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This paper presents a review of the optimization techniques and strategies applied to wind turbine performance optimization. The topic is addressed by identifying the most significant objectives, targets and issues, as well as the optimization formulations, schemes and models available in the published literature. The current energy demand combined with depletion of fossil-fuel reserves and stricter environmental regulations have led to the development of alternative renewable energy solutions like wind energy. The current 2030 United States target is to have at least 20% of the US energy supply by onshore and offshore wind farms. To meet these demands, wind energy costs have to be able to compete with traditional fossil fuel sources. Hence, it is essential and vital that wind turbine designers and manufactures search the optimal solution that fits the objectives under a set of design constraints. Throughout the last 30 years, the objective function has evolved from the earlier maximized metric of the power coefficient to the maximization of the annual energy production. Common alternatives such as blade mass minimization and maximization of the rotor thrust and torque have been examined. However, the main objective has been focused on the minimization of the cost of energy in order for wind energy to become more competitive and economically attractive. The purpose of this paper is to review previous work that undertakes the performance optimization of horizontal wind turbines by highlighting the main aspects when tackling the wind turbine optimization problem such as: objective functions, design constraints, tools and models and optimization algorithms. In addition, in a conclusion of the review, a discussion and argument about the challenges, issues and future developments are identified. [ABSTRACT FROM AUTHOR]

Abstract: A strong and intermeshed electricity grid allows the cost-efficient achievement of renewable energy targets by enabling the use of favorable sites and by facilitating the balancing of stochastic infeed from renewables and electricity demand. However, construction of new lines is currently proceeding slowly in Europe. This paper quantifies the benefits of optimal transmission grid extensions for Europe up to 2050 by iterating an investment and dispatch optimization model with a load flow based grid model. We find that large grid extensions, allowing the full exploitation of the most favorable RES-E sites throughout Europe, are beneficial from a least-cost perspective. If the electricity network were to be cost-optimally extended, 228,000km would be built before 2050 (+76% compared to today). Only for sites located furthest from large consumption areas in Central Europe would the value of grid extensions not always outweigh its costs. Furthermore, the capacity of transmission lines connecting favorable RES-E sites with demand centers is cost-optimally dimensioned to almost entirely export all RES-E generation that exceeds local electricity demand. Only in periods with the highest infeed of fluctuating renewables, electricity is stored. When optimal grid extensions are impeded, storage investments are chosen to a larger extent. [Copyright &y& Elsevier]

Abstract: Forest biomass is one of the renewable sources of energy that has been used for generating electricity. The feasibility and cost of producing electricity from forest biomass depend on long term availability of biomass, its cost and quality, and the cost of collecting, pre-processing, handling, transportation, and storage of forest biomass, in addition to the operating and maintenance costs of the conversion facility. To improve the cost competitiveness of forest biomass for electricity generation, mathematical programming models can be used to manage and optimize its supply chain. In this paper, the supply chain configuration of a typical forest biomass power plant is presented and a dynamic optimization model is developed to maximize the overall value of the supply chain. The model considers biomass procurement, storage, energy production and ash management in an integrated framework at the tactical level. The developed model is a nonlinear mixed integer programming which is solved using the outer approximation algorithm provided in AIMMS software package. It is then applied to optimize the supply chain of a real biomass power plant in Canada. The optimum solution provides more profit compared to the actual profit of the power plant. Different scenarios for maximum available supply and also investment in a new ash recovery system were evaluated and the results were analyzed. The model in particular shows that investment in a new ash recovery system has economic as well as environmental benefits for the power plant. [Copyright &y& Elsevier]

Abstract: This paper aims to test the ETSAP [2] ETSAP (Energy Technology Systems Analysis Programme) is an IEA Implementation Agreement since 1976, developing the MARKAL-TIMES family of models. 2 -TIAM global energy system model and to try out how far it can go towards a global 100% renewable energy system with the existing model database. This will show where limits in global resources are met and where limits in the data fed to the model until now are met. Results from the modelling are global and regional energy consumption, emission of greenhouse gasses and thereby potential increase in global mean temperature. Furthermore, total system costs are compared to a reference scenario. Existing analysis with TIAM shows different ways of reaching the 2°C target (increase in global mean temperature) and also how uncertainty on different factors influences the optimal solution. The main conclusion from existing analysis with TIAM is that the 2°C target is possible to reach, but it will be expensive. Another very important point is that if uncertainty on the value of the climate sensitivity (Cs ) is taken into account then the optimal strategy calls for early action compared to a full foresight optimisation using the most probable value of Cs . [ABSTRACT FROM AUTHOR]