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The key to this study is to explore the relation between renewable energy and trade openness under climate change impacts on international trade. This study is based on the analyses of trade openness index and the Notre Dame-Global Adaptation Index in measures of research of the transfer of renewable energy technologies globally, promotion of the growth of renewable energy share, and the country's current vulnerability to climate disruptions. Research is focused on evaluation of low-carbon trade, analysis of renewable heat, and estimation of the impact of renewable energy on trade openness under climate disruptions. Research verified that development of renewable energy can reduce the human impact on climate change, but such technologies are not available to all countries; because of this, different countries may be affected by climate change to varying degrees. The implementation of renewable energy technologies should help countries become more resilient to climate change through the availability of appropriate means of producing renewable energy; however, this is possible subject to the country's openness to the arrival of goods and technologies from outside-increasing trade openness level. Countries with open and competitive markets are more inclusive to adopt and support the renewable energy technologies and their development. From another position, development of trade and deep integration of economy can create external dependence on imported energy sources and block the development of domestic renewable energy sectors. The economic challenges in the field of renewable energy need the focus of governments and policy makers on supporting the stability of renewable generation facilities, increasing the reliability of energy supply, protection from seasonal changes in demand for electricity, support of the energy cooperatives and private consumers of renewable energy, and transition the public transport sector for renewable energy sources. Further research in this direction has prospects, because globalization is a stable trend of recent decades, and allows for an even distribution of efforts to produce various products and provide them to the population, but decarbonization potential of renewable energy production is untapped as fossil fuels dominate in largest world markets.

With California's ambitious goal to achieve decarbonization of the electrical grid by the year 2045, significant challenges arise in power system investment planning. Existing modeling methods and software focus on computational efficiency, which is currently achieved by simplifying the associated unit commitment formulation. This may lead to unjustifiable inaccuracies in the cost and constraints of gas-fired generation operations, and may affect both the timing and the extent of investment in new resources, such as renewable energy and energy storage. To address this issue, this paper develops a more detailed and rigorous mixed-integer model, and more importantly, a solution methodology utilizing surrogate level-based Lagrangian relaxation to overcome the combinatorial complexity that results from the enhanced level of model detail. This allows us to optimize a model with approximately 12 million binary and 100 million total variables in under 48 hours. The investment plan is compared with those produced by E3's RESOLVE software, which is currently employed by the California Energy Commission and California Public Utilities Commission. Our model produces an investment plan that differs substantially from that of the existing method and saves California over 12 billion dollars over the investment horizon.

We develop an explicit second order staggered finite difference discretization scheme for simulating the transport of highly heterogeneous gas mixtures through pipeline networks. This study is motivated by the proposed blending of hydrogen into natural gas pipelines to reduce end use carbon emissions while using existing pipeline systems throughout their planned lifetimes. Our computational method accommodates an arbitrary number of constituent gases with very different physical properties that may be injected into a network with significant spatiotemporal variation. In this setting, the gas flow physics are highly location- and time- dependent, so that local composition and nodal mixing must be accounted for. The resulting conservation laws are formulated in terms of pressure, partial densities and flows, and volumetric and mass fractions of the constituents. We include non-ideal equations of state that employ linear approximations of gas compressibility factors, so that the pressure dynamics propagate locally according to a variable wave speed that depends on mixture composition and density. We derive compatibility relationships for network edge domain boundary values that are significantly more complex than in the case of a homogeneous gas. The simulation method is evaluated on initial boundary value problems for a single pipe and a small network, is cross-validated with a lumped element simulation, and used to demonstrate a local monitoring and control policy for maintaining allowable concentration levels.