Least-Cost Planning and electricity industry reform in Australia

Least-Cost Planning played a key role in the development of the energy efficiency and renewable energy industries in the USA, It has not been widely used elsewhere, largely due to differences in other nations' regulatory environments and the emergence of competitive markets as the dominant paradigm for electricity planning, Least-Cost Planning, however may over valuable insights for creating regulatory framework for competitive electricity markers. This paper examines some lessons which may be extracted from an analysis of the Least-Cost Planning experience in the USA and suggests how these lessons might prove beneficial in guiding Australia's electricity industry reform, This analysis demonstrates how market-based reforms may be flawed if they ignore the history of previous reform processes.

Critical review of acid in situ leach uranium mining: 1. USA and Australia

The technique of in situ leach (ISL) uranium mining is well established in the USA, as well as being used extensively in Eastern Europe and the former Soviet Union. The method is being proposed and tested on uranium deposits in Australia, with sulfuric acid chemistry and no restoration of groundwater following mining. Test sites in the USA were required to restore groundwater to ascertain the extent of impacts and compare costs to alkaline ISL mines. The problems encountered include expensive and difficult restoration, gypsum precipitation, higher salinity and some heavy metals and radionuclides after restoration. One of the most critical issues is whether natural attenuation is capable of restoring groundwater quality and geochemical conditions in an acid leached aquifer zone. The history of acid ISL sites in the USA and Australia are presented in this study, with a particular focus on the demonstration of restoration of groundwater impacts.

Critical review of acid in situ leach uranium mining: 2. Soviet Block and Asia

The technique of in situ leach (ISL) uranium mining is well established in the USA, as well as being used extensively in Eastern Europe and the former Soviet Union. The method is being proposed and tested on uranium deposits in Australia, with sulphuric acid chemistry and no restoration of groundwater following mining. ISL mines in the former Soviet Union generally used acid reagents and were operated without due consideration given to environmental protection. At many former mine sites, the extent of groundwater contamination is significant because of high salinity, heavy metal and radionuclide concentrations compared with pre-mining and changes in the hydrogeological regime caused by mining. After the political collapse of the Soviet Union by the early 1990s, most uranium mines were shut down or ordered to be phased out by government policy. Programmes of restoration are now being undertaken but are proving technically difficult and hampered by a lack of adequate financial resources. The history and problems of acid ISL sites in countries of the former Soviet Union and Asia are presented in this study.

An architecture for an effective usage of data mining in business intelligence systems

Business Intelligence (BI) is one emergent area of the Decision Support Systems (DSS) discipline. Over the last years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. Therefore, a lack of an effective usage of DM in BI can be found in some BI systems. An architecture that pretends to conduct to an effective usage of DM in BI is presented.

Short-term electricity prices forecasting in a competitive market by a hybrid PSO-ANFIS approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn. (C) 2012 Elsevier Ltd. All rights reserved.

Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements

This paper deals with the establishment of a characterization methodology of electric power profiles of medium voltage (MV) consumers. The characterization is supported on the data base knowledge discovery process (KDD). Data Mining techniques are used with the purpose of obtaining typical load profiles of MV customers and specific knowledge of their customers’ consumption habits. In order to form the different customers’ classes and to find a set of representative consumption patterns, a hierarchical clustering algorithm and a clustering ensemble combination approach (WEACS) are used. Taking into account the typical consumption profile of the class to which the customers belong, new tariff options were defined and new energy coefficients prices were proposed. Finally, and with the results obtained, the consequences that these will have in the interaction between customer and electric power suppliers are analyzed.

VPPs information needs for effective operation in competitive electricity markets

The large increase of renewable energy sources and Distributed Generation (DG) of electricity gives place to the Virtual Power Producer (VPP) concept. VPPs may turn electricity generation by renewable sources valuable in electricity markets. Information availability and adequate decision-support tools are crucial for achieving VPPs’ goals. This involves information concerning associated producers and market operation. This paper presents ViProd, a simulation tool that allows simulating VPPs operation, focusing mainly in the information requirements for adequate decision making.

Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.