Amir Abiri-Jahromi

Optimized Sectionalizing Switch Placement Strategy in Distribution Systems

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.

A Two-Stage Framework for Power Transformer Asset Maintenance Management—Part I: Models and Formulations

Summary form only given. 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.

An Efficient Mixed-Integer Linear Formulation for Long-Term Overhead Lines Maintenance Scheduling in Power Distribution Systems

This paper presents an efficient mixed-integer linear formulation for long term maintenance schedule of overhead lines. The proposed formulation is based on risk management approach and utilizes the model of decoupled risk factors. The proposed methodology yields a significant computational saving comparing to the previously reported dynamic programming. Risk management approach enables the asset managers to consider the actual condition of electrical equipment and expected consequence of their failures. Furthermore, the decoupled risk strategy in conjunction with the mixed-integer linear programming formulation establishes a precise description of time-dependent deterioration failure rate and provides the ability to determine the most cost-effective maintenance scenario while satisfying the reliability constraints. The proposed approach is tested on the Roy Billinton Test System distribution feeders and a typical real size case study. The results presented show the accuracy and efficiency of the proposed approach.

Optimal Scheduling of Spinning Reserve Based on Well-Being Model

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.

Incorporating Service Quality Regulation in Distribution System Maintenance Strategy

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.

Risk based maintenance optimization of overhead distribution networks utilizing priority based dynamic programming

This paper presents a priority based dynamic programming approach for long term maintenance scheduling of overhead distribution networks. The proposed approach is based on risk management approach and utilizes the model of decoupled risk factors. Two heuristic factors are defined and utilized in order to establish a maintenance priority list and to curtail the dynamic programming search space. The proposed methodology yields a significant computational saving compare to the previously reported dynamic programming. Risk management approach enables the asset managers to consider the actual condition of electrical equipments and expected consequence of their failures. Furthermore, the decoupled risk strategy in conjunction with the piecewise modeling of failure rate establishes a precise description of time-dependent deterioration failure rate and provides the ability to determine the most cost-effective maintenance scenario. The proposed approach is applied to the RBTS distribution feeders and a typical real size case study. The results presented show the accuracy and efficiency of the proposed approach.

Reliability-constrained unit commitment using stochastic mixed-integer programming

This paper proposes a stochastic mixed-integer programming (SMIP) model for the reliability-constrained unit commitment (RCUC) problem. The major objective of the paper is to examine both features of accuracy and efficiency of the proposed SMIP model of RCUC. The spinning reserve of generating units is considered as the only available reserve provision resource; however, the proposed formulation can be readily extended to comprise the other kind of reserve facilities. Expected load not served (ELNS) and loss of load probability (LOLP) are accommodated as the reliability constraints. Binding either or both reliability indices ensures the security of operation incorporating the stochastic nature of component outages. In this situation, the spinning reserve requirement is no longer considered explicitly. The Monte Carlo simulation method is used to generate scenarios for the proposed SMIP model. The scenario reduction method is also adopted to reduce computation burden of the proposed method. The IEEE reliability test system (RTS) is employed to numerically analyze the proposed model and the implementation issues are discussed. The simulations are conducted in the single- and multi-period bases and the performance of the model is investigated verses different reliability levels and various numbers of scenarios.

On the Loadability Sets of Power Systems—Part II: Minimal Representations

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.

A two-stage framework for power transformer asset maintenance management—Part I: Models and formulations

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.

Characterizing statistical bounds on aggregated demand-based primary frequency control

An analytical approach is proposed in this paper to characterize statistical bounds and uncertainties associated with the aggregated response of frequency-sensitive Thermostatically Controlled Loads (TCLs) participating in primary frequency control. A set of random variables is first introduced to exemplify the intrinsic uncertainty associated with the instantaneous power consumption of a single TCL in a population. Physically-based models, laboratory analysis or field measurement data can be employed to characterize the proposed random variables. Then, a bottom-up aggregation methodology and statistical theory are employed to characterize the aggregated response of a population of TCLs. Monte Carlo simulations are used to verify the correctness of the proposed analytics. The proposed methodology can be employed by system operators as well as demand response aggregators to predict the aggregated response of a population of TCLs participating in primary frequency control.