Multistage expansion planning of generation and interconnections with sustainable energy development criteria: A multiobjective model

A novel multiobjective, multiarea and multistage model to long-term expansion planning of integrated generation and transmission corridors incorporating sustainable energy developing is presented in this paper. The proposed MESEDES model is a “bottom-up” energy model which considers the electricity generation/transmission value-chain, i.e., power generation alternatives including renewables, nuclear and traditional thermal generation along with transmission corridors. The model decides the optimal location and timing of the electricity generation/transmission abroad the multistage planning horizon. The proposed model considers three objectives belonging to sustainable energy development criteria such as: (a) the minimization of investments and operation costs of power generation, transmission corridors, energy efficiency (demand side management (DSM) programs) considering CO2 capture technologies; (b) minimization of Life-Cycle Greenhouse Gas Emissions (GHG); and (c) maximization of the diversification of electricity generation mix. The proposed model consider aspects of the carbon abatement policy under the CDM - Clean Development Mechanism or European Union Greenhouse Gas Emission Trading Scheme. A case study is used to illustrate the proposed framework.     

面向国际合作的全球性灵活碳减排价格机制研究

如何形成一个稳定、有效的碳减排国际合作机制是目前国际社会关注的重要问题。分析了京都议定书在全球碳减排合作方面存在的问题,构建了一个全球性灵活碳减排价格机制,基于各国的碳价格水平,通过价格激励模型和清洁发展激励模型,引导各国提高碳价格,促使全球碳价格平均值等于全球碳价格目标值。通过预测性的实例分析,研究全球性碳减排价格机制对中国、美国、欧盟和印度的影响。分析结果表明,全球性碳减排价格机制可操作性较强,能够满足碳排放限额与交易机制所需的灵活性,而且,全球性灵活碳减排价格机制并未引起较高的社会成本。     

Pattern recognition as a tool to support decision making in the management of the electric sector. Part II: A new method based on clustering of multivariate time series

This work presents a new method for the clustering and pattern recognition of multivariate time series (CPT-M) based on multivariate statistics. The algorithm comprises four steps that extract essential features of multivariate time series of residential users with emphasis on seasonal and temporal profile, among others. The method was successfully implemented and tested in the context of an energy efficiency program carried out by the Electric Company of Alagoas (Brazil) that considers, among others, the analysis of the impact of replacing refrigerators in low-income consumers’ homes in several towns located within the state of Alagoas (Brazil). The results were compared with a well-known method of time series clustering already established in the literature, the Fuzzy C-Means (FCM). Unlike C-means models of clustering, the CPT-M method is also capable to obtain directly the number of clusters. The analysis confirmed that the CPT-M method was capable to identify a greater diversity of patterns, showing the potential of this method in better recognition of consumption patterns considering simultaneously the effect of other variables in additional to load curves. This represents an important aspect to the process of decision making in the energy distribution sector.