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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]

Reducing local and global emissions is one of the drivers behind the promotion of renewable energy technologies (RETs). Some RET projects (e.g. solar, marine power, and some biomass and wind) usually require supplemental income from carbon credits in order to make them economically viable. This paper presents the mathematical development of nodal marginal CO 2 emission metrics for hydrothermal systems. These metrics are obtained from optimal power flow and hydrothermal dispatch models considering hydro uncertainty. The proposed model allows a more accurate assessment of the emission reduction effect of RET projects and the provision of efficient carbon signals to the system. Modeling of hydro reservoirs and intertemporal constraints also allows incorporating the emission mitigation potential of water storage. The concepts of marginal CO 2 emissions for hydrothermal systems are first exemplified using a small test system, and then simulations on a real hydrothermal system illustrate how these metrics can inform investment and operational decisions. Theoretical and simulation results show that transmission and hydro storage constraints neglect may provide wrong signals for RET investors, missing the incentives to install in areas where the potential for emissions reduction is higher (where renewable energy displaces a larger amount of coal or diesel). Policy makers can use the proposed model to assess the cost-effectiveness of emission reduction policies. [ABSTRACT FROM AUTHOR]

Around the world, 1.1 billion people are severely affected by their lack of access to electricity. Other vulnerable communities receive low quality access, or face expensive prices that force them to restrict their consumption because of suboptimal technology choices made by their suppliers, which are sometimes forced by local regulation. Microgrids, properly sized and managed, may represent the best option to overcome these dilemmas, offering a tailor made supply. Today’s standard methodologies to design isolated microgrids optimize the cost of supply as well as the cost of the energy not served with an exogenous per unit value for the lost load. They do not include community’s restrictions, such as willingness-to-pay, consumption level, budget constraints or its particular (endogenous) value of lost load. We developed a novel methodology that offers a range of microgrid designs to an isolated community, where each of them is optimal for a particular consumption pattern and value of lost load, from which the community may choose the one that best suits their needs. For this purpose, a Pareto optimal cost-coverage trade-off was constructed for an isolated community in northern Chile. A three-stage optimization was done: capacity (Genetic Algorithm), operation (robust optimization and mixed integer linear programming) and configuration ( DC or AC ). Diesel, gas, PV, wind and storages were modeled and 176 designs were found in total. More expensive microgrids (and with a larger electricity coverage) have hybrid mixes (conventional and renewable) and have an almost linear total cost from 298 to 249 USD/MW h for ENS from 0% to 28%. Lower quality microgrids are fully renewable, providing a very cheap but unreliable supply. The direct impact of lower-cost/limited supply microgrids offered here is the improvement of the quality of life of millions of vulnerable people, but it requires adjustments in the country’s public policies of electrification programs. [ABSTRACT FROM AUTHOR]

• A microgrid + learning curves Robust framework: decision to install energy resources. • Impact of Distributed Resources on users & utility: evolution of 4 tariff-designs. • The pioneering case of Chile: massive, competitive Photovoltaics without subsidies. • Bankruptcy risk and Death Spiral: avoided with multipart tariff design. • Peak-shaving: with three-part tariff infant storage is possible in Chile. In the future, drastic cost reductions of Distributed Energy Resources will probably drive their deployment without the need of economic incentives - especially photovoltaic energy. Dynamic Grid Parity Models combine learning curves with grid-parity. They are the state-of-the-art solution to assess the time-evolving competitiveness of generation technologies, but fail to capture the residential end-user's choices of installing Distributed Energy Resources once they become feasible. We propose a robust framework based on a local and optimal microgrid combined with learning curves to assess the potential penetration of Distributed Energy Resources in households. This framework adds a notably richer interaction between the elements of the distribution system, e.g., optimal dispatch or peak shaving. We quantify the time-evolution of residential end-user's bills and the utility's revenue, applied to four tariff designs. Today Chile pioneers a massive deployment of photovoltaic systems without incentives, becoming a unique example worldwide, specially the so called "Solar City of Diego de Almagro", a town with a remarkable solar resource and massive PV deployment, chosen as the case study. Results show PV dominance with flat bundled volumetric tariffs and the increase of utility's bankruptcy risk if tariffs are not updated (47% revenue reduction). If updated, bills would increase 24%, affecting non-owners. A two-part tariff overcomes this but it is regressive and it delays PV deployment. A three-part tariff improves efficiency and introduces prosumage, with a small peak-shaving effect. Owners could face regulatory risks due to possible tariff design changes. This study lays the foundation for future rate cases, and for distribution and transmission planning. [ABSTRACT FROM AUTHOR]

• Significant levels of self-sufficiency based on renewable microgrids (MG) in Chile. • Community MGs are generally more profitable than MGs for single-dwellings. • Renewable MGs are effective at reducing the community peak demand tariff. • Better profitability and self-sufficiency levels are found in rural areas. • We recommend removing barriers to community microgrid projects. Grid-connected microgrids (MG) can offer significant benefits to electricity systems by improving the security of energy supply with clean renewable energy sources. Unlocking these benefits requires designing suitable business models that can capture the value of MGs and that help removing barriers to their uptake. In this work, we evaluate the profitability and self-sufficiency of optimally designed MGs for new proposed business models. The business models are tested in seventeen different geographical locations which offer a vast diversity of renewable resources, seasonal climatic variability and electricity tariffs. Both Net Billing and Net Metering schemes are tested, for both single-dwelling and community residential customers. Electricity tariffs include both energy volumetric and peak demand charges. We model the optimal sizing and energy management of the MGs by minimizing the total customer electricity costs. The generation mix of the MGs is the result of a bilevel optimization which take into account renewable resources and costs, diesel prices and tariffs. MGs can potentially be comprised by solar PV, wind and diesel generation, together with battery storage systems and demand response resources. We find significant levels of self-sufficiency based on solar sources across the country, and also wind power in some specific locations. The community business model, case which includes the peak demand charges, is generally more profitable than the case of single-dwellings. This shows that the MGs are effective at reducing the peak demand charge of a residential electricity bill. We therefore recommend the further promotion of community energy arrangements as a key way to improve the current very slow uptake of distributed generation in developing countries. [ABSTRACT FROM AUTHOR]