1,721,104 research outputs found
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Electrification of isolated communities in Mexico : The case of wind energy systems
Full text linkThe Mexican government, supported by international organizations, has financed a myriad of rural electrification projects. However, high costs and a lack of trained staff in isolated regions have obstructed the expansion of the grid to all rural areas, particularly in the poorest regions of the country.
This study focuses on rural villages in the State of Chiapas, Mexico, and compares the amount required to connect each isolated town to the national grid to the cost to build an independent system powered by wind energy systems.
It was concluded that, in most cases, the use of a microgrid is the best solution; the application of wind energy systems must be complemented by training in O&M activities for local inhabitants, and by the allocation of financial resources as grants for funding.Earth and Planetary Science
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Generation capacity expansion planning using screening curves method
Full text linktextGeneration capacity expansion planning has been evolving in rencent
decades. First, the long-term planning procedure is taking more detailed
considerations of short-term operation impacts. Second, as more renewable
resources being integrated into the grid, a new strategy of dealing with the
non-dispatchable renewable energy should be developed, with more ancillary
services needing to be procured from thermal units. These trends are expected
to continue.
This thesis describes a methodology in generation capacity expansion
planning. The screening curves method can be used to estimate optimal generation
mix for a target year. This thesis first introduces three screening curves
methods, which are classified based on their ability to deal with detailed shortterm
operational issues. It then includes ancillary service and wind integration
impacts. Finally, it presents a case study of a projected ERCOT 2030 system.Electrical and Computer Engineerin
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Frequency control adequacy for increasing levels of variable generation
Full text linktextThe integration of signi cant levels of variable generation into the electricity grid has increased the complexity of power system operations. The
strong unpredictability of variable generation poses an important operating
complexity and demands an adequate dimensioning and deployment of system reserves. This work establishes su cient conditions for the dimensioning
and deployment of adequate reserves. These conditions involve the determi-
nation of reserve requirements and the design of a frequency control system
consistent with such requirements. The analysis is divided into the adequacy
of primary and secondary reserves, and simulations of ERCOT validated by
empirical data are considered. Adequacy criteria from current practices are
used to evaluate the performance of the formulation.Electrical and Computer Engineerin
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Integrated energy risk management models for electric utility companies
Full text linkThis dissertation presents models for integrated energy risk management for electric utility companies (EUCs). First, two fundamental market
factors in deregulated electricity markets (electricity demand and price) are
proposed and detailed studies of the correlation between electricity load and
natural gas price reveals some interesting results. Second, an optimal natural
gas supply selection framework based on modern utility theory is proposed.
The framework is the first integrated risk management model to address the
optimal fuel supply problem, which has been much more difficult and critical
to EUCs in deregulated electricity markets. The framework can be extended
for use in various time frame and as a benchmarking tool for trader’s strategy.
Thirdly, a framework to determine the feasible structures and find out the
optimal insurance on generation forced outages (IGFO) contracts for EUCs is
developed and its benefits to EUCs are discussed.Electrical and Computer Engineerin
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Electricity market forecast using machine learning approaches
Full text linkElectricity generation and load should always be balanced to maintain a tightly regulated system frequency in the power grid. Electricity generation and load both depend on many factors, such as the weather, temperature, and wind. These characteristics make the dynamics of electricity price very different from that of any other commodities or financial assets. The electricity price can exhibit hourly, daily, and seasonal fluctuations, as well as abrupt unanticipated spikes. Almost all electricity market participants use wind/load/price forecasting tools in their daily operations to optimize their operation plans, and bidding and hedging strategies, in order to maximize the profits and avoid price risks. However, the unreliable and inaccurate predictions with current forecasting tools have caused many serious problems, which can cause system instabilities and result in extreme prices even in the absence of scarcity. This dissertation presents an implementation of state of the art machine learning approaches into the forecasting tools to improve the reliability and accuracy of electricity price prediction.
Most existing wholesale electricity markets consist of a Day-Ahead Market and a Real-Time Market that work together to ensure the adequacy of electricity generation capacity for the Real-Time operation to secure the reliability of the grid. The two markets have different purposes, with the Day-Ahead Market serving as preparation for and hedging against variation in the Real-Time Market. Also, the Day-Ahead Market uses hourly Day-Ahead forecasting information and the Real-Time Market uses most up-to-date Real-Time information when running calculations. So the forecasting strategies of Day-Ahead and Real-Time Markets should be different as well. The dissertation has two parts. The first part focuses on Day-Ahead price forecasting and the second part focuses on Real-Time price forecasting.Electrical and Computer Engineerin
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Models for electricity market efficiency and bidding strategy analysis
Full text linktextThis dissertation studies models for the analysis of market efficiency and
bidding behaviors of market participants in electricity markets. Simulation models are
developed to estimate how transmission and operational constraints affect the
competitive benchmark and market prices based on submitted bids. This research
contributes to the literature in three aspects. First, transmission and operational
constraints, which have been neglected in most empirical literature, are considered in
the competitive benchmark estimation model. Second, the effects of operational and
transmission constraints on market prices are estimated through two models based on
the submitted bids of market participants. Third, these models are applied to analyze the
efficiency of the Electric Reliability Council Of Texas (ERCOT) real-time energy
market by simulating its operations for the time period from January 2002 to April
2003. The characteristics and available information for the ERCOT market are
considered.
In electricity markets, electric firms compete through both spot market bidding
and bilateral contract trading. A linear asymmetric supply function equilibrium (SFE)
model with transmission constraints is proposed in this dissertation to analyze the
bidding strategies with forward contracts. The research contributes to the literature in
several aspects. First, we combine forward contracts, transmission constraints, and
multi-period strategy (an obligation for firms to bid consistently over an extended time
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horizon such as a day or an hour) into the linear asymmetric supply function
equilibrium framework. As an ex-ante model, it can provide qualitative insights into
firms’ behaviors. Second, the bidding strategies related to Transmission Congestion
Rights (TCRs) are discussed by interpreting TCRs as linear combination of forwards.
Third, the model is a general one in the sense that there is no limitation on the number
of firms and scale of the transmission network, which can have asymmetric linear
marginal cost structures. In addition to theoretical analysis, we apply our model to
simulate the ERCOT real-time market from January 2002 to April 2003. The effects of
forward contracts on the ERCOT market are evaluated through the results. It is shown
that the model is able to capture features of bidding behavior in the market.Electrical and Computer Engineerin
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Multicriteria generation and transmission expansion planning in Paraguay
Full text linkThe purpose of this research is to develop a methodology using welfare economics expanded with multi attribute decision making for portfolio selection of renewable sources in Paraguay’s regulated hydroelectric market. This approach considers expansion planning of the Paraguayan Interconnected System (SIN), including generation, transmission, and 2016 Paraguay’s Energy Policy. This optimization of the generation expansion problem involves a study period from 2017-2040 assuming a rate of 9.84% per year increasing demand. This study models the SIN using Stochastic Dynamic Dual Programming (SDDP), a probabilistic hydrothermal operation cost optimizer. Based on this Base Case, generation expansion is needed after 2026. Using the Optimization Expansion-Operation Module (OPTGEN-SDDP) software to solve the Expansion Case Optimization, after 2029 the transmission system cannot sustain the demand increase. By 2040, the new installed capacity needed is 26,900 MW. By 2040, there is a need for 13,571 MVA transmission expansion to connect mostly South systems to the Metro system. At 2014 prices, the generation expansion would be at a cost of $7.1 million per MVA of new generation capacity. A mixed-integer linear optimization formulation is implemented outside the OPTGEN-SDDP Module. The multicriteria expansion problem is analyzed using a utility function to consider socio-economical, technology and environmental criteria of energy policy interest. The result is then incorporated back to the OPTGEN-SDDP Module. Analyzing a sustainability index for each case, in all cases the Net Import index has a decreasing trend in the period of study. Furthermore, due to transmission constraints, the Reliability index cannot be improved without transmission expansion in any study case. The resultant generation portfolio in both expansion problems includes 26% solar generation, a scale in line with Paraguay’s Energy Policy of diversification of the energy matrix.
Small hydro and solar generation sources are a viable alternative to build an electricity generation portfolio mix for the Paraguayan electricity market, by using both a welfare economics optimization and an extended welfare economics optimization with a multi attribute decision making approach. But transmission constraints are still a major issue for the full exploitation of these resources.Energy and Earth Resource
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Transmission expansion planning considering substation arrangements
Full text linkTransmission expansion planning (TEP) is aimed at expanding the existing transmission system to satisfy potential power demand growth and future power plant expansion. Generally speaking, the TEP problem can be mathematically modeled as a large scale, non-convex, and non-linear optimization problem. Uncertainties causing by development of renewable energy, electricity market, and load fluctuations are also taken into consideration. The tradition TEP problem can be solved using stochastic mixed integer linear programming and contingency analysis. However, the practical application of TEP problems generates some questions.
This thesis mainly focuses on certain restrictions ignored by traditional TEP problem formulation, which are important in practice and will change the optimal solution completely. By adding certain restrictions based on spacing arrangements on substations, TEP problems can be solved more efficiently and will be more valuable for industry.Electrical and Computer Engineerin
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Electric transmission system expansion planning for the system with uncertain intermittent renewable resources
Full text linktextThis dissertation proposes a new transmission planning method for electric power systems with large planned additions of uncertain intermittent renewable resources. The major contribution of this dissertation is applying stochastic programming that represents two uncertain parameters, wind and load, to transmission planning. We apply an ad hoc partition method to approximate the bivariate random variables of load and wind. A two-stage stochastic transmission planning problem is repeatedly solved by replacing continuous random variables with approximations that have a more refined partition at each iteration. A candidate solution is provided when improvement is not observed at an optimal value, even with more refined approximations. Numerical results show the efficiency of the method. However, if the number of samples is not sufficient to represent the original random variable's characteristics, the solution may be poor. Therefore, we employ a sampling method using Gaussian copula in order to generate as many random samples as necessary. The problem is replicated and solved using a fixed number of samples generated by Gaussian copula. In order to asses solution quality, a 95\%-confidence interval on the optimality gap is formed. A candidate stochastic solution for transmission investment is used to simulate the operation of a utility-scale storage system. A mixed integer program (MIP) is applied to this formulation. As a case study, the Electric Reliability Council of Texas (ERCOT) wind and load data is employed, along with a simplified model of the transmission system. Energy storage is also considered. The storage operation shifts wind power from off-peak hours to on-peak hours, and its wind power generation shows a close character to that of a base load generator.Electrical and Computer Engineerin
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Analyzing strategic behaviors in electricity markets via transmission-constrained residual demand
Full text linktextThis dissertation studies how to characterize strategic behaviors in electricity markets from a transmission-constrained residual demand perspective. This dissertation generalizes the residual demand concept, widely used by economists in general markets, to electricity markets, which are constrained by transmission networks. The transmission-constrained residual demand is characterized by a sensitivity analysis of the optimal power flow program, which is the electricity market clearing engine. Methods are proposed to optimize a generator or generation firm's profit utilizing the residual demand sensitivity information, which has several advantages over existing methods. The transmission-constrained residual demand concept and the methods are helpful for market participants to develop bidding strategies and for market monitors to analyze market power in electricity markets.Electrical and Computer Engineerin
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