Electric Utility Resource Planning: Economics, Reliability, and Decision-Making


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Most people—including many legislators, regulators, and other decision makers in the electric utility industry—have misconceptions about how electric utilities really "work" and plan for the future. This lack of understanding can lead to poorly informed decisions and policies that directly affect the choices utilities must make.

Taking readers step by step through this process, the book in the following order : "Creates" a hypothetical utility Explains how and why a utility operates its system of generating units Discusses the planning methods that a utility would or should use Guides readers through each stage of a planning analysis for the hypothetical utility, examining various resource options conservation, new power plants, and solar In addition, the author introduces four Fundamental Principles of Resource Planning that should guide utilities.

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Featuring contributions from worldwide leaders in the field, the carefully crafted Electric Power Generation, Transmission, and Distribution, Third Edition part of the five-volume set, The Electric Power Engineering Handbook provides convenient access to detailed information on a diverse array of power engineering topics. Topics covered include: Electric power generation: nonconventional methods Electric power generation: conventional methods Transmission system Distribution systems Electric power utilization Power quality L.

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Hydro Power 6. Sean White. Whether or not you are taking the NABCEP Associate Exam, learning the material covered in this book is the best investment you can make insuring your place and moving up in the solar industry. Amory Lovins. Certain regions are subject to frequent adverse weather events of sufficient intensity to cause damage to the electrical networks, requiring human intervention to verify and eliminate the causes of such disruption and thus leading to the creation of emergency service orders. These orders, with a high degree of uncertainty and urgency, along with commercial orders, with a previously defined route, become a task of great relevance for the network operation center of the electric distribution systems and qualifying even more the highly dynamic environment involved.

The key issue for the resource planning, with regard to the service crews, refers to an estimation of service time that allows for more assertive planning possible, improving the crew throughput.

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Considering the need to define a typical behaviour to those orders, this chapter proposes a predictive modelling of emergency services for resource planning when considering the geographic dispersion of such services and also the time windows that comprise the amount of service time demanded. The methodology to resource planning presented in this chapter is based on the predictive modelling of emergency service by defining random variables to denote how much service time is demanded in each portion of the whole geographic area considered and also how important such demands may be in the sense of reliability indexes, as number of customers and the amount of power not supplied.

Following this definition of random variables, one may obtain the service time to attend emergency services stratified by each region and also restricted to a certain time window, that is, 1-h interval. With this information, a decision-making process may be conducted to define the number of hours demanded by maintenance crews that will be needed to attend all the services in the 1-h interval.

The whole picture when assuming 1 day demand will be possible by collecting all these 1-h intervals, furnishing the geographic location and the on-site service time to a possible proactive-routing approach.


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In order to show how the methodology may be applied, a case study is developed considering a horizon of emergency service occurrences, from a given Brazilian Power Utility. The purpose of this case study is not only about the past but also about how to update the case study of actual occurrences in such a way that they may be considered for future estimations.

The process of conducting statistical analyses to construct the time series for each given 1-h interval is depicted in a graphic user interface also allowing the timeline description of the estimation of service time dispersed over the geographic area assumed, followed by the decision-making process of selecting the most relevant random variables to denote the service time demanded.

Although the advent of smart grids has conducted to increasing the level of automation [ 1 ], the need for human intervention is particularly necessary in cases of extreme weather events or even in the occurrence of collisions in overhead networks, which become exposed by itself and may cause the lack of power supply.


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This interruption refers to the switching from normal to the emergency operating condition [ 2 ], in all of the networks or even in a restricted part of it, conducting customers to the lack of power supply and affecting continuity indexes. In addition, companies still have the billing process affected by not addressing the load demand for a certain period. The use of repair crews becomes imperative precisely to overcome the limitations that interfere with the remote control equipment, since the insulation of the defect can even be performed automatically but its correction requires human interference.

From this crew management comes the well-known emergency dispatching problem to repair crews already scheduled with pre-programmed services, which leads to a change of course and describes the problem of dynamic routing of vehicles [ 3 ]. Exactly in the context of service operations in electric power distribution utilities, the problem of this work is defined in [ 4 ]. The nature of the optimization problem has its roots on minimizing the waiting time between the detection of the defect and the arrival of crews at the service location, because the less this time the less the time without supply, thus contributing to the reliability of the electric power distribution system.

Another aspect worth mentioning is the random nature of events: the occurrence of emergency orders. In case of a reactive system, each new pending emergency order, or a set of them, gives rise to the problem of dynamic routing [ 5 ]. In addition, the routing problem associated with the context of this work also presents an important particularity: the service time in each service location, which is exactly the estimated service time for resolution.

It follows that the dispatch problem is closed to the problem of minimum latency [ 6 ]. This chapter aims to anticipate the occurrence of emergency services in order to provide a proactive approach to dispatch, in which emergency occurrences can be assumed to be probable and therefore likely to be included in routing solutions of programmed orders. The routing problem is usually addressed by assuming that attributes customer demands, travel and service times, orders criticality are previously known.

However, most of the real cases present some level of uncertainty [ 8 ]. Anticipating these uncertainties already in planning phase is important because it allows a more precisely routing plan and favouring the crew management, operational cost reduction and also avoiding penalties from regulatory agencies through improvement of reliability indicators and the revenue from the energy sale, by reducing supply disruptions. Exactly, this last class is that related to this work, referred many times as partially dynamic, this approach assumes that some of the unknown data are in stochastic format.

A proactive planning can be realized by transforming the stochastic knowledge in dummy consumers with expected service time and temporal and spatial position. With such definitions, the next section describes how to address the uncertainty by performing demand-forecasting techniques. Considering the external service orders of electric power utilities, we treat two different types of situations, the commercial and the emergency orders.

The commercial ones have a typical input behaviour, known deadline and, as a consequence of these characteristics, can be planned in advance, without technical or legal problems involving power failures. By contrast, the emergency orders are randomly generated, with the corresponding dynamic character [ 10 ].

Electric Utility Resource Planning: Economics, Reliability, and Decision-Making Electric Utility Resource Planning: Economics, Reliability, and Decision-Making
Electric Utility Resource Planning: Economics, Reliability, and Decision-Making Electric Utility Resource Planning: Economics, Reliability, and Decision-Making
Electric Utility Resource Planning: Economics, Reliability, and Decision-Making Electric Utility Resource Planning: Economics, Reliability, and Decision-Making
Electric Utility Resource Planning: Economics, Reliability, and Decision-Making Electric Utility Resource Planning: Economics, Reliability, and Decision-Making
Electric Utility Resource Planning: Economics, Reliability, and Decision-Making Electric Utility Resource Planning: Economics, Reliability, and Decision-Making
Electric Utility Resource Planning: Economics, Reliability, and Decision-Making Electric Utility Resource Planning: Economics, Reliability, and Decision-Making
Electric Utility Resource Planning: Economics, Reliability, and Decision-Making Electric Utility Resource Planning: Economics, Reliability, and Decision-Making

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