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A large fraction of Earth’s plant species are faced with increased chances of extinction.

In the recent years, due to the economic and environmental requirements, the use of distributed generations (DGs) has increased. If DGs have the optimal size and are located at the optimal locations, they are capable of enhancing the voltage profile and reducing the power loss. This paper proposes a new approach to obtain the optimal location and size of DGs. To this end, exchange market algorithm (EMA) is offered to find the optimal size and location of DGs subject to minimizing loss, increasing voltage profile, and improving voltage stability in the distribution systems. The effectiveness of the proposed approach is verified on both 33- and 69-bus IEEE standard systems. 6 pages, 3 figures, IEEE Green Energy and Smart Systems Conference (IGESSC 2020)

We propose a new method for generating semidefinite relaxations of optimal power flow problems. The method is based on chordal conversion techniques: by dropping some equality constraints in the conversion, we obtain semidefinite relaxations that are computationally cheaper, but potentially weaker, than the standard semidefinite relaxation. Our numerical results show that the new relaxations often produce the same results as the standard semidefinite relaxation, but at a lower computational cost. Revised manuscript; accepted for publication in IEEE Trans. on Power Systems

Part one of this two-part paper presents new models for evaluating flexible resources in two-settlement electricity markets (day-ahead and real-time) with uncertain net loads (demand minus wind). Physical resources include wind together with fast- and slow-start demand response and thermal generators. We also model financial participants (virtual bidders). Wind is stochastic, represented by a set of scenarios. The two-settlement system is modeled as a two-stage process in which the first stage involves unit commitment and tentative scheduling, while the second stage adjusts flexible resources to resolve imbalances. The value of various flexible resources is evaluated through four two-settlement models: 1) an equilibrium model in which each player independently schedules its generation or purchases to maximize expected profit; 2) a benchmark (expected system cost minimization); 3) a sequential equilibrium model in which the independent system operator first optimizes against a deterministic wind power forecast; and 4) an extended sequential equilibrium model with self-scheduling by profit-maximizing slow-start generators. A tight convexified unit commitment allows for demonstration of certain equivalencies of the four models. We show how virtual bidding enhances market performance, since, together with self-scheduling by slow-start generators, it can help a deterministic day-ahead market to choose the most efficient unit commitment.

AbstractAnthropogenic deoxygenation of the Baltic Sea caused major declines in demersal and benthic habitat quality with consequent impacts on biodiversity and ecosystem services. Using Baltic cod otolith chemical proxies of hypoxia, salinity, and fish metabolic status and growth, we tracked changes from baseline conditions in the late Neolithic (4500 BP) and early twentieth century to the present, in order to understand how recent, accelerating climate change has affected this key species. Otolith hypoxia proxies (Mn:Mg) increased with expanding anoxic water volumes, but decreased with increasing salinity indexed by otolith Sr:Ca. Metabolic status proxied by otolith Mg:Ca and reconstructed growth were positively related to dissolved oxygen percent saturation, with particularly severe declines since 2010. This long-term record of otolith indicators provides further evidence of a profound state change in oxygen for the worse, in one of the world’s largest inland seas. Spreading hypoxia due to climate warming will likely impair fish populations globally and evidence can be tracked with otolith chemical biomarkers.

Amphibian population declines far exceed those of other vertebrate groups, with 30% of all species listed as threatened by the International Union for Conservation of Nature. The causes of these declines are a matter of continued research, but probably include climate change, land-use change and spread of the pathogenic fungal disease chytridiomycosis. Here we assess the spatial distribution and interactions of these primary threats in relation to the global distribution of amphibian species. We show that the greatest proportions of species negatively affected by climate change are projected to be found in Africa, parts of northern South America and the Andes. Regions with the highest projected impact of land-use and climate change coincide, but there is little spatial overlap with regions highly threatened by the fungal disease. Overall, the areas harbouring the richest amphibian faunas are disproportionately more affected by one or multiple threat factors than areas with low richness. Amphibian declines are likely to accelerate in the twenty-first century, because multiple drivers of extinction could jeopardize their populations more than previous, mono-causal, assessments have suggested.

Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets’ planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage decision-dependent stochastic optimization model for long-term large-scale generation expansion planning, where large amounts of wind power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming model. The wind penetration, investment decisions, and the optimality of the decision-dependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning.