A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning

Demand response management in power systems using a particle swarm optimization approach

Competitive electricity markets have arisen as a result of power-sector restructuration and power-system deregulation. The players participating in competitive electricity markets must define strategies and make decisions using all the available information and business opportunities

Demand response management in power systems using a particle swarm optimization approach

Competitive electricity markets have arisen as a result of power-sector restructuration and power-system deregulation. The players participating in competitive electricity markets must define strategies and make decisions using all the available information and business opportunities

Electric vehicle scenario simulator tool for smart grid operators

This paper presents a simulator for electric vehicles in the context of smart grids and distribution networks. It aims to support network operator´s planning and operations but can be used by other entities for related studies. The paper describes the parameters supported by the current version of the Electric Vehicle Scenario Simulator (EVeSSi) tool and its current algorithm. EVeSSi enables the definition of electric vehicles scenarios on distribution networks using a built-in movement engine. The scenarios created with EVeSSi can be used by external tools (e.g., power flow) for specific analysis, for instance grid impacts. Two scenarios are briefly presented for illustration of the simulator capabilities

Planejamento da produção de energia eólica considerando os preços marginais localizados

A utilização de energias renováveis tem se tornado cada vez mais comum em virtude do aumento da preocupação ambiental e da crescente necessidade energética. Dentre as fontes de energia renovável, as centrais eólicas surgem como uma das principais alternativas para produção de energia. O seu custo de operação é relativamente baixo quando comparado com o custo do investimento para a construção de uma central eólica. Neste sentido, este trabalho busca realizar o planejamento da produção eólica com base nos Preços Marginais Localizados (LMP). Os LMP fornecem importantes informações sobre as condições de operação de um sistema elétrico de energia, e por isso são utilizados neste trabalho como critério de decisão para a alocação de uma central eólica em um sistema de transmissão. Alguns casos de estudo foram criados utilizando dois sistemas modelo (9 e 30 barramentos), com o objetivo de avaliar os benefícios desta alocação com base nos LMP.The use of renewable energy has become each more common because of the increase of the environmental concern and the growing energy need. Among these renewable energy sources, the wind plants have appeared as one of the main alternatives to energy production. Its cost of operation is relatively low in comparation to the investment cost to build a wind plant. In this way, this work intends to make a wind production planning based on Local Marginal Prices (LMP). The LMP give important information about the operation conditions of a electric system, and that is why they are used in this work as selection criterion to the placement of a wind plant in a transmission system. Some study cases were created by using two model systems (9 and 30 buses), with the goal to evaluate the benefits of this allocation based on LMP