Transmission tariffs in competitive electricity markets

In context of electricity market, the transmission price is an important tool to an efficient development 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 that reason evaluate tariff must have strict criterions. This paper explains several methodologies to tariff the use of transmission network by transmission network users. The methods presented are: Post-Stamp Method; MW-Mile Method; Distribution Factors Methods; Tracing Methodology; Bialek’s Tracing Method and Locational Marginal Price.

Nodal price simulation in competitive electricity markets

Locational Marginal Prices (LMP) are important pricing signals for the participants of competitive electricity markets, as the effects of transmission losses and binding constraints are embedded in LMPs [1],[2]. This paper presents a software tool that evaluates the nodal marginal prices considering losses and congestion. The initial dispatch is based on all the electricity transactions negotiated in the pool and in bilateral contracts. It must be checked if the proposed initial dispatch leads to congestion problems; if a congestion situation is detected, it must be solved. An AC power flow is used to verify if there are congestion situations in the initial dispatch. Whenever congestion situations are detected, they are solved and a feasible dispatch (re-dispatch) is obtained. After solving the congestion problems, the simulator evaluates LMP. The paper presents a case study based on the the 118 IEEE bus test network.

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.

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.

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.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

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.

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.

Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity markets

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.

Strategic bidding in electricity markets: an agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

Multi-agent Simulation of Bilateral Contracting in Competitive Electricity Markets

Traditional vertically integrated power utilities around the world have evolved from monopoly structures to open markets that promote competition among suppliers and provide consumers with a choice of services. Market forces drive the price of electricity and reduce the net cost through increased competition. Electricity can be traded in both organized markets or using forward bilateral contracts. This article focuses on bilateral contracts and describes some important features of an agent-based system for bilateral trading in competitive markets. Special attention is devoted to the negotiation process, demand response in bilateral contracting, and risk management. The article also presents a case study on forward bilateral contracting: a retailer agent and a customer agent negotiate a 24h-rate tariff. © 2014 IEEE.

Renewable energy in Islands: an integrated proposal for electricity generation and transports

The purpose of this article is to analyse and evaluate the economical, energetic and environmental impacts of the increasing penetration of renewable energies and electrical vehicles in isolated systems, such as Terceira Island in Azores and Madeira Island. Given the fact that the islands are extremely dependent on the importation of fossil fuels - not only for the production of energy, but also for the transportation’s sector – it’s intended to analyse how it is possible to reduce that dependency and determine the resultant reduction of pollutant gas emissions. Different settings have been analysed - with and without the penetration of EVs. The Terceira Island is an interesting case study, where EVs charging during off-peak hours could allow an increase in geothermal power, limited by the valley of power demand. The percentage of renewable energy in the electric power mix could reach the 74% in 2030 while at the same time, it is possible to reduce the emissions of pollutant gases in 45% and the purchase of fossil fuels in 44%. In Madeira, apart from wind, solar and small hydro power, there are not so many endogenous resources and the Island’s emission factor cannot be so reduced as in Terceira. Although, it is possible to reduce fossil fuels imports and emissions in 1.8% in 2030 when compared with a BAU scenario with a 14% of the LD fleet composed by EVs.

Renewable energy in Islands: an integrated proposal for electricity generation and transports

The purpose of this article is to analyse and evaluate the economical, energetic and environmental impacts of the increasing penetration of renewable energies and electrical vehicles in isolated systems, such as Terceira Island in Azores and Madeira Island. Given the fact that the islands are extremely dependent on the importation of fossil fuels - not only for the production of energy, but also for the transportation’s sector – it’s intended to analyse how it is possible to reduce that dependency and determine the resultant reduction of pollutant gas emissions. Different settings have been analysed - with and without the penetration of EVs. The Terceira Island is an interesting case study, where EVs charging during off-peak hours could allow an increase in geothermal power, limited by the valley of power demand. The percentage of renewable energy in the electric power mix could reach the 74% in 2030 while at the same time, it is possible to reduce the emissions of pollutant gases in 45% and the purchase of fossil fuels in 44%. In Madeira, apart from wind, solar and small hydro power, there are not so many endogenous resources and the Island’s emission factor cannot be so reduced as in Terceira. Although, it is possible to reduce fossil fuels imports and emissions in 1.8% in 2030 when compared with a BAU scenario with a 14% of the LD fleet composed by EVs.

A trader portfolio optimization of bilateral contracts in electricity retail markets

Electricity markets are systems for effecting the purchase and sale of electricity using supply and demand to set energy prices. Two major market models are often distinguished: pools and bilateral contracts. Pool prices tend to change quickly and variations are usually highly unpredictable. In this way, market participants often enter into bilateral contracts to hedge against pool price volatility. This article addresses the challenge of optimizing the portfolio of clients managed by trader agents. Typically, traders buy energy in day-ahead markets and sell it to a set of target clients, by negotiating bilateral contracts involving three-rate tariffs. Traders sell energy by considering the prices of a reference week and five different types of clients. They analyze several tariffs and determine the best share of customers, i.e., the share that maximizes profit. © 2014 IEEE.

Investment focused strategies in electricity markets using the ITEM-game simulator

This paper presents a methodology to establish investment and trading strategies of a power generation company. These strategies are integrated in the ITEM-Game simulator in order to test their results when played against defined strategies used by other players. The developed strategies are focused on investment decisions, although trading strategies are also implemented to obtain base case results. Two cases are studied considering three players with the same trading strategy. In case 1, all players also have the same investment strategy driven by a market target share. In case 2, player 1 has an improved investment strategy with a target share twice of the target of players 2 and 3. Results put in evidence the influence of the CO2 and fuel prices in the company investment decision. It is also observed the influence of the budget constraint which might prevent the player to take the desired investment decision.