proposed approach tailors the maintenance plans by taking into account the equipment condition in terms of failure rate, cost of planned and unplanned outage, as well as labor and material cost in conjunction with the incentives provided by PRM. New insights are also gained regarding the effects of PRM on the process of decision making by DSO. [ABSTRACT FROM AUTHOR]

By the introduction of competitive supply, modern electric utilities are forced to operate their systems closer to their security limits. Under such fragile conditions, any disturbance could endanger system security and lead to system collapse. There is, therefore, a need to develop new methods which could evaluate optimal scheduling of operating reserve and forewarn the system operators to take necessary preventive actions in case need arises. An effective method is proposed in this paper to optimally allocate spinning reserve based on a well-being model. The method used in conventional well-being framework for classifying power system comfort zone into healthy and marginal states is initially revised. The optimal value of health index and system spinning reserve requirements are then evaluated on the basis of a trade off between the total cost of spinning reserve and the expected cost of energy not served. The effectiveness of the proposed method is examined using the IEEE-RTS. [ABSTRACT FROM AUTHOR]