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A novel energy management system for a renewable based microgrid is proposed. It provides on-line set points for each generation unit, operation modes for a water supply system, and signals for consumers based on a demand side management mechanism. The smart microgrid is composed of photovoltaic panels, a wind turbine, a diesel generator, a battery bank, and a water supply system. The energy management system (EMS) minimizes the operational costs while supplying the water and electric load demands. It considers a two days ahead prediction of the weather conditions. Also, a neural network for a two days ahead electric consumption forecasting is designed. The system is implemented and tested using a real data set from a reference location. Results show the economic sense of the set points and management, for a practical implementation of the system in a specific location in Chile.Index Terms-Energy management system, demand side management, microgrid, wind turbine, photovoltaic panels, neural networks.978-1-4244-9894-9/11/$26.00 ©2011 IEEE

Hydrologic alteration due to hydropeaking reservoir operations is a main concern worldwide. Subdaily environmental flow constraints (ECs) on operations can be promising alternatives for mitigating negative impacts. However, those constraints reduce the flexibility of hydropower plants, potentially with higher costs for the power system. To study the economic and environmental efficiency of ECs, this work proposes a novel framework comprising four steps: (i) assessment of the current subdaily hydrologic alteration; (ii) formulation and implementation of a short-term, grid-wide hydrothermal coordination model; (iii) design of ECs in the form of maximum ramping rates (MRRs) and minimum flows (MIFs) for selected hydropower reservoirs; and (iv) identification of Pareto-efficient solutions in terms of grid-wide costs and the Richard-Baker flashiness index for subdaily hydrologic alteration (SDHA). The framework was applied to Chile's main power grid, assessing 25 EC cases, involving five MIFs and five MRRs. Each case was run for a dry, normal, and wet water year type. Three Pareto-efficient ECs are found, with remarkably small cost increase below 2% and a SDHA improvement between 28% and 90%. While the case involving the highest MIF worsens the flashiness of another basin, the other two have no negative effect on other basins and can be recommended for implementation.

This paper aims to analyse the economy-wide implications of a carbon tax applied on the Chilean electricity generation sector. In order to analyse the macroeconomic impacts, both an energy sectorial model and a Dynamic Stochastic General Equilibrium model have been used. During the year 2014 a carbon tax of 5 US$/tCO2e was approved in Chile. This tax and its increases (10, 20, 30, 40 and 50 US$/tCO2e) are evaluated in this article. The results show that the effectiveness of this policy depends on some variables which are not controlled by policy makers, for example, non-conventional renewable energy investment cost projections, natural gas prices, and the feasibility of exploiting hydroelectric resources. For a carbon tax of 20 US$/tCO2e, the average annual emission reduction would be between 1.1 and 9.1 million tCO2e. However, the price of the electricity would increase between 8.3 and 9.6 US$/MWh. This price shock would decrease the annual GDP growth rate by a maximum amount of 0.13%. This article compares this energy policy with others such as the introduction of non-conventional renewable energy sources and a sectorial cap. The results show that the same global OPEN ACCESSEnergies 2015, 8 2675 greenhouse gas (GHG) emission reduction can be obtained with these policies, but the impact on the electricity price and GDP are lower than that of the carbon tax.

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