Modelling of a Stirling engine with parabolic dish for thermal to electric conversion of solar energy

Stirling engines with parabolic dish for thermal to electric conversion of solar energy is one of the most promising solutions of renewable energy technologies in order to reduce the dependency from fossil fuels in electricity generation. This paper addresses the modelling and simulation of a solar powered Stirling engine system with parabolic dish and electric generator aiming to determine its energy production and efficiency. The model includes the solar radiation concentration system, the heat transfer in the ther- mal receiver, the thermal cycle and the mechanical and electric energy conversion. The thermodynamic and energy transfer processes in the engine are modelled in detail, including all the main processes occur- ring in the compression, expansion and regenerator spaces. Starting from a particular configuration, an optimization of the concentration factor is also carried out and the results for both the transient and steady state regimes are presented. It was found that using a directly illuminated thermal receiver with- out cavity the engine efficiency is close to 23.8% corresponding to a global efficiency of 10.4%. The com- ponents to be optimized are identified in order to increase the global efficiency of the system and the trade-off between system complexity and efficiency is discussed.

Bidding Decision of Wind-Thermal GenCo in Day-Ahead Market

This paper deals with the self-scheduling problem of a price-taker having wind and thermal power production and assisted by a cyber-physical system for supporting management decisions in a day-ahead electric energy market. The self-scheduling is regarded as a stochastic mixed-integer linear programming problem. Uncertainties on electricity price and wind power are considered through a set of scenarios. Thermal units are modelled by start-up and variable costs, furthermore constraints are considered, such as: ramp up/down and minimum up/down time limits. The stochastic mixed-integer linear programming problem allows a decision support for strategies advantaging from an effective wind and thermal mixed bidding. A case study is presented using data from the Iberian electricity market.