244 research outputs found
Generation Scheduling in Microgrids under Uncertainties in Power Generation
Recently, the concept of Microgrids (MG) has been introduced in the distribution network. Microgrids are defined as small power systems that consist of various distributed micro generators that are capable of supplying a significant portion of the local demand. Microgrids can operate in grid-connected mode, in which they are connected to the upstream grid, or in isolated mode, where they are disconnected from the upstream grid and the local generators are the only source of power supply. In order to maximize the benefits of the resources available in a microgrid, an optimal scheduling of the power generation is required. Renewable resources have an intermittent nature that causes uncertainties in the system. These added uncertainties must be taken into consideration when solving the generation scheduling problem in order to obtain reliable solutions.
This research studies the scheduling of power generation in a microgrid that has a group of dispatchable and non-dispatchable generators. The operation of a microgrid during grid-connected mode and isolated mode is analyzed under variable demand profiles. Two mixed integer linear programming (MILP) models for the day-ahead unit commitment problem in a microgrid are proposed. Each model corresponds to one mode of operation. Uncertainty handling techniques are integrated in both models. The models are solved using the General Algebraic Modeling System (GAMS). A number of study cases are examined to study the operation of the microgrid and to evaluate the effects of uncertainties and spinning reserve requirement on the microgrid’s expenses
Investigation on electricity market designs enabling demand response and wind generation
Demand Response (DR) comprises some reactions taken by the end-use customers to decrease
or shift the electricity consumption in response to a change in the price of electricity or a
specified incentive payment over time. Wind energy is one of the renewable energies which
has been increasingly used throughout the world. The intermittency and volatility of
renewable energies, wind energy in particular, pose several challenges to Independent
System Operators (ISOs), paving the way to an increasing interest on Demand Response
Programs (DRPs) to cope with those challenges. Hence, this thesis addresses various
electricity market designs enabling DR and Renewable Energy Systems (RESs) simultaneously.
Various types of DRPs are developed in this thesis in a market environment, including
Incentive-Based DR Programs (IBDRPs), Time-Based Rate DR Programs (TBRDRPs) and
combinational DR programs on wind power integration. The uncertainties of wind power
generation are considered through a two-stage Stochastic Programming (SP) model. DRPs are
prioritized according to the ISO’s economic, technical, and environmental needs by means of
the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The
impacts of DRPs on price elasticity and customer benefit function are addressed, including
the sensitivities of both DR parameters and wind power scenarios. Finally, a two-stage
stochastic model is applied to solve the problem in a mixed-integer linear programming (MILP)
approach. The proposed model is applied to a modified IEEE test system to demonstrate the
effect of DR in the reduction of operation cost.A Resposta Dinâmica dos Consumidores (DR) compreende algumas reações tomadas por estes
para reduzir ou adiar o consumo de eletricidade, em resposta a uma mudança no preço da
eletricidade, ou a um pagamento/incentivo específico. A energia eólica é uma das energias
renováveis que tem sido cada vez mais utilizada em todo o mundo. A intermitência e a
volatilidade das energias renováveis, em particular da energia eólica, acarretam vários
desafios para os Operadores de Sistema (ISOs), abrindo caminho para um interesse crescente
nos Programas de Resposta Dinâmica dos Consumidores (DRPs) para lidar com esses desafios.
Assim, esta tese aborda os mercados de eletricidade com DR e sistemas de energia renovável
(RES) simultaneamente. Vários tipos de DRPs são desenvolvidos nesta tese em ambiente de
mercado, incluindo Programas de DR baseados em incentivos (IBDRPs), taxas baseadas no
tempo (TBRDRPs) e programas combinados (TBRDRPs) na integração de energia eólica. As
incertezas associadas à geração eólica são consideradas através de um modelo de
programação estocástica (SP) de dois estágios. Os DRPs são priorizados de acordo com as
necessidades económicas, técnicas e ambientais do ISO por meio da técnica para ordem de
preferência por similaridade com a solução ideal (TOPSIS). Os impactes dos DRPs na
elasticidade do preço e na função de benefício ao cliente são abordados, incluindo as
sensibilidades dos parâmetros de DR e dos cenários de potência eólica. Finalmente, um
modelo estocástico de dois estágios é aplicado para resolver o problema numa abordagem de
programação linear inteira mista (MILP). O modelo proposto é testado num sistema IEEE
modificado para demonstrar o efeito da DR na redução do custo de operação
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