Electricity Distribution Networks Post-Liberalisation: Essays on Economic Regulation, Investment, Efficiency, and Business Model

This thesis investigates some of the key current economic and regulatory challenges pertaining to grid development. These issues include: investment drivers, the relationship between investment and static/dynamic efficiency, and integration of distributed energy resources as alternatives to traditional network reinforcement. The thesis comprises four essays and uses a range of techniques including theoretical and empirical analysis in Chapters 2, 3, and 4; as well as conceptual modelling in Chapter 5. A common feature of the first three chapters is the usage of a dataset composed of 129 Norwegian distribution companies, observed between 2004 and 2010. The issue of investment determinants and the responsiveness of companies to the regulators’ incentives for investment have been investigated in Chapter 2. This chapter uses a Bayesian Model Averaging technique (BMA) to identify the investment drivers in regulated firms. The results of the chapter provide an insight into investment behaviour of network companies under incentive regulation. The identified investment determinants shed light on the effectiveness of investment incentives and can be used to improve the process of capital cost treatment under incentive regulation. A theoretical framework for the relationship between investment and efficiency, including the concept of “no impact efficiency”, which is defined as the revenue-neutral efficiency effect of investment under total cost benchmarking, is introduced in Chapter 3. The observed efficiency effect of investment and no impact efficiency are estimated using a Stochastic Frontier Analysis (SFA) technique. The concept of no impact efficiency is important because it describes the process under which incentive regulation, with ex-post regulatory treatment of investment, achieves investment optimality. It also provides a useful benchmark for the sector regulators to examine the investment efficiency of regulated firms. Chapter 4 explores the concept of dynamic efficiency under incentive regulation. In this respect, the notion of “inefficiency persistence” due to presence of quasi-fixed inputs, under total cost benchmarking, is introduced. The theoretical framework shows that inefficiency of regulated companies is a combination of period-specific effects (shocks) and a carry-over component from previous periods due to sluggish adjustment of capital stocks and/or production capacity. The two components of inefficiency and the rate of inefficiency transmission between periods are estimated using a dynamic stochastic frontier model in a Bayesian framework. The results show that the persistence of inefficiency can seriously affect the companies’ short run productivity and, consequently, regulated revenues. This can lead to disincentives for investment and innovation. An innovative solution to the traditional demand-driven network investment is investigated in Chapter 5. The feasibility and advantages of adopting a portfolio of distributed energy resources including distributed generation, storage, demand response and energy efficiency as alternatives to grid capacity enhancement, have been discussed. Also, a market-oriented approach termed “contract for deferral scheme” (CDS) is introduced in order to integrate these resources under an extended business model of distribution companies. The CDS contract protects the developers of distributed resources from market risks, decreases the financing costs and improves commercial bankability of investments. Additionally, CDS acts as a proxy for vertical integration and helps distribution companies to improve the efficiency of their asset utilisation

Pushing the frontier : three essays on Bayesian Stochastic Frontier modelling

This thesis presents three essays in Bayesian Stochastic Frontier models for cost and production functions and links the fields of productivity and efficiency measurement and spatial econometrics, with applications to energy economics and aggregate productivity. The thesis presents a chapter of literature review highlighting the advances and gaps in the stochastic frontier literature. Chapter 3 discusses measurement of aggregate efficiency in electricity consumption in transition economies in a cost frontier framework. The underlying model is extended to a Spatial Autoregressive model with efficiency spillovers in Chapter 4, showing good performance in simulations. The model is applied to aggregate productivity in European countries, leading to evidence of convergence between eastern and western economies over time, as in the previous chapter regarding efficiency in electricity consumption. Finally, Chapter 5 proposes a spatial model which allows for dependence in the structure of the inefficiency component while accounting for unobserved heterogeneity. This approach is applied to New Zealand electricity distribution networks, finding some evidence of efficiency spillovers between the firms. All essays explore the performance of the model using simulations and discuss the utility of the approaches in small samples. The thesis concludes with a summary of findings and future paths of research

Power System State Estimation In Large-Scale Networks

Power system state estimation constitutes one of the critical functions that are executed at the control centers. Its optimal performance is required in order to operate the power system in a safe, secure and economic manner. State estimators (SE) process the available measurements by taking into account the information about the network model and parameters. The quality of estimated results will depend on the measurements, the assumed network model and its parameters. Hence. SE requires to use various techniques to ensure validity of the results and to detect and identify sources of errors. The Weighted Least Squares (WLS) method is the most popular technique of SE. This thesis provides solutions to enhance the WLS algorithm in order to increase the performance of SE. The gain and the Jacobian matrices associated with the basic algorithm require large storage and have to be evaluated at every iteration, resulting in more computation time. The elements of the SE Jacobian matrix are processed one-by-one based on the available measurements, and the Jacobian matrix, H is updated suitably, avoiding all the power flow equations. thus simplifying the development of the Jacobian. The results obtained proved that the suggested method takes lesser computational time compared with the available NRSE method, particularly when the size of the network becomes larger. The uncertainty in analog measurements could occur in a real time system. Thus, the higher weighting factor or wrongly assigned weighting factor to the measurement could lead to flag the measurements as bad. This thesis describes a pre-screening process to identify the bad measurements and the measurement weights before performing the WLS estimation technique employed in SE. The autoregressive (AR) techniques. Burg and Modified Covariance (MC), are used to predict the data and at the same time filtering the logical weighting factors that have been assigned to the identified bad measurements. The results show that AR methods managed to accurately predict the data and filter the weigthage factors for the bad measurements. Also the WLS algorithm is modified to include Unified Power Flow Controller (UPFC) parameters. The developed methods are successfully tested on IEEE standard systems and the Sabah Electricy Sdn. Bhd. (SESB) system without and with UPFC. The developed program is suitable either to estimate the UPFC controller parameters or to estimate these parameter values in order to achieve the given control specifications in addition to the power system state variables

Signal enhancement for automatic recognition of noisy speech

Also issued as Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (p. 81-84).Supported by the Staff Associate Program at MIT Lincoln Laboratory.Shawn M. Verbout