Optimal location of gas supply units in natural gas system network

Natural gas industry has been confronted with big challenges: great growth in demand, investments on new GSUs – gas supply units, and efficient technical system management. The right number of GSUs, their best location on networks and the optimal allocation to loads is a decision problem that can be formulated as a combinatorial programming problem, with the objective of minimizing system expenses. Our emphasis is on the formulation, interpretation and development of a solution algorithm that will analyze the trade-off between infrastructure investment expenditure and operating system costs. The location model was applied to a 12 node natural gas network, and its effectiveness was tested in five different operating scenarios.

A new approach for multi-agent coalition formation and management in the scope of electricity markets

This paper presents a new methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using the multi-agent market simulator MASCEM, taking advantage of its ability to provide the means to model and simulate VPP (Virtual Power Producers). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. The new features include the development of particular individual facilitators to manage the communications amongst the members of each coalition independently from the rest of the simulation, and also the mechanisms for the classification of the agents that are candidates to join the coalition. In addition, a global study on the results of the Iberian Electricity Market is performed, to compare and analyze different approaches for defining consistent and adequate strategies to integrate into the agents of MASCEM. This, combined with the application of learning and prediction techniques provide the agents with the ability to learn and adapt themselves, by adjusting their actions to the continued evolving states of the world they are playing in.

Risk Analysis and Behavior of Electricity Portfolio Aggregator

The scope of this paper is to adapt the standard mean-variance model of Henry Markowitz theory, creating a simulation tool to find the optimal configuration of the portfolio aggregator, calculate its profitability and risk. Currently, there is a deep discussion going on among the power system society about the structure and architecture of the future electric system. In this environment, policy makers and electric utilities find new approaches to access the electricity market; this configures new challenging positions in order to find innovative strategies and methodologies. Decentralized power generation is gaining relevance in liberalized markets, and small and medium size electricity consumers are also become producers (“prosumers”). In this scenario an electric aggregator is an entity that joins a group of electric clients, customers, producers, “prosumers” together as a single purchasing unit to negotiate the purchase and sale of electricity. The aggregator conducts research on electricity prices, contract terms and conditions in order to promote better energy prices for their clients and allows small and medium customers to benefit improved market prices.

Multidimensional scaling analysis of electricity market prices

This paper studies the impact of energy and stock markets upon electricity markets using Multidimensional Scaling (MDS). Historical values from major energy, stock and electricity markets are adopted. To analyze the data several graphs produced by MDS are presented and discussed. This method is useful to have a deeper insight into the behavior and the correlation of the markets. The results may also guide the construction models, helping electricity markets agents hedging against Market Clearing Price (MCP) volatility and, simultaneously, to achieve better financial results.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which performs realistic simulations of the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day, the type of day (business day, weekend, holiday, etc.) and most important, the renewable based distributed generation forecast. The proposed approach is tested and validated using real electricity markets data from the Iberian operator – MIBEL.

Status of Non-Technical Losses of Electricity in Brazil

The electricity demand in Brazil has been growing. Some studies estimate that through 2035 the energy consumption (the power consumption) should increase 78%. Two distinct actions are necessary to meet this growth: the construction of new generating plants and to reduce electrical losses in the country. As the construction of power plants have a high price, coupled with the growth of (current) environmental concern, electric utilities are investing in reducing losses, both technical and non-technical. In this context, this paper aims to present an overview of nontechnical losses in Brazil and to raise a discussion on the reasons that contribute to energy fraud.

Study of Proposed Identification of Non-Technical Losses of Electricity in Brazil

The electric utilities have large revenue losses annually due to commercial losses, which are caused mainly by fraud on the part of consumers and faulty meters. Automatic detection of such losses where there is a complex problem, given the large number of consumers and the high cost of each inspection, not to mention the wear of the relationship between company and consumer. Given the above, this paper aims to briefly present some methodologies applied by utilities to identify consumer frauds.

Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

Analysis of Electricity Market Prices Using Multidimensional Scaling

This paper studies the impact of the energy upon electricity markets using Multidimensional Scaling (MDS). Data from major energy and electricity markets is considered. Several maps produced by MDS are presented and discussed revealing that this method is useful for understanding the correlation between them. Furthermore, the results help electricity markets agents hedging against Market Clearing Price (MCP) volatility.

A green future for European electricity? :Energy sources, policies and further determinants of the household price of electricity

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

The regional electricity generation mix in Scotland: A portfolio selection approach

Standalone levelised cost assessments of electricity supply options miss an important contribution that renewable and non-fossil fuel technologies can make to the electricity portfolio: that of reducing the variability of electricity costs, and their potentially damaging impact upon economic activity. Portfolio theory applications to the electricity generation mix have shown that renewable technologies, their costs being largely uncorrelated with non-renewable technologies, can offer such benefits. We look at the existing Scottish generation mix and examine drivers of changes out to 2020. We assess recent scenarios for the Scottish generation mix in 2020 against mean-variance efficient portfolios of electricity-generating technologies. Each of the scenarios studied implies a portfolio cost of electricity that is between 22% and 38% higher than the portfolio cost of electricity in 2007. These scenarios prove to be “inefficient” in the sense that, for example, lower variance portfolios can be obtained without increasing portfolio costs, typically by expanding the share of renewables. As part of extensive sensitivity analysis, we find that Wave and Tidal technologies can contribute to lower risk electricity portfolios, while not increasing portfolio cost.

IPH response to Department, Trade and Investment (NI) draft strategic energy framework

The draft Framework set out the proposed priorities for Northern Ireland's energy future over the next ten years or so and illustrates the key energy goals in term of competitiveness, security of energy supply, sustainablilty and infrastructure investment. It also proposes new and ambitious renewable electricity and renewable heat targets by 2020, which reflect the need for effected action against climate change and the need to address other policy goals in terms of security and sustainability of supply and costs.

Format of medical order sheet improves security of antibiotics prescription: The experience of an intensive care unit.

OBJECTIVE: To assess whether formatting the medical order sheet has an effect on the accuracy and security of antibiotics prescription. DESIGN: Prospective assessment of antibiotics prescription over time, before and after the intervention, in comparison with a control ward. SETTING: The medical and surgical intensive care unit (ICU) of a university hospital. PATIENTS: All patients hospitalized in the medical or surgical ICU between February 1 and April 30, 1997, and July 1 and August 31, 2000, for whom antibiotics were prescribed. INTERVENTION: Formatting of the medical order sheet in the surgical ICU in 1998. MEASUREMENTS AND MAIN RESULTS: Compliance with the American Society of Hospital Pharmacists' criteria for prescription safety was measured. The proportion of safe orders increased in both units, but the increase was 4.6 times greater in the surgical ICU (66% vs. 74% in the medical ICU and 48% vs. 74% in the surgical ICU). For unsafe orders, the proportion of ambiguous orders decreased by half in the medical ICU (9% vs. 17%) and nearly disappeared in the surgical ICU (1% vs. 30%). The only missing criterion remaining in the surgical ICU was the drug dose unit, which could not be preformatted. The aim of antibiotics prescription (either prophylactic or therapeutic) was indicated only in 51% of the order sheets. CONCLUSIONS: Formatting of the order sheet markedly increased security of antibiotics prescription. These findings must be confirmed in other settings and with different drug classes. Formatting the medical order sheet decreases the potential for prescribing errors before full computerized prescription is available.

Assessing the viability of power-supply systems: a tentative protocol

This Technical Report presents a tentative protocol used to assess the viability of powersupply systems. The viability of power-supply systems can be assessed by looking at the production factors (e.g. paid labor, power capacity, fossil-fuels) – needed for the system to operate and maintain itself – in relation to the internal constraints set by the energetic metabolism of societies. In fact, by using this protocol it becomes possible to link assessments of technical coefficients performed at the level of the power-supply systems with assessments of benchmark values performed at the societal level throughout the relevant different sectors. In particular, the example provided here in the case of France for the year 2009 makes it possible to see that in fact nuclear energy is not viable in terms of labor requirements (both direct and indirect inputs) as well as in terms of requirements of power capacity, especially when including reprocessing operations.