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• Aggregation and cooperation among peers will expand beyond prosumers in microgrids. • Growth in IoT and metering infrastructure is empowering end-use consumers. • Methodology allows aggregated consumers to schedule load and share energy. • Application demonstrates a significant cost reduction while assuring fairness. • Offers a novel cost-sharing strategy guided by cost minimization and Nash bargaining. End-use consumers (peers) are being empowered to aggregate for direct demand-side participation through load scheduling and energy sharing. This is the result of the growth of Internet of Things (IoT) enabled loads, availability of advanced metering infrastructure, and the move towards real-time (RT) pricing of electricity. Peer-to-peer (P2P) cooperation has received significant interest in recent years, though the focus of this growing body of research is on modeling prosumer behavior in microgrids. Hence, there is a need for new methodologies to examine empowerment of all end-use consumers (not limited to prosumers) to form aggregations and develop fair rules of cooperation to reduce cost. This paper offers an optimization based methodology to address the above need for power systems. It minimizes the total cost and considers fairness using a Nash bargaining approach. Since cost and fairness are often in conflict, trade-off strategies are also presented. The model to asses fairness is nonlinear. Hence, it is transformed into a second order cone program (SOCP) and solved using GUROBI software version 7.5.2. The methodology is implemented on a sample 5-bus network, built using price and demand data from one of the load zones of Pennsylvania, New Jersey, and Maryland (PJM) power network in the United States. It is shown that two aggregations of peers participating in the sample network can reduce their total cost by 14.17% and 22.7%, while maintaining fairness. Concluding remarks highlight some of the limitations of the methodology. [ABSTRACT FROM AUTHOR]

• Substitution of 2nd generation for 1st generation biofuel is investigated. • An optimization model for the 2nd generation biofuel supply chain is proposed. • Both economic and environmental performance measures are examined. • 2nd generation biofuel is more cost effective than 1st generation biofuel. • Considering stover handling or not leads to different environmental performances. Multiple generations of biofuel manufacturing technologies have been proposed and developed, among which first generation technology is the most mature. It typically utilizes corn grain as feedstock in bio-refineries in many countries such as the United States. However, the use of edible matter of crops like corn may lead to a food-versus-fuel competition. To address such an issue, second generation biofuel manufacturing technology using the non-edible matter of crops has been developed. In this paper, the economic viability and environmental sustainability are analyzed and quantified, when restructuring the biofuel supply chain from employing first generation manufacturing technology to the one utilizing second generation technology considering the existing supply chain infrastructure. Two supply chain restructuring strategies, i.e., distributed and centralized preprocessing deployment, are modeled for implementing biomass preprocessing for second generation biofuel manufacturing. Bi-objective optimization formulations for the corn stover-sourced biofuel (a typical second generation biofuel) supply chain considering both economic and environmental aspects are proposed under both strategies. Different decision variables such as the locations selected for constructing preprocessing centers and the corresponding corn stover handling capacities of such centers, as well as the material flows between farms, preprocessing centers, and bio-refinery plants are identified. A case study based on the state of Missouri in the United States is implemented to illustrate the effectiveness of the proposed model and analyze the performance of different strategies. The results of the case study show that when corn stover is used as feedstock in bio-refineries, the unit cost of bioethanol production can be reduced, while the unit emission is increased compared to the corn-sourced supply chain under both supply chain restructuring strategies. Specifically, in Missouri where the daily stover handling amount per bio-refinery plant is relatively low and the stover collection radius is relatively small, the centralized strategy outperforms the distributed one with a higher cost reduction and a smaller increase in emissions. It reduces the unit cost by 27.39%, while increasing the unit emission by 24.42%, compared to the corn-sourced supply chain. [ABSTRACT FROM AUTHOR]

Highlights • We analyze the economic value of a U.S windfarm using long-term LCOE information. • Long-term LCOE of wind energy is obtained from two decades of history. • Monte Carlo and Risk-Neutral valuation is used to valuate a reference windfarm. • Managerial flexibilities and generation cost uncertainty make windfarms more valuable. • Real Option accounts for the flexibility of decision-making with stochastic costs. Abstract Renewable energies are a natural replacement for conventional or fossil fuel energy generation. One of these sources, wind energy, has exhibited significant cost reductions during the last decades. Even though this historical cost reduction trend is well known in the industry and in the academia, information on this cost trend and volatility has been scarcely incorporated in previous works, thus a natural concern arises when assessing their influence on the economic valuation of wind farms. Moreover, wind projects are modular in size and have a short time-to-market, providing a valuable managerial flexibility to defer investment under non-attractive market conditions, such as high development cost scenarios. Traditional evaluation tools often consider those scenarios, wrongly assuming that wind-developers would move forward with the project under such conditions. Accordingly, this paper analyzes the effect of incorporating both generation cost history and investment flexibility in the valuation of wind farms in the United States using Real Options methodology. Taking two decades of cost data and a reference wind farm, this "uncertainty-flexibility" effect is incorporated and valued, finding that the value of the wind project is 14.2% larger than the one neglecting such effect – where cost stochasticity and managerial flexibility are ignored. Therefore, traditional tools for project valuation, such as discounted cash flow and scenario analysis can no longer solely be used, as they do not properly account for the cost reduction trend, uncertainty, and managerial flexibility that a wind farm could face. [ABSTRACT FROM AUTHOR]