Modelado de Sistemas Energéticos

Planificación de Operaciones de Sistemas

Planificación Integrada de Recursos

Calendario Operativo a Corto Plazo

Modelado de Recursos Renovables

Modelización Avanzada de Energías Renovables

Potencial Hidroeléctrico y Evaluaciones Ambientales

Herramientas de Soporte Financiero

Gestión de Portafolio de Energía

Herramientas Integradas y Entornos Computacionales

Sistema Computacional de Alto Rendimiento

A risk-constrained Energy Reallocation Mechanism for renewable sources with a Marginal Benefit approach

A risk-constrained Energy Reallocation Mechanism for renewable sources with a Marginal Benefit approach

Electric Power Systems Research, 2018

Because electricity generation from renewable energy is seasonal and intermittent, the risk associated with its cash flow volatility may discourage investments in this area. In this paper, a method called Energy Reallocation Mechanism is proposed to mitigate this problem. The idea, inspired by the current mechanism used in Brazil for Hydropower plants, consists of the creation of a renewable energy portfolio and the allocation of the quotas of this portfolio to each source. Because the portfolio cash flow is more stable than the individual cash flows, the financial risk of being in the portfolio is reduced. The proposed mechanism is based on a stochastic approach and it combines future scenarios of generation in order to produce future scenarios of cash flow. Beyond the Expected Value applied in the Brazilian mechanism, it also considers the Conditional Value at Risk to account for average and risk, respectively, associated with the generation profile of each source. Finally, based on the aforementioned, the Marginal Benefit method is applied to allocate the portfolio’s quotas for the renewable sources guaranteeing that the solution provided is suitable under the cooperative game theory viewpoint. For didactic reasons a small renewable energy portfolio, formed by Wind Power, Small Hydro and Biomass, is used for comparing the proposed mechanism with the Shapley Value method and the current method applied in Brazil. The applicability for larger systems is demonstrated with real data from the Brazilian Power System.

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