A long-term swarm intelligence hedging tool applied to electricity markets

This paper proposes a swarm intelligence long-term hedging tool to support electricity producers in competitive electricity markets. This tool investigates the long-term hedging opportunities available to electric power producers through the use of contracts with physical (spot and forward) and financial (options) settlement. To find the optimal portfolio the producer risk preference is stated by a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance estimation and the expected return are based on a forecasted scenario interval determined by a long-term price range forecast model, developed by the authors, whose explanation is outside the scope of this paper. The proposed tool makes use of Particle Swarm Optimization (PSO) and its performance has been evaluated by comparing it with a Genetic Algorithm (GA) based approach. To validate the risk management tool a case study, using real price historical data for mainland Spanish market, is presented to demonstrate the effectiveness of the proposed methodology.

Electricity markets: transmission prices methods

In the context of electricity markets, transmission pricing is an important tool to achieve an efficient operation of the electricity system. The electricity market is influenced by several factors; however the transmission network management is one of the most important aspects, because the network is a natural monopoly. The transmission tariffs can help to regulate the market, for this reason transmission tariffs must follow strict criteria. This paper presents the following methods to tariff the use of transmission networks by electricity market players: Post-Stamp Method; MW-Mile Method Distribution Factors Methods; Tracing Methodology; Bialek’s Tracing Method and Locational Marginal Price. A nine bus transmission network is used to illustrate the application of the tariff methods.

Auction of financial transmission rights in electricity market environment

An auction model is used to increase the individual profits for market players with products they do not use. A Financial Transmission Rights Auction has the goal of trade transmission rights between Bidders and helps them raise their own profits. The ISO plays a major rule on keep the system in technical limits without interfere on the auctions offers. In some auction models the ISO decide want bids are implemented on the network, always with the objective maximize the individual profits for all bidders in the auction. This paper proposes a methodology for a Financial Transmission Rights Auction and an informatics application. The application receives offers from the purchase and sale side and considers bilateral contracts as Base Case. This goal is maximize the individual profits within the system in their technical limits. The paper includes a case study for the 30 bus IEEE test case.

ANN based day-ahead ancillary services forecast for electricity market simulation

Adequate decision support tools are required by electricity market players operating in a liberalized environment, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services (AS) represent a good negotiation opportunity that must be considered by market players. Based on the ancillary services forecasting, market participants can use strategic bidding for day-ahead ancillary services markets. For this reason, ancillary services market simulation is being included in MASCEM, a multi-agent based electricity market simulator that can be used by market players to test and enhance their bidding strategies. The paper presents the methodology used to undertake ancillary services forecasting, based on an Artificial Neural Network (ANN) approach. ANNs are used to day-ahead prediction of non-spinning reserve (NS), regulation-up (RU), and regulation down (RD). Spinning reserve (SR) is mentioned as past work for comparative analysis. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

An integrated system to support electricity tariff contract definition

This paper presents an integrated system that helps both retail companies and electricity consumers on the definition of the best retail contracts and tariffs. This integrated system is composed by a Decision Support System (DSS) based on a Consumer Characterization Framework (CCF). The CCF is based on data mining techniques, applied to obtain useful knowledge about electricity consumers from large amounts of consumption data. This knowledge is acquired following an innovative and systematic approach able to identify different consumers’ classes, represented by a load profile, and its characterization using decision trees. The framework generates inputs to use in the knowledge base and in the database of the DSS. The rule sets derived from the decision trees are integrated in the knowledge base of the DSS. The load profiles together with the information about contracts and electricity prices form the database of the DSS. This DSS is able to perform the classification of different consumers, present its load profile and test different electricity tariffs and contracts. The final outputs of the DSS are a comparative economic analysis between different contracts and advice about the most economic contract to each consumer class. The presentation of the DSS is completed with an application example using a real data base of consumers from the Portuguese distribution company.

A long-term risk management tool for electricity markets using swarm intelligence

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.

Impact of Sahara dust transport on Cape Verde atmospheric element particles

The objectives of this study were to (1) conduct an elemental characterization of airborne particles sampled in Cape Verde and (2) assess the influence of Sahara desert on local suspended particles. Particulate matter (PM10) was collected in Praia city (14°94'N; 23°49'W) with a low-volume sampler in order to characterize its chemical composition by k0-INAA. The filter samples were first weighed and subsequently irradiated at the Portuguese Research Reactor. Results showed that PM10 concentrations in Cape Verde markedly exceeded the health-based air quality standards defined by the European Union (EU), World Health Organization (WHO), and U.S. Environmental Protection Agency (EPA), in part due to the influence of Sahara dust transport. The PM10 composition was characterized essentially by high concentrations of elements originating from the soil (K, Sm, Co, Fe, Sc, Rb, Cr, Ce, and Ba) and sea (Na), and low concentrations of anthropogenic elements (As, Zn, and Sb). In addition, the high concentrations of PM measured in Cape Verde suggest that health of the population may be less affected compared with other sites where PM10 concentrations are lower but more enriched with toxic elements.

Commercial agents portfolio optimization in electricity markets

As it is well known, competitive electricity markets require new computing tools for power companies that operate in retail markets in order to enhance the management of its energy resources. During the last years there has been an increase of the renewable penetration into the micro-generation which begins to co-exist with the other existing power generation, giving rise to a new type of consumers. This paper develops a methodology to be applied to the management of the all the aggregators. The aggregator establishes bilateral contracts with its clients where the energy purchased and selling conditions are negotiated not only in terms of prices but also for other conditions that allow more flexibility in the way generation and consumption is addressed. The aggregator agent needs a tool to support the decision making in order to compose and select its customers' portfolio in an optimal way, for a given level of profitability and risk.

In vitro exposure of acer negundo pollen to atmospheric levels of SO2 and NO2: effects on allergenicity and germination

In the last years, a rising trend of pollen allergies in urban areas has been attributed to atmospheric pollution. In this work, we investigated the effects of SO2 and NO2 on the protein content, allergenicity, and germination rate of Acer negundo pollen. A novel environmental chamber was assembled to exposure pollen samples with SO2 or NO2 at two different levels: just below and two times the atmospheric hour-limit value acceptable for human health protection in Europe. Results showed that protein content was lower in SO2- exposed pollen samples and slightly higher in NO2-exposed pollen compared to the control sample. No different polypeptide profiles were revealed by SDSPAGE between exposed and nonexposed pollen, but the immunodetection assays indicated higher IgE recognition by all sera of sensitized patients to Acer negundo pollen extracts in all exposed samples in comparison to the nonexposed samples. A decrease in the germination rate of exposed in contrast to nonexposed pollen was verified, which was more pronounced for NO2-exposed samples. Our results indicated that in urban areas, concentrations of SO2 and NO2 below the limits established for human protection can indirectly aggravate pollen allergy on predisposed individuals and affect plant reproduction.

Avaliação energética de uma escola

Mestrado em Engenharia Química

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Analysis of polycyclic aromatic hydrocarbons in atmospheric particulate samples by microwaveassisted extraction and liquid chromatography

A methodology based on microwave-assisted extraction (MAE) and LC with fluorescence detection (FLD) was investigated for the efficient determination of 15 polycyclic aromatic hydrocarbons (PAHs) regarded as priority pollutants by the US Environmental Protection Agency and dibenzo(a,l)pyrene in atmospheric particulate samples. PAHs were successfully extracted from real outdoor particulate matter (PM) samples with recoveries ranging from 81.4±8.8 to 112.0±1.1%, for all the compounds except for naphthalene (62.3±18.0%) and anthracene (67.3±5.7%), under the optimum MAE conditions (30.0 mL of ACN for 20 min at 110ºC). No clean-up steps were necessary prior to LC analysis. LOQs ranging from 0.0054 ng/m3 for benzo( a)anthracene to 0.089 ng/m3 for naphthalene were reached. The validated MAE methodology was applied to the determination of PAHs from a set of real world PM samples collected in Oporto (north of Portugal). The sum of particulate-bound PAHs in outdoor PM ranged from 2.5 and 28 ng/m3.

Six thinking hats for electricity markets simulation: adapting meeting tools to agent decision

In almost all industrialized countries, the energy sector has suffered a severe restructuring that originated a greater complexity in market players’ interactions. The complexity that these changes brought made way for the creation of decision support tools that facilitate the study and understanding of these markets. MASCEM – “Multiagent Simulator for Competitive Electricity Markets” arose in this context providing a framework for evaluating new rules, new behaviour, and new participants in deregulated electricity markets. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. ALBidS is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This tool’s goal is to force the thinker to move outside his habitual thinking style. It was developed to be used mainly at meetings in order to “run better meetings, make faster decisions”. This dissertation presents a study about the applicability of the Six Thinking Hats technique in Decision Support Systems, particularly with the multiagent paradigm like the MASCEM simulator. As such this work’s proposal is of a new agent, a meta-learner based on STH technique that organizes several different ALBidS’ strategies and combines the distinct answers into a single one that, expectedly, out-performs any of them.

Evaluation of atmospheric deposition and patterns of polycyclic aromatic hydrocarbons in façades of historic monuments of Oporto (Portugal)

Atmospheric pollution by motor vehicles is considered a relevant source of damage to architectural heritage. Thus the aim of this work was to assess the atmospheric depositions and patterns of polycyclic aromatic hydrocarbons (PAHs) in façades of historical monuments. Eighteen PAHs (16 PAHs considered by US EPA as priority pollutants, dibenzo[a,l]pyrene and benzo[j]fluoranthene) were determined in thin black layers collected from façades of two historical monuments: Hospital Santo António and Lapa Church (Oporto, Portugal). Scanning electron microscopy (SEM) was used for morphological and elemental characterisation of thin black layers; PAHs were quantified by microwave-assisted extraction combined with liquid chromatography (MAE-LC). The thickness of thin black layers were 80–110 μm and they contained significant levels of iron, sulfur, calcium and phosphorus. Total concentrations of 18 PAHs ranged from 7.74 to 147.92 ng/g (mean of 45.52 ng/g) in thin black layers of Hospital Santo António, giving a range three times lower than at Lapa Church (5.44– 429.26 ng/g; mean of 110.25 ng/g); four to six rings compounds accounted at both monuments approximately for 80–85% of ΣPAHs. The diagnostic ratios showed that traffic emissions were significant source of PAHs in thin black layers. Composition profiles of PAHs in thin black layers of both monuments were similar to those of ambient air, thus showing that air pollution has a significant impact on the conditions and stone decay of historical building façades. The obtained results confirm that historical monuments in urban areas act as passive repositories for air pollutants present in the surrounding atmosphere.

Stochastic short-term maintenance scheduling of GENCOs in an oligopolistic electricity market

In the proposed model, the independent system operator (ISO) provides the opportunity for maintenance outage rescheduling of generating units before each short-term (ST) time interval. Long-term (LT) scheduling for 1 or 2 years in advance is essential for the ISO and the generation companies (GENCOs) to decide their LT strategies; however, it is not possible to be exactly followed and requires slight adjustments. The Cournot-Nash equilibrium is used to characterize the decision-making procedure of an individual GENCO for ST intervals considering the effective coordination with LT plans. Random inputs, such as parameters of the demand function of loads, hourly demand during the following ST time interval and the expected generation pattern of the rivals, are included as scenarios in the stochastic mixed integer program defined to model the payoff-maximizing objective of a GENCO. Scenario reduction algorithms are used to deal with the computational burden. Two reliability test systems were chosen to illustrate the effectiveness of the proposed model for the ST decision-making process for future planned outages from the point of view of a GENCO.