• Energy Research
• 7. Clean energy close

Microgrids (MGs) are sustainable solutions for rural zone electrification that use local renewable resources. However, only careful planning at the start of an MG project can ensure its future optimal operation. In this paper, a novel methodology for MG planning by using the uncertainty characterization of renewable resources and demand is presented. Additionally, a model of electricity consumption is proposed and applied in an isolated rural community. In such communities, consumption patterns typically need to be derived as model inputs because consumption measurements are not available for the planning stage. To obtain these inputs, clustering algorithms based on self-organizing maps (SOMs) and fuzzy c-means are used to classify the families of the community given sociodemographic information obtained via surveys. Subsequently, Markov chains (MCs) are employed to generate consumption patterns based on consumption measurements in some dwellings and surveys applied to the community. The nonlinearities and uncertainties associated with renewable resources and consumption are modeled by using prediction interval (PI) models. These PI models provide the required consumption and generation scenarios for deriving the optimal sizing and topological information to address the MG planning problem. The results of the robust planning approach based on scenarios are useful at the feasibility and design phases of an MG project. The proposed methodology is successfully applied to MG planning for a rural Mapuche community, where a conservative criterion was considered to minimize the investment risk. This criterion corresponds to the worst-case scenario in which the demand increases by 19.9% compared to that of the baseline scenario and a lower energy cost is obtained. However, the net present cost and operational costs increase by 14% and 11.75% compared to those of the baseline scenario, respectively. Railway Engineering

Microgrids (MGs) are sustainable solutions for rural zone electrification that use local renewable resources. However, only careful planning at the start of an MG project can ensure its future optimal operation. In this paper, a novel methodology for MG planning by using the uncertainty characterization of renewable resources and demand is presented. Additionally, a model of electricity consumption is proposed and applied in an isolated rural community. In such communities, consumption patterns typically need to be derived as model inputs because consumption measurements are not available for the planning stage. To obtain these inputs, clustering algorithms based on self-organizing maps (SOMs) and fuzzy c-means are used to classify the families of the community given sociodemographic information obtained via surveys. Subsequently, Markov chains (MCs) are employed to generate consumption patterns based on consumption measurements in some dwellings and surveys applied to the community. The nonlinearities and uncertainties associated with renewable resources and consumption are modeled by using prediction interval (PI) models. These PI models provide the required consumption and generation scenarios for deriving the optimal sizing and topological information to address the MG planning problem. The results of the robust planning approach based on scenarios are useful at the feasibility and design phases of an MG project. The proposed methodology is successfully applied to MG planning for a rural Mapuche community, where a conservative criterion was considered to minimize the investment risk. This criterion corresponds to the worst-case scenario in which the demand increases by 19.9% compared to that of the baseline scenario and a lower energy cost is obtained. However, the net present cost and operational costs increase by 14% and 11.75% compared to those of the baseline scenario, respectively. Railway Engineering

This paper presents the design and evaluation of a dynamic simulator for an ISCC (integrated solar combined cycle) plant. The design of the simulator is based on the phenomenological equations for both a combined cycle plant and a solar plant. The simulator incorporates a regulatory control strategy based on PI (proportional-integral) controllers and was developed in the MATLAB/Simulink® environment. A MPC (model predictive control) strategy established at a supervisory level is presented. The intent of the strategy is to regulate the steam pressure of the superheater of the ISCC plant. The combined use of the simulator and the supervisory control strategy allows for the quantification of the reduction in fuel consumption that can be achieved when integrated solar collectors are used in a combined cycle plant. The ISCC plant simulator is suitable for designing, evaluating and testing control strategies and for planning the integration of solar and combined cycle

This paper presents the design and evaluation of a dynamic simulator for an ISCC (integrated solar combined cycle) plant. The design of the simulator is based on the phenomenological equations for both a combined cycle plant and a solar plant. The simulator incorporates a regulatory control strategy based on PI (proportional-integral) controllers and was developed in the MATLAB/Simulink® environment. A MPC (model predictive control) strategy established at a supervisory level is presented. The intent of the strategy is to regulate the steam pressure of the superheater of the ISCC plant. The combined use of the simulator and the supervisory control strategy allows for the quantification of the reduction in fuel consumption that can be achieved when integrated solar collectors are used in a combined cycle plant. The ISCC plant simulator is suitable for designing, evaluating and testing control strategies and for planning the integration of solar and combined cycle

Summary This paper proposes a novel simulator of energy consumption patterns that allows designing demand side management (DSM) strategies without economic incentives. The simulator emulates consumers' patterns with and without installed DSM interfaces, based on both actual consumption measurements and surveys applied to the inhabitants of an existing isolated microgrid (Huatacondo, Chile) that has a particular DSM strategy without economic incentives. The simulator uses Markov chains to generate data characterizing consumption patterns without DSM and Bayesian networks for cases in which the users respond to the DSM strategy. Data obtained from the simulator are used to derive a response model of the consumers to the DSM interface, which can be included for the energy management system design. Results show that the implemented strategy can be effective and can generate savings up to 4.45% in diesel consumption for an ideal case where all the dwellings have the interface installed. Copyright © 2016 John Wiley & Sons, Ltd.

Summary This paper proposes a novel simulator of energy consumption patterns that allows designing demand side management (DSM) strategies without economic incentives. The simulator emulates consumers' patterns with and without installed DSM interfaces, based on both actual consumption measurements and surveys applied to the inhabitants of an existing isolated microgrid (Huatacondo, Chile) that has a particular DSM strategy without economic incentives. The simulator uses Markov chains to generate data characterizing consumption patterns without DSM and Bayesian networks for cases in which the users respond to the DSM strategy. Data obtained from the simulator are used to derive a response model of the consumers to the DSM interface, which can be included for the energy management system design. Results show that the implemented strategy can be effective and can generate savings up to 4.45% in diesel consumption for an ideal case where all the dwellings have the interface installed. Copyright © 2016 John Wiley & Sons, Ltd.