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Electric energy storage technologies have recently been in the spotlight, discussed as essential grid assets that can provide services to increase the reliability and resiliency of the grid, including furthering the integration of variable renewable energy resources. Though they can provide numerous grid services, there are a number of factors that restrict their current deployment. The most significant barrier to deployment is high capital costs, though several recent deployments indicate that capital costs are decreasing and energy storage may be the preferred economic alternative in certain situations. However, a number of other market and regulatory barriers persist, limiting further deployment. These barriers can be categorized into regulatory barriers, market (economic) barriers, utility and developer business model barriers, cross-cutting barriers and technology barriers. This report, through interviews with stakeholders and review of regulatory filings in four regions roughly representative of the United States, identifies the key barriers restricting further energy storage development in the country. The report also includes a discussion of possible solutions to address these barriers and a review of initiatives around the country at the federal, regional and state levels that are addressing some of these issues. Energy storage could have a key role to play in the future grid, but market and regulatory issues have to be addressed to allow storage resources open market access and compensation for the services they are capable of providing. Progress has been made in this effort, but much remains to be done and will require continued engagement from regulators, policy makers, market operators, utilities, developers and manufacturers.

Electric energy storage technologies have recently been in the spotlight, discussed as essential grid assets that can provide services to increase the reliability and resiliency of the grid, including furthering the integration of variable renewable energy resources. Though they can provide numerous grid services, there are a number of factors that restrict their current deployment. The most significant barrier to deployment is high capital costs, though several recent deployments indicate that capital costs are decreasing and energy storage may be the preferred economic alternative in certain situations. However, a number of other market and regulatory barriers persist, limiting further deployment. These barriers can be categorized into regulatory barriers, market (economic) barriers, utility and developer business model barriers, cross-cutting barriers and technology barriers. This report, through interviews with stakeholders and review of regulatory filings in four regions roughly representative of the United States, identifies the key barriers restricting further energy storage development in the country. The report also includes a discussion of possible solutions to address these barriers and a review of initiatives around the country at the federal, regional and state levels that are addressing some of these issues. Energy storage could have a key role to play in the future grid, but market and regulatory issues have to be addressed to allow storage resources open market access and compensation for the services they are capable of providing. Progress has been made in this effort, but much remains to be done and will require continued engagement from regulators, policy makers, market operators, utilities, developers and manufacturers.

Artificial neural networks (ANN) are widely accepted as a tool that offers an alternative way to tackle complex problems in optimizing the food processes. The interest in using ANN in optimization arises from their capacity to model without any assumptions about the nature of underlying mechanisms and their ability to take into account non-linearities and iterations between variables as well as to perform rapid calculations. They have been used in optimization of parameters, resulting in optimal production of the food preparation. ANN inverse (ANNi) is also being applied to optimize the operating conditions or parameters in food processes. ANNi is a new tool that inverts an ANN and uses an optimization method to find the optimum parameter value (or unknown parameter) for given required conditions in the processes. In order to perform ANNi, it is necessary to build the ANN that simulates the output parameters of a food process. In general, this class of ANN model is constituted of a feedforward network with one hidden layer, considering one or more well-known input parameter(s) of the process. Normally, a Levenberg-Marquardt learning algorithm, a hyperbolic tangent sigmoid and a linear transfer-function with several neurons in the hidden layer (because of the process complexity) are considered in the built model. With the required or optimized output and some input parameters in ANNi, it is possible to calculate the unknown input parameters. Consequently, the ANNi is applied to determine the optimal parameters and it already has applications in different process with very short elapsed time (seconds). The great advantage of using ANNi lies in the fact that it makes it faster and easier to predict optimal parameters to control the food preparation.

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