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O ano de 2019 marca o aniversário de 15 anos implementação do chamado Novo Modelo do Setor Elétrico. Da mesma forma, são 15 anos desde a constituição da Câmara de Comercialização de Energia Elétrica – CCEE. A relevância do Setor Elétrico para o país é inquestionável e neste contexto a CCEE cumpre importantes atribuições para viabilizar a comercialização de energia elétrica no território nacional. Neste cenário, tendo, de um lado, um período relevante para uma análise retrospectiva e, de outro, a possibilidade de aprimoramento regulatório da CCEE, parece ser um momento propício para analisar seus marcos institucionais vigentes. O foco específico deste trabalho consiste na análise do desenho institucional da CCEE e nos mecanismos de governança à disposição da Câmara. Serão analisadas as regras que criaram e embasam a atuação da CCEE à luz das chamadas melhores práticas de governança para associações civis, a fim de verificar se existem inconsistências entre a realidade e o que seria recomendável. Ademais, será feito estudo do caso da atuação da CCEE como intermediadora da contratação de financiamentos para socorrer as distribuidoras de energia afetadas pelos efeitos da Medida Provisória nº 579/2012. O objetivo, neste ponto, é verificar se eventuais deficiências no desenho institucional da CCEE e/ou em seus mecanismos de governança pode(m) ter facilitado o alargamento de competências da Câmara pelo Governo Federal. In 2019, the so-called New Electric Sector Model. Similarly and the establishment of the Brazilian Power Trading Chamber – CCEE make 15 years. The relevance of the Electricity Sector to the country is unquestionable and in this context the CCEE fulfills important duties and roles to enable the power trading in the national territory. In this scenario, having, in one hand, a relevant timeframe for a retrospective analysis and, on the other, the possibility of suggest regulatory improvements of the CCEE design, it seems to be an appropriate moment to analyze the current institutional design and framework of the Chamber. The specific focus of this work is the analysis of the institutional design of the CCEE and the governance mechanisms available to the Chamber. The purpose of this work is to analyze the rules that created and ground CCEE's actions in the light of the so-called best governance practices for civil associations, in order to verify if there are inconsistencies between the reality and what would be advisable in accordance with such best practices. Moreover, the analysis will be followed by case study of CCEE's role as an intermediary for financing transactions, contracted to help energy distribution concessionaires that were affected in consequence of institutional change promoted by the Provisional Measure No. 579/2012. The main purpose, at this point, is to verify if any eventual deficiencies in CCEE's institutional design and/or its governance mechanisms may have enabled the arbitrary extension of the powers of the Chamber by the Federal Government.

Demand side management (DSM) is one of the most challenging areas in smart grids, which provides multiple opportunities for residents to minimize electricity cost. In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization. Furthermore, a comparative analysis is also performed to show the legitimacy and productiveness of CSA and SA.

The Indian Electricity Act 2003 defines power trading as "The Purchase of electricity for resale thereof". In this study, we review various facets of power trading, Indian electricity market, power exchanges and the day-ahead spot electricity market of India. The power exchanges are attributed with inherent features like being nationwide, ensuring anonymity, offering transparency, being automated and enabling efficient price-discovery, we believe that, power trading in India is all set to undergo a paradigm shift in terms of volume of transaction through power exchanges, price discovery, number of participants and will eventually ensure in playing a major role in addressing the biggest concern of supply-demand mismatch of electricity in India

This thesis is in the first part concerned with the electricity trading market overwiew. Power generation and transmition system is introduced as a basic fact for power trading including energy generation, energy cosumption and energy balance of certain country and whole region. Energy policy of European Union in introduced as a leading philosophy of electricity market development in next years. Existing Market coupling between Slovenia and Italy is introduced. Further projects of Market coupling between Slovenia and other neighbouring countries is included. An example of Market coupling mechanism has been described on a simulation between three random countries. Simulation includes three isolated markets on the first hand, than with congestion of ATCs. Third step is simulated as Market coupling without ATC congestions. With implementing the Market coupling mechanism, all European wellfare was described as a consequence of afford put into development of European common energy market. This thesis is in the first part concerned with the electricity trading market overwiew. Power generation and transmition system is introduced as a basic fact for power trading including energy generation, energy cosumption and energy balance of certain country and whole region. Energy policy of European Union in introduced as a leading philosophy of electricity market development in next years. Existing Market coupling between Slovenia and Italy is introduced. Further projects of Market coupling between Slovenia and other neighbouring countries is included. An example of Market coupling mechanism has been described on a simulation between three random countries. Simulation includes three isolated markets on the first hand, than with congestion of ATCs. Third step is simulated as Market coupling without ATC congestions. With implementing the Market coupling mechanism, all European wellfare was described as a consequence of afford put into development of European common energy market.

Forecasting electricity price is a challenging task for on-line trading and e-commerce. Forecasting the hourly market clearing prices (MCP) in daily power markets is the most essential task and basis for any decision making in order to maximize the benefits. Volatility in electricity price in deregulated open power markets and its forecasting using neural networks is presented. Artificial neural networks are found to be most suitable tool as they can map the complex interdependencies between electricity price, historical load, temperature and other factors. The basic idea behind 'neural network approach' is to use history and other estimated factors in the future to 'fit' and 'extrapolate'the prices and quantities. The structure of the neural network is a three-layer back propagation (BP) network. The price forecasting results using the neural network model show that the price of electricity in the deregulated markets can be forecasted with reasonable accuracy.

Infrastructural issues related to a power trading in SEE has been described.

We present a mathematical model with stochastic input data for mean-risk optimization of electricity portfolios containing several physical components and energy derivative products. The model is designed for the optimization horizon of one year in hourly discretization. The aim consists in maximizing the mean book value of the portfolio at the end of the optimization horizon and, at the same time, in minimizing the risk of the portfolio decisions. The risk is measured by the conditional value-at-risk and by some multiperiod extension of CVaR, respectively. We present numerical results for a large-scale realistic problem adapted to a municipal power utility and study the effects of varying weighting of risk.

This paper investigates the economic attractiveness of exporting coal-generated electricity from the State of Wyoming to the State of California while investing in large-scale CO 2 capture and sequestration to meet California's strict environmental regulations. The project involves partially capturing CO 2 from coal power plants, storing it temporarily underground using CO 2 Interim Storage, and ultimately using it for Enhanced Oil Recovery. A detailed financial assessment is performed in view of current electricity prices and federal and state regulations in the U.S., especially those related to CCS tax credits. The results show that the project is profitable under current regulations and technical assumptions, and it can be a good opportunity to drive large-scale deployment of CCS, along with the development of EOR activities and the deployment of the newly proposed CO 2 Interim Storage technology.

Disponible : http://www.ewec2006proceedings.info/allfiles2/966_Ewec2006fullpaper.pdf; International audience; In the framework of the Anemos project we developed a professional, flexible platform for operating wind power prediction models, laying the main focus on state-of-the-art IT techniques, inter-platform operability, availability and safety of operation. Currently, 7 plug-in prediction models from all over Europe are able to work on this platform.

International audience; Standard practice of decision-making in energy systems relies largely on complex modeling chains to address technical constraints and integrate numerous sources of uncertainty. The increased penetration of Renewable Energy Sources (RES) such as wind and photovoltaic plants adds complexity due to the weather dependency of their electricity production. Artificial Intelligence (AI) based tools have proven their efficiency in different applications in the energy sector ranging from forecasting to optimization and decision making. They permit to simplify modeling chains and to improve performance due to higher learning capabilities compared to state-of-the-art methods.However, decision-makers of the energy sector need to understand how decision-aid tools construct their outputs from the data. AI-based tools are often seen as black-box models and this penalizes their acceptability by end-users (traders, power system operators a.o.). The lack of interpretability of AI tools is a major challenge for the wider adoption of AI in the energy sector and a fundamental requirement to better support humans in the decision-aid process. Agents of energy systems expect very high levels of reliability for the various services they provide. As energy systems are impacted by multiple uncertainty sources (e.g. available power of RES plants, climate change, market conditions), developed AI tools should not only be performant on average situations but be able to guarantee robust solutions in the case of an extreme event.Therefore, our research focuses on understandable representations of data-driven decision-aid models for human operators in the energy sector. In order to enhance the interpretability of the AI models, a technique borrowed from the computer science domain is explored and further developed. Genetic programming and more precisely Symbolic Regression is used to derive a symbolic representation for the data-driven model that can take the form of a single equation. This equation results according to a specific reward function. The optimal solutions are selected naturally mimicking the biological theory of survival of the fittest.The main outcome is the production of symbolic representations of the AI models that require minimum changes when applied to different case studies. In this presentation a real-world use-case is considered, to demonstrate the added value of the proposed tools for decision making when trading the production of wind and solar power plants to the day-ahead market. An annual period of data is considered to train and test the proposed model. The typical modeling chain involves as many as 12 models for forecasting RES production and market quantities together with stochastic optimization to derive trading decisions. This complex chain is here replaced by a single AI-based model. Such simplification is a significant enhancement to the modeling chain interpretability and facilitates trust to the human decision-maker.This work is carried out in part in the frame of the European project Smart4RES supported by the H2020 Framework Program and in part in the frame the Marie-Curie COFUND project Ai4theSciences