# Optimal Replacement of Underground Distribution Cables

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1 1 Optimal Replacement of Underground Distribution Cables Jeremy A. Bloom, Member, IEEE, Charles Feinstein, and Peter Morris Abstract This paper presents a general decision model that enables utilities to generate business cases for asset management policies, with a specific application to underground distribution cables. The model takes a life-cycle costing approach that enables corporate financial managers and regulators to assess the multiyear financial impacts of maintaining specific classes of power delivery infrastructure assets, such as underground cables. The model specifies the evolution of the condition of the asset population over time, the various decision alternatives that are available, including testing, and the basic data needed to support the decision model. The decision model represents the dynamic process of underground cable deterioration mathematically, using a set of equations that provide a forecast of future deterioration. The dynamic equations describe the evolution of cable condition probabilistically given the current cable state, there will be a probability distribution of states in which the cable might be observed the next time it is inspected. The model represents the information obtainable from diagnostic tests and determines when it is cost-effective to use them. The model also specifies the data needed for decision-making. The model uses a dynamic programming formulation to solve for the optimal asset management strategy. Index Terms Business economics, Business planning, Management decision-making, Power cable testing, Power cables, Power distribution economics, Power distribution planning, Power system economics, Power system planning, Reliability management I I. INTRODUCTION ncreasing pressure from both customers and regulators to maintain and enhance service reliability, while at the same time controlling costs, has put many utilities distribution businesses in a classic dilemma of conflicting objectives. For that reason, asset management has become an increasingly important aspect of corporate business strategies. A significant focus of EPRI s asset management research in recent years has been to develop a rational basis for selecting repair or replacement options for specific classes of equipment that balances the risks of equipment failure against the costs of continued maintenance or capital replacement. This paper discusses methods for making these decisions with a focus on an asset of particular concern to many utilities, their J. A. Bloom is with the Electric Power Research Institute, Palo Alto, California ( C. D. Feinstein is Associate Professor, Leavey School of Business, Santa Clara University ( and with VMN Group LLC, Redwood City, California. P. A. Morris is with VMN Group LLC, Redwood City, California ( underground distribution cables. This paper describes a set of analytical models for dealing with the complexities of the underground cable management decisions. This framework: systematically captures the factors that influence the cost effectiveness of cable management policies identifies the key information needed for making good decisions provides an objective way to choose among decision alternatives enables calculating the cost and performance consequences of the policies. Managing a population of aging equipment requires considering three distinct phenomena: 1) representing the dynamic processes of failure and replacement of the equipment; 2) projecting changing failure rates as equipment ages; 3) balancing the costs of equipment failure with replacement options to come up with the least-cost equipment replacement policy. This paper presents a basic framework for asset management [4] that is applicable to many kinds of equipment. The following elements of this decision model are addressed in this paper: Objective, Decision Options, and Optimal Policies Condition Dynamics and State Definitions Failure Rates Expert Judgment Testing Data requirements II. OBJECTIVE, DECISION OPTIONS, AND OPTIMAL POLICIES The decision model addresses the question of what to do with cable assets as a function of their age and condition. The objective is to minimize the lifecycle cost of maintaining the underground cable inventory. The lifecycle cost comprises the total costs of the decisions taken throughout a multi-year time horizon on a present value basis. The decision options available for a given cable segment may include inspect/test, repair, rejuvenate using silicone injection, or replace. The unit to which these options may be applied is an underground cable segment, typically the span from manhole to manhole. The decision model represents these decisions as depending on the state of an underground cable segment. The specification of a decision for each state is called a policy. In general, the optimal policy will trade off the risk and cost of a cable failure, which increases with age and deteriorating condition,

5 5 the test outcomes. If the test results in a finding of no insulation degradation, no action is necessary. If the test finds mild, moderate, or severe degradation, it is optimal to rejuvenate at age 15 and to replace at 20 or 25. TABLE 5 EXAMPLE OPTIMAL POLICY WITH TESTING Failures Age No Action 5 No Action No Action 10 No Action No Action No Action 15 No Action Test Rejuvenate 20 No Action Test Replace 25 Test Replace Replace 30 Replace Replace Replace 35 Replace Replace Replace 40 Replace Replace Replace TABLE 6 EXAMPLE CONTINGENT TEST STRATEGY STRATEGY CONTINGENT ON TEST OUTCOME Shaded cells indicate change in policy due to test Age Failures No Degrade Mild Degrade Mod Degrade Severe Degrade 15 1 No Action Rejuvenate Rejuvenate Rejuvenate 20 1 No Action Replace Replace Replace 25 0 No Action Replace Replace Replace X. CONCLUSIONS EPRI has developed a decision framework that enables utilities to generate sound business cases for asset management policies. This framework takes a life-cycle costing approach that enables corporate financial managers and regulators to assess the multi-year financial impacts of maintaining specific classes of power delivery infrastructure assets, such as underground distribution cables. The analytical tools presented in this report share a basic framework for decision-making that specifies the evolution of the condition of the asset population over time, the various decision alternatives that are available, including testing, and the basic data needed to support the decision model. The decision model represents the dynamic process of underground cable deterioration mathematically, using a set of equations that provide a forecast of future deterioration. The decision model is flexible with respect to what data is available for decision-making. Using this decision model can reduce costs for managing critical power delivery assets. Case studies have indicated 25%-35% lifecycle cost savings for optimal policies compared to policies currently in use by utilities. Optimal policies can also improve reliability (e.g., SAIFI) by planning replacements of vulnerable equipment prior to failure. XI. ACKNOWLEDGMENT The authors gratefully acknowledge the contributions of Robert J. Keefe of EPRI and Dr. John Densley, P.Eng, IEEE Fellow, of ArborLec Solutions Inc. for contributing their expert judgment regarding parameters of the cable decision model. XII. REFERENCES [1] Asset Population Management and Testing Methodology Extensions, EPRI, Palo Alto, CA: [2] Cable Reliability Management Strategies, EPRI, Palo Alto, CA: [3] Equipment Failure Model and Data for Underground Distribution Cables, EPRI, Palo Alto, CA: [4] Guidelines for Intelligent Asset Replacement, Vol.3, Underground Distribution Cables, EPRI, Palo Alto, CA: [5] Medium Voltage Cable Failure Trends, EPRI, Palo Alto, CA: XIII. BIOGRAPHIES Dr. Jeremy A. Bloom (M 1979) is EPRI s program manager for power delivery asset management, where he is responsible for assisting utilities to get the most value out of their transmission and distribution assets, including project prioritization and aging infrastructure replacement. During his career at EPRI, he has been responsible projects ranging across integrated resource planning, energy efficiency and demand-side planning, markets and technology assessment, distributed resources, and strategic planning. Prior to joining EPRI, Dr. Bloom worked for General Public Utilities and Cornell University. Dr. Charles D. Feinstein is an Associate Professor in the department of Operations and Management Information Systems at Santa Clara University. His main areas of interest are optimization, dynamic systems, and risk analysis. With EPRI support, he has developed methods for identifying optimal policies for asset management, including repair and replace strategies; for electric utility project prioritization; and for capacity expansion using distributed resources. Dr. Peter Morris is cofounder of VMN Group LLC, a quantitative consulting company. He holds a faculty appointment as a Consulting Professor in the Department of Management Science and Engineering at Stanford University. Dr. Morris has over 30 years of experience in the research, teaching and application of decision analysis and quantitative modeling. Prior to cofounding VMN Group, he served as CEO of Applied Decision Analysis, Inc. Dr. Morris s areas of expertise include decision and risk analysis, probabilistic modeling and combining expert judgments. Dr. Morris originated the Bayesian approach to combining expert judgments, which is the foundation for a significant portion of current expert-aggregation research and government policy applications.

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