2,704 research outputs found

    Assessing financial and flexibility incentives for integrating wind energy in the grid via agent-based modeling

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    This article provides an agent-based model of a hypothetical standalone electricity network to identify how the feed-in tariffs and the installed capacity of wind power, calculated in percentage of total system demand, affect the electricity consumption from renewables. It includes the mechanism of electricity pricing on the Day Ahead Market (DAM) and the Imbalance Market (IM). The extra production volumes of Electricity from Renewable Energy Sources (RES-E) and the flexibility of electrical consumption of industries is provided as reserves on the IM. Five thousand simulations were run by using the agent-based model to gather data that were then fit in linear regression models. This helped to quantify the effect of feed-in tariffs and installed capacity of wind power on the consumption from renewable energy and market prices. The consumption from renewable sources, expressed as percentage of total system consumption, increased by 8.17% for every 10% increase in installed capacity of wind power. The sharpest increase in renewable energy consumption is observed when a feed-in tariff of 0.04 €/kWh is provided to the wind farm owners, resulting in an average increase of 9.1% and 5.1% in the consumption from renewable sources while the maximum installed capacity of wind power is 35% and 100%, respectively. The regression model for the annualized DAM prices showed an increase by 0.01 €cents/kWh in the DAM prices for every 10% increase in the installed wind power capacity. With every increase of 0.01 €/kWh in the value of feed-in tariffs, the mean DAM price is lowered as compared to the previous value of the feed-in tariff. DAM prices only decrease with increasing installed wind capacity when a feed-in tariff of 0.04 €/kWh is provided. This is observed because all wind power being traded on DAM at a very cheap price. Hence, no volume of electricity is being stored for availability on IM. The regression models for predicting IM prices show that, with every 10% increase in installed capacity of wind power, the annualized IM price decreases by 0.031 and 0.34 €cents/kWh, when installed capacity of wind power is between 0 and 25%, and between 25 and 100%, respectively. The models also showed that, until the maximum installed capacity of wind power is less than 25%, the IM prices increase when the value of feed-in tariff is 0.01 and 0.04 €/kWh, but decrease for a feed-in tariff of 0.02 and 0.03 €/kWh. When installed capacity of wind power is between 25 and 100%, increasing feed-in tariffs to the value of 0.03 €/kWh result in lowering the mean IM price. However, at 0.04 €/kWh, the mean IM price is higher, showing the effect of no storage reserves being available on IM and more expensive reserves being engaged on the IM. The study concludes that the effect of increasing installed capacity of wind power is more significant on increasing consumption of renewable energy and decreasing the DAM and IM prices than the effect of feed-in tariffs. However, the effect of increasing values of both factors on the profit of RES-E producers with storage facilities is not positive, pointing to the need for customized rules and incentives to encourage their market participation and investment in storage facilities

    Agent-based simulation for renewable energy incentive design

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    In this thesis, we propose a novel approach to model the diffusion of residential PV systems. For this purpose, we use an agent-based model where agents are the families living in the area of interest. The case study is the Emilia-Romagna Regional Energy plan, which aims to increase the produc- tion of electricity from renewable energy. So, we study the microdata from the Survey on Household Income and Wealth (SHIW) provided by Bank of Italy in order to obtain the characteristics of families living in Emilia-Romagna. These data have allowed us to artificial generate families and reproduce the socio-economic aspects of the region. The families generated by means of a software are placed on the virtual world by associating them with the buildings. These buildings are acquired by analysing the vector data of regional buildings made available by the region. Each year, the model determines the level of diffusion by simulating the installed capacity. The adoption behaviour is influenced by social interactions, household’s economic situation, the environmental benefits arising from the adoption and the payback period of the investment

    Exploring Regulation Policies in Distribution Networks through a Multi-Agent Simulator

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    This paper presents a multi-agent simulator that describes the interactions between the agents of a distribution network (DN), and an environment. The agents are the users of the DN and the electricity distribution system operator. The environment is the set of rules (tariff design, technology costs, or incentive schemes) that impacts the agents interactions. For a given environment, we can simulate the evolution of the agents and the environment itself. We assume the electricity consumers are rational agents that may deploy distributed renewable energy installations if they are cost-efficient compared to the retail electricity tariff. The deployment of such installations may alter the cost recovery scheme of the distribution system operator, by inducing a change in the way the user use of the grid. By modelling the cost recovery mechanism of the distribution system operator, the system simulates the evolution of the retail electricity tariff in response to such a change in the aggregated consumption and production.Peer reviewe

    Analysis of A Next Generation Energy System Based on the Integration of Transportation Subsystem Details

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    As the economy continues to grow, the current energy system will need to meet the increasing demand, especially in the developing countries. The depletion of fossil fuels, the surge in energy use, and the growing threat of climate change require rapid development of next-generation energy system. Renewable energy, such as wind, solar, and biomass, will undoubtedly play an important role, as a result of improved technology and enhanced capability in energy storage. For example, the closer integration of transportation to the energy system through vehicle electrification will have an increasing effect on the trajectory of the energy system. In order to gain a deeper understanding of the future energy system, anticipate potential problems during the evolution, and provide constructive suggestions for policy makers, a systematic analysis of the next generation energy system is highly desirable

    Over the Precipice: Transitional Pressures from Household PV Battery Adoption on Electricity Markets and the Potential for Decarbonisation

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    The electricity system is undergoing simultaneous change from the integration of large-scale wind and solar farms to the rapid growth of rooftop solar in over 3 million Australian households. This research establishes a range of future impacts that households with solar PV and battery systems could have on the wider power sector, the extent to which policymakers may influence their outcome, and their potential contribution to decarbonisation as an emerging source of renewable energy

    Factors for Measuring Photovoltaic Adoption from the Perspective of Operators

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    The diffusion of photovoltaic distributed generation is relevant for addressing the political, economic, and environmental issues in the electricity sector. However, the proliferation of distributed generation brings new administrative and operational challenges for the sustainability of electric power utilities. Electricity distributors operate in economies of scale, and the high photovoltaic penetration means that these companies have economic and financial impacts, in addition to influencing the migration of other consumers. Thus, this paper aims to systematically identify and evaluate critical factors and indicators that may influence electricity distributors in predicting their consumers’ adoption of photovoltaic technology, which were subjected to the analysis of 20 industry experts. Results show that the cost of electricity, generation capacity, and cost of the photovoltaic systems are the most relevant indicators, and it is possible to measure a considerable part of them using the internal data of the electricity distributors. The study contributes to the understanding of the critical factors for the forecast of the adoption of consumers to distributed photovoltaic generation, to assist the distribution network operators in the decision making, and the distribution sustainability. Also, it establishes the theoretical, political, and practical implications for the Brazilian scenario and developing countries.This research was funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico [grant numbers 311926/2017-7, 142448/2018-4, 310594/2017-0 and 465640/2014-1], Coordenação de Aperfeiçoamento de Pessoal de Nível Superior [grant numbers 1773252/2018, 1845395/2019 and 23038.000776/2017–54] and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul [grant number 17/2551-0000517-1].info:eu-repo/semantics/publishedVersio

    Exploring market designs for local energy markets : core functionalities and value proposition in the context of blockchain, IoT and prosumers

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    This dissertation aimed to assess the impact of innovative smart market solutions and Blockchain technology on achieving efficient localized energy markets. Trends suggest the future of renewable energy generation will involve a move away from centralized power plants, and towards a large number of smaller generation units, such as PV cells. There are clear synergies between the market dynamics of photovoltaic systems and Blockchain-enabled smart markets, which can be harnessed towards integrating new consumption patterns and energy sources, as well as connecting consumers. Successful business strategy to integrate these technologies can lead to market leadership in this new industry. Captivating consumers is a key determinant of success, and offering lower electricity prices a necessary condition. For such offering to be feasible, markets need to be more efficient, as smart microgrids are proving to be. Consequently, there came the interest to see how new local electricity markets could be set up, while taking advantage of decentralization. A peer-to-peer, auction-based, local energy market was idealized and various simulations of were ran with differing levels of participants and structure, to understand the impact on the price of electricity achieved by the market. Market size and structure were both shown to affect price at different magnitudes, suggesting an ideal setup of 25-40 participants with generation capabilities over 60% of demand. Further analysis was undertaken to understand the impact of smart meters and Blockchain integration in such a market. Afterwards, conclusions were compiled and recommendations provided for how to approach new practical implementations.Esta dissertação teve como objetivo avaliar o impacto de inovadoras soluções de mercados inteligentes e tecnologia Blockchain em mercados locais de energia. Tendencias apontam para que o futuro das energias renovaveis passe por uma maior prevalencia de paineis fotovoltaicos domesticos. As sinergias entre as atuais dinamicas em mercados eletricos e o uso da Blockchain em mercados inteligentes parecem claras, podendo ser aproveitaveis para integrar novos perfis de consumo e conectar consumidores. Sendo um novo segmento, estratégias de mercado bem conseguidas serão essencias para ganhar posição, e a capacidade de angariar consumidores será um indicador crucial de sucesso. Para tal, os mercados têm que ser mais eficientes, algo que se tem revelado factual em casos de micro sistemas. Assim, criou-se o interesse de perceber como desenhar e implementar mercados localizados de energia que beneficiem desta tendencia de desintermediação. Para tal, um mercado interativo à base de leilões de eletricidade entre consumidores foi idealizado. Posteriormente, este foi simulado repetidamente, com diferentes dimensões e estruturas, a fim de perceber o seu impacto nos preços médios alcançados. Foi mostrado que tamanho e composição afetam os preços em magnitudes diferentes, sugerindo uma dimensão ideal de 25-40 participantes, com capacidades de autogeração superiores a 60%. Análises posteriors foram desenvolvidas de modo substantive, para avaliar o impacto de contadores eletricos inteligentes e integração da Blockchain neste tipo de mercado. Finalmente, conclusões foram reunidas e transformadas em recomendações para futuras implementações práticas
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