published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Economics, 1982. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY / Vita. / Bibliography: p. 325-332. / by Jeffrey Alan Dubin. / Ph.D.

Deregulation is a growing trend and the electricity industry has not escaped its reaches. With worldwide experiences spanning only thirty years, there is substantial interest in analyzing current and future market designs so that market power cannot be used to increase the price of electricity significantly. / This thesis analyzes market power in electricity markets through the notion of Nash equilibrium (NE) and, more specifically, through Supply Function Equilibrium (SFE). We will examine how SFE can be modified to incorporate capacity constraints on generators and generating companies (gencos) controlling more than one generator for a Poolco electricity market with marginal pricing. / A genco's supply function is assumed to be of the form gi=l-aibi . Gaming is done either with ai or bi only, while keeping the other parameter at true cost. Gaming with both variables cannot be analyzed since the problem would have too many degrees of freedom. For each possible generator output level (minimum output, maximum output, or in between), analytical methods are employed to determine all candidate Nash equilibria. Then, simulations are performed over the range of possible genco offers to determine whether these candidates meet the complete set of Nash equilibrium criteria, specifically whether any genco can or cannot improve its profit by gaming. / For various inelastic demand levels, study cases indicate that there are either no Nash equilibria or only one. In the multi-unit genco case, the price of electricity is found to be higher than in the case where each genco owns only one generator, illustrating the effect of market concentration on the price. Whether capacity constraints are considered or not, the price of electricity appears to be higher if gencos are allowed to game with bi instead of ai. / The inclusion of capacity constraints on generators and the consideration of the multi-unit genco case will allow for better genco modeling in a Poolco market with marginal pricing. In turn, this will lead to more accurate analysis of the effects of current and possible rules and regulations on the price of electricity.

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Universal, affordable and reliable electricity remains a key pillar towards achieving Sustainable Development Goals. It is low income countries that find bridging gaps in electricity access particularly challenging. Making judicious financial investments is critical in a low income setting as there are multiple competing compelling areas in which to make resource allocations. A data driven approach that can leverage prior data from electricity service providers can guide decision making. This dissertation presents approaches that leverage such data, to assist utilities and national bodies with insights that could be useful. There are five unique contributions made. These are in the form of key results about electricity consumption patterns, novel methodologies for electricity demand prediction and relevant metrics for estimating the cost of a grid connection. First, this thesis, through in-depth analysis of electricity data from thousands of households, sheds light on electricity consumption patterns in Rwanda and Kenya. This work revealed that utilities are increasingly connecting low consuming households whose consumption peaks sooner and plateaus lower than their peers who were connected earlier. While the previous focus of research has been on addressing electricity supply-side constraints, this work is the first of it's kind to show that electricity consumption for the newly electrified is very low, thereby making capital cost recovery of a grid connection even harder to achieve. This mismatch between supply and demand emphasizes the need for utilities to better quantify expected demand upon connection. Secondly, this thesis makes methodological contributions that support electricity demand prediction for the yet-to-be grid-connected households. Specifically, Convolutional Neural Network (CNN) models were designed to take as inputs pre-grid-access daytime satellite image patches and output electricity consumption levels. Results from this work show that the proposed methodologies perform better than utility based estimates of anticipated demand. This methodology shows that rapid large scale evaluation of latent demand can be effectively performed using daytime satellite imagery, thereby giving guidance on which sites or regions are more suitable for grid versus off-grid technologies. Outputs from the models have been utilized by energy planners in Kenya. The third unique contribution made in this dissertation is in the development of key metrics to estimate the cost of grid-access. Complementary to the evaluation of electricity demand, this thesis also develops an electricity grid network optimization model, connecting 9.2 million structures in Kenya. Given transformer placement and the estimates for low and medium voltage line, an approximation for the per household wire requirement is obtained. The work shows that traditional rural/urban classification based on population density may not be enough and is often deceiving in estimating the cost of grid-access and a new categorization based on our proposed per household wire requirement metrics provides more relevant estimates on the total cost. Fourthly, this dissertation also demonstrates methods to re-purpose electricity data in order to provide insights to new domains such as household wealth. This work illustrates how household overall expenditure can be obtained from electricity usage data and how electricity usage can be obtained from daytime satellite imagery. This methodological contribution provides a pathway for stakeholders to estimate household overall expenditure from daytime satellite imagery. The work shows the value of electricity data in answering other questions in new domains without the deployment of additional surveys or hardware. The final research contribution discussed in this thesis focuses on methods to make smart modifications to existing machine learning models to support analysis in settings where label availability is small and label quality is poor. This concept is illustrated with a building segmentation task given misaligned and omitted building footprints. Our proposed end-to-end learning pipeline demonstrates how data constrained regions can learn about building characteristics despite having incomplete and noisy labels. In addition, this work is used to provide explanatory features to the CNNs used for prediction in the earlier parts of the work. While the focus of the research was on Kenya and Rwanda, this work transcends multiple domains such as water and internet access and can be extending to countries seeking evidence-based approaches to inform sustainable development.

In chapter 1, I study long term contracts in retail electricity markets. Deregulation of retail electricity markets gives consumer choices over contracts of different lengths. Long term contracts allow consumers to hedge against future price increase, but they can be more expensive than spot contracts. There is little empirical evidence on how consumers value long term contracts. Using a dataset from an incumbent retailer containing 10-year panel of consumer contract choice data, this paper analyzes consumers' valuations of long term contracts. I first document that a significant percentage of consumers actively choose long term contracts when they are more expensive than shorter contracts. To quantify the value of long term contracts and welfare implication of product innovation after retail deregulation, I build and estimate a dynamic model that incorporates risk preference, price expectations and consumer inertia. Counterfactual calculation shows that on average consumers gain about 6% per month from long term contracts. In chapter 2, I quantify the effect of introducing large-scale renewable energy on the wholesale electricity market. Renewable energy capacity has increased in many markets as renewable is crucial to reduce emission in the energy sector. More than 8GWh of wind capacity has been added in Texas between 2014 and 2017. Using hourly data from Texas, I find increasing daily wind energy production results in statistically significant reduction of wholesale electricity price for all hours of the day except 10pm, and the effect is larger during peak hours. Increasing wind production reduces output from both coal and natural gas power plants. Using hours when no transmission limit is binding and load is above 50th percentile in the load distribution, I find increasing hourly wind production reduces offer prices submitted by owners of fossil fuel power plants. In chapter 3, I study the effect of transmission limit on market outcomes. Wholesale electricity markets are often subject to transmission constraints that prevent efficient dispatch of power. Increasing renewable capacity demands transmission infrastructure investment. In 2011 to 2013, Electricity Reliability Council of Texas (ERCOT) constructed several high voltage transmission lines from the wind-rich west Texas to demand centers. Using data on electricity production, demand, price and information on grid congestion, this paper shows that an increase of 100MW in the transmission limit from the West to the North reduces the hourly output of fossil fuel generators in the North by 71.1MWh and decreases the price in the North by 0.17$/MWh when the transmission constraint from the West to the North is binding. Meanwhile, the increase of the transmission limit reduces dispatch of coal and combined cycle gas power plants in the North, but increases production of simple cycle and steam gas power plants in the North.

Orientador: Carlos Alberto de Castro Júnior / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-21T03:54:49Z (GMT). No. of bitstreams: 1 Tomiyama_EliasKento_M.pdf: 3080864 bytes, checksum: 7293fe7b3271c5c27e9b967c48a4e245 (MD5) Previous issue date: 2012 / Resumo: Esta dissertação de mestrado apresenta quatro metodologias de alocação de custos da transmissão: Pro rata (PR), Divisão Proporcional (PS), Zbus-médio (Zbus_AVG) e Nodal. Enquanto a primeira desconsidera o aspecto locacional e aloca os custos baseado apenas na quantidade de potência produzida pelos geradores e consumida pelas cargas, as últimas três levam em conta este aspecto, ou seja, as tarifas pagas por geradores e cargas dependem do seu ponto de conexão na rede elétrica. Através de simulações computacionais são levantadas várias situações de operação no sentido de avaliar a influência e as possíveis implicações de ordem regulatória, política, econômica e social de um país provocadas pela inclusão do aspecto locacional na definição das tarifas de uso do sistema de transmissão. Por fim, mostra-se a possibilidade de se considerar as tarifas pagas pelos agentes do sistema como um critério de decisão a mais no problema do planejamento da expansão da transmissão / Abstract: This dissertation describes four transmission cost allocation methodologies: Pro rata (PR), Proportional sharing (PS), Zbus-average (Zbus_AVG) and Nodal. While the first one disregards the locational aspect and allocates costs based only on the amount of power delivered by generators and consumed by loads, the last three ones take this aspect into account, i.e. charges are dependent on where generators and demands are connected in the network. Several computer simulations were made in order to assess the influence of the locational aspect into transmission pricing scheme and the results were used for a critical analysis, including political, regulatory, economic and social aspects. Finally, we discuss the possibility of using the fees paid by transmission system agents as an additional criterion for the Transmission Expansion Planning problem / Mestrado / Energia Eletrica / Mestre em Engenharia Elétrica