To achieve a high level of reliability, efficiency, and robustness in electric systems, the concept of smart grid has been proposed. It is an update of the traditional electric grid designed to meet current and future customers' requirements. With the smart grid, demand management has been adopted in order to shape the load pattern of the consumers, maintain supply-demand balance, and reduce the total energy cost. In this thesis, we focus mainly on energy savings by critically investigating the problem of load management in the smart grid. We first propose a user aware demand management approach that manages residential loads while taking into consideration users' comfort. This latter is modeled in a simple yet effective way that considers waiting time, type of appliance, as well as a weight factor to prioritize comfort or savings. The proposed approach is based on game theory using a modified regret matching procedure. It provides users with high incentives to participate actively in load management and borrows advantages of both centralized and decentralized schemes. Then, we investigate the issue of fairness within demand response programs. The fair division of the system bill stemming from the use of shared microgrid resources with different costs is examined. The Shapley value provides one of the core solutions to fairness problems; however, it has been known to be computationally expensive for systems such as microgrids. Therefore, we incorporate an approximation of the Shapley value into a demand response algorithm to propose a fair billing mechanism based on the contribution of each user towards attaining the aggregated system cost. Finally, we study energy trading in the smart grid as an alternative way to reduce the load on the grid by efficiently using renewable energy resources. We propose a solution that takes into account the smart grid physical infrastructure, in addition to the distribution of its users. Different constraints stemming from the nature of the smart grid have been considered towards a realistic solution. We show through simulation results that all of the proposed schemes reduce the load on the grid, the energy bills, and the total system energy cost while maintaining the users' comfort as well as fairness.

The need to cope with the rapid transformation of the conventional electrical grid into the future smart grid, with multiple connected microgrids, has led to the investigation of optimal smart grid architectures. The main components of the future smart grids such as generators, substations, controllers, smart meters and collector nodes are evolving; however, truly effective integration of these elements into the microgrid context to guarantee intelligent and dynamic functionality across the whole smart grid remains an open issue. Energy trading is a significant part of this integration. In microgrids, energy trading refers to the use of surplus energy in one microgrid to satisfy the demand of another microgrid or a group of microgrids that form a microgrid community. Different techniques are employed to manage the energy trading process such as optimization-based and conventional game-theoretical methods, which bring about several challenges including complexity, scalability and ability to learn dynamic environments. A common challenge among all of these methods is adapting to changing circumstances. Optimization methods, for example, show promising performance in static scenarios where the optimal solution is achieved for a specific snapshot of the system. However, to use such a technique in a dynamic environment, finding the optimal solutions for all the time slots is needed, which imposes a significant complexity. Challenges such as this can be best addressed using game theory techniques empowered with machine learning methods across grid infrastructure and microgrid communities. In this thesis, novel Bayesian coalitional game theory-based and Bayesian reinforcement learning-based coalition formation algorithms are proposed, which allow the microgrids to exchange energy with their coalition members while minimizing the associated cost and power loss. In addition, a deep reinforcement learning scheme is developed to address the problem of large convergence time resulting from the sizeable state-action space of the methods mentioned above. The proposed algorithms can ideally overcome the uncertainty in the system. The advantages of the proposed methods are highlighted by comparing them with the conventional coalitional game theory-based techniques, Q-learning-based technique, random coalition formation, as well as with the case with no coalitions. The results show the superiority of the proposed methods in terms of power loss and cost minimization in dynamic environments.

A expansão da oferta energética frente à crescente demanda se mostra como um grande desafio para o país nas últimas décadas, tanto que para estimular os investimentos privados na área de geração de energia elétrica, o poder executivo, em 2003, iniciou as discussões de forma a apresentar seus objetivos e os pré-requisitos para alcançá-los e que mais tarde viria a se transformar na reforma do modelo institucional, introduzida no ano de 2004. Um dos marcos da reforma promovida são os leilões de compra de energia elétrica, tanto de geração existente, quanto de empreendimentos novos, sendo que a modalidades de contratação da energia pode ser por quantidade e por disponibilidade. Na modalidade de contratação por quantidade, o gerador vender um determinado montante de energia a um preço e deverá entregá-la ao comprador independentemente do seu montante de geração verificada em cada período. Na modalidade de contratação de energia por disponibilidade, o empreendedor é remunerado pelo investimento incorrido para construção e manutenção da usina, sendo ressarcido pelos compradores do contrato no custo de operação da usina incorrido, em caso de despacho da geração pelo operador do sistema elétrico. A contratação por disponibilidade é aplicada aos empreendimentos termelétricos e eólicos, cujos combustíveis podem ser desde os ventos e resíduos agrícolas de baixo custo, até óleo combustível derivado de petróleo com um alto custo e suscetível às oscilações de preço no mercado internacional. Esse custo assumido pelo comprador é um dos principais pontos de discussão desse trabalho, uma vez que por ocasião do leilão esse custo é ainda uma mera estimativa intrinsecamente relacionada com a operação eletro-energética do sistema elétrico brasileiro. Além disso, será introduzida a discussão sobre o impacto do custo da energia contratada por disponibilidade na tarifa dos consumidores, como resultado do próprio leilão associado à estratégia de cada distribuidora para contratação de seu mercado. / Electric energy supply of the fast growing demand in Brazil has been a challenge for the country in the last fifteen years. In order to stimulate power generation investments, a new institutional power sector model was introduced in 2004. This model introduced the obligation of the energy requirements acquisition by Distribution Utilities (DISCO) only through energy auctions from old and new power plants. New power plants energy auctions can be performed in two different ways: energy and capacity contracts. In the energy contracts, the energy amount is guaranteed by the seller, frequently in the case of hydro power plants. On the other hand, in the capacity arrangements the seller requires a fee, which results from the bidding process, as well as a charge defined to recover maintenance and fuel costs of thermal power plants, if the plant were requested to dispatch. Thermal power plants are dispatched by the Independent System Operator (ISO) using hydrothermal system optimization model. However, fuel costs are paid by DISCOs and will be passed-through to the end customers tariffs. The main focus of this work, is the economical and financial risks of capacity arrangement and the impact in the captive customers tariffs.

A expansão da oferta energética frente à crescente demanda se mostra como um grande desafio para o país nas últimas décadas, tanto que para estimular os investimentos privados na área de geração de energia elétrica, o poder executivo, em 2003, iniciou as discussões de forma a apresentar seus objetivos e os pré-requisitos para alcançá-los e que mais tarde viria a se transformar na reforma do modelo institucional, introduzida no ano de 2004. Um dos marcos da reforma promovida são os leilões de compra de energia elétrica, tanto de geração existente, quanto de empreendimentos novos, sendo que a modalidades de contratação da energia pode ser por quantidade e por disponibilidade. Na modalidade de contratação por quantidade, o gerador vender um determinado montante de energia a um preço e deverá entregá-la ao comprador independentemente do seu montante de geração verificada em cada período. Na modalidade de contratação de energia por disponibilidade, o empreendedor é remunerado pelo investimento incorrido para construção e manutenção da usina, sendo ressarcido pelos compradores do contrato no custo de operação da usina incorrido, em caso de despacho da geração pelo operador do sistema elétrico. A contratação por disponibilidade é aplicada aos empreendimentos termelétricos e eólicos, cujos combustíveis podem ser desde os ventos e resíduos agrícolas de baixo custo, até óleo combustível derivado de petróleo com um alto custo e suscetível às oscilações de preço no mercado internacional. Esse custo assumido pelo comprador é um dos principais pontos de discussão desse trabalho, uma vez que por ocasião do leilão esse custo é ainda uma mera estimativa intrinsecamente relacionada com a operação eletro-energética do sistema elétrico brasileiro. Além disso, será introduzida a discussão sobre o impacto do custo da energia contratada por disponibilidade na tarifa dos consumidores, como resultado do próprio leilão associado à estratégia de cada distribuidora para contratação de seu mercado. / Electric energy supply of the fast growing demand in Brazil has been a challenge for the country in the last fifteen years. In order to stimulate power generation investments, a new institutional power sector model was introduced in 2004. This model introduced the obligation of the energy requirements acquisition by Distribution Utilities (DISCO) only through energy auctions from old and new power plants. New power plants energy auctions can be performed in two different ways: energy and capacity contracts. In the energy contracts, the energy amount is guaranteed by the seller, frequently in the case of hydro power plants. On the other hand, in the capacity arrangements the seller requires a fee, which results from the bidding process, as well as a charge defined to recover maintenance and fuel costs of thermal power plants, if the plant were requested to dispatch. Thermal power plants are dispatched by the Independent System Operator (ISO) using hydrothermal system optimization model. However, fuel costs are paid by DISCOs and will be passed-through to the end customers tariffs. The main focus of this work, is the economical and financial risks of capacity arrangement and the impact in the captive customers tariffs.

Orientadores: Christiano Lyra Filho, Leontina Maria Viana Graziadio Pinto / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-17T15:14:17Z (GMT). No. of bitstreams: 1 Fernandez_MarceloRichter_M.pdf: 1222990 bytes, checksum: 048fede24ff2fe68c77f818304e01abd (MD5) Previous issue date: 2010 / Resumo: Este trabalho propõe o desenvolvimento de metodologia para a operação de um portfólio de contratos de compra e venda de energia elétrica no ambiente de contratação livre utilizando conceitos de otimização, em especial de programação linear, matemática financeira e estatística, além de considerar também as regras de comercialização de energia do setor elétrico brasileiro. Isso permite que comercializadores maximizem seus resultados financeiros pela alocação ótima de seus contratos de compra e venda de energia. Além disso, permite a simulação de cenários alternativos de consumo, variações de preços de curto prazo e opções de contrato. A partir dessas informações, os agentes de comercialização de energia elétrica podem realizar comparações entre as várias alternativas de operações no mercado de energia; podem também comparar essas ações com outras propostas de investimentos. Estudos de casos ilustram a aplicação da metodologia a cenários reais do sistema elétrico brasileiro. A análise desses resultados permite avaliar benefícios alcançados e sugere possibilidades de desdobramentos / Abstract: This work proposes the development of a new approach for the operation of electric energy contracts in the Brazilian free market. It uses concepts of linear optimization, financial mathematics, statistics and considers the Brazilian free market regulation. The methodology allows traders to maximize their profits by using the optimal allocation for each energy contract. It also allows traders to simulate trading with different scenarios of energy consumption by the customers, spot market prices, future market prices and long term contracts. Results allow investors to compare energy trading options with other alternatives for investments. Case studies illustrate the application of the proposed methodology to real world scenarios; benefits are also evaluated / Mestrado / Mestre em Engenharia Elétrica

The main focus of this thesis lies on description of Risk Management in context of Energy Trading. The paper will predominantly discuss Value at Risk and its modifications as a main overall indicator of Energy Risk.

abstract: Blockchain technology enables peer-to-peer transactions through the elimination of the need for a centralized entity governing consensus. Rather than having a centralized database, the data is distributed across multiple computers which enables crash fault tolerance as well as makes the system difficult to tamper with due to a distributed consensus algorithm. In this research, the potential of blockchain technology to manage energy transactions is examined. The energy production landscape is being reshaped by distributed energy resources (DERs): photo-voltaic panels, electric vehicles, smart appliances, and battery storage. Distributed energy sources such as microgrids, household solar installations, community solar installations, and plug-in hybrid vehicles enable energy consumers to act as providers of energy themselves, hence acting as 'prosumers' of energy. Blockchain Technology facilitates managing the transactions between involved prosumers using 'Smart Contracts' by tokenizing energy into assets. Better utilization of grid assets lowers costs and also presents the opportunity to buy energy at a reasonable price while staying connected with the utility company. This technology acts as a backbone for 2 models applicable to transactional energy marketplace viz. 'Real-Time Energy Marketplace' and 'Energy Futures'. In the first model, the prosumers are given a choice to bid for a price for energy within a stipulated period of time, while the Utility Company acts as an operating entity. In the second model, the marketplace is more liberal, where the utility company is not involved as an operator. The Utility company facilitates infrastructure and manages accounts for all users, but does not endorse or govern transactions related to energy bidding. These smart contracts are not time bounded and can be suspended by the utility during periods of network instability. / Dissertation/Thesis / Masters Thesis Computer Science 2019

Peer-to-Peer energy trading enables the prosumers and consumers to trade their energy in a simple services.By this the energy users have possibility to have a surplusshare of energy without any interruptions[1].But for the higher deployment of thep2p energy services, the allocation of the resources for the energy trading transactions are also challenging to model in these days. Blockchain technology, which isof a distributed ledger system and also provides a secure way of sharing the information between the peers of the network, is suitable for the proposed p2p energytrading model which can be useful for the higher scale deployments. This thesis provides an initial implementation of the p2p energy trading modelusing the blockchain and also measures the performance of the implemented modelwith the computational.A literature review is conducted for obtaining the previousstudies related to p2p energy trading using blockchain with the performance evaluation.Then the technologies related to the thesis are described and from the literaturestudies the required models are described and considered for proposing the systemmodel for the thesis. The implemented system model is also analyzed with different computational efforts for the service management functions. For generating the transactions, a Fabricclient SDK is created, which ensures that each transaction communicates with theblockchain’s smart contract for the secured transaction. Finally, after measuring thecomputational efforts, I want to observe the performance outcome for the measuredcomputational parameters so that the system’s behavior can be analyzed when thetransactions are happening between the peers by using the specific blockchain technology.

Blockchain has become the most revolutionary technology in the 21st century. In recent years, one of the concerns of world energy isn't just sustainability yet, in addition, being secure and reliable also. Since information and energy security are the main concern for the present and future services, this thesis is focused on the challenge of how to trade energy securely on the background of using distributed marketplaces that can be applied. The core technology used in this thesis is distributed ledger, specifically blockchain. Since this technology has recently gained much attention because of its functionalities such as transparency, immutability, irreversibility, security, etc, we tried to convey a solution for the implementation of a secure peer-to-peer (P2P) energy trading network over a suitable blockchain platform. Furthermore, blockchain enables traceability of the origin of data which is called data provenience. In this work, we applied a secure blockchain technology in peer-to-peer energy sharing or trading system where the prosumer and consumer can trade their energies through a secure channel or network. Furthermore, the service management functionalities such as security, reliability, flexibility, and scalability are achieved through the implementation. \\ This thesis is focused on the current proposals for p2p energy trading using blockchain and how to select a suitable blockchain technique to implement such a p2p energy trading network. In addition, we provide an implementation of such a secure network under blockchain and proper management functions. The choices of the system models, blockchain technology, and the consensus algorithm are based on literature review, and it carried to an experimental implementation where the feasibility of that system model has been validated through the output results.

Yes / This paper used different risk management indicators applied to the investment optimization performed by consumers in Distributed Generation (DG). The objective function is the total cost incurred by the consumer including the energy and capacity payments, the savings, and the revenues from the installation of DG, alongside the operation and maintenance (O&M) and investment costs. Probability density function (PDF) was used to model the price volatility in the long-term. The mathematical model uses a two-stage stochastic approach: investment and operational stages. The investment decisions are included in the first stage and which do not change with the scenarios of the uncertainty. The operation variables are in the second stage and, therefore, take different values with every realization. Three risk indicators were used to assess the uncertainty risk: Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and Expected Value (EV). The results showed the importance of migration from deterministic models to stochastic ones and, most importantly, the understanding of the ramifications of every risk indicator.