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Due to increasing amounts of energy being supplied by intermittent renewable energy sources, future energy systems need to be able to react flexibly to sudden changes. To address this challenge, a large body of research is devoted to improving the utilization of energy flexibility of various types of energy resources on the supply and demand side. This is commonly done by applying mathematical models. The objective of this paper is to review such models and identify common modeling approaches used to quantify and optimize the use of flexible energy resources. The literature review is conducted systematically, encompassing 215 publications that feature 694 models of different energy resources. The main distinction of this work, in comparison to other reviews on the topic of energy flexibility lies in its focus on the mathematical formulation of models of energy resources and the quantification of the models' level of detail. The aim is to identify common modeling approaches and the corresponding research problems they address. The review shows that the majority of research efforts are directed towards the management and optimization of distributed energy resources. The most commonly employed modeling approach involves abstract black-box models with limited levels of detail. Linear programming and meta-heuristics are frequently used to optimize energy flexibility. A critical reflection of the current common practice is conducted and recommendations for future research are formulated. [Display omitted] • Literature review of 215 reports presenting model approaches for energy flexibility. • Categories of research problems identified and deduction of two main research focuses. • Presenting models of common energy resources classified into different model types. • Mostly low level of detail seen in modeling energy flexibility. • Optimization models are used to represent and quantify flexibility. [ABSTRACT FROM AUTHOR]

Abstract: Considerable amounts of energy are consumed in supermarket refrigeration systems worldwide. Due to the thermal capacity of refrigerated goods and the rather simplistic control most commonly applied, there is a potential for distributing the system load over time in a more cost-optimal way. In this paper we describe a novel economic-optimizing Model Predictive Control (MPC) scheme that reduces operating costs by utilizing the thermal storage capabilities. A nonlinear optimization tool to handle a non-convex cost function is utilized for simulations with validated scenarios. In this way we explicitly address advantages from daily variations in outdoor temperature and electricity prices. Secondly, we formulate a new cost function that enables the refrigeration system to contribute with ancillary services to the balancing power market. This involvement can be economically beneficial for the system itself, while crucial services can be delivered to a future flexible and intelligent power grid (Smart Grid). Furthermore, we discuss a novel incorporation of probabilistic constraints and Second Order Cone Programming (SOCP) with economic MPC. A Finite Impulse Response (FIR) formulation of the system models allows us to describe and handle model as well as prediction uncertainties in this framework. This means we can demonstrate means for robustifying the performance of the controller. [Copyright &y& Elsevier]

Abstract: Smart grids utilise omnidirectional data transfer to operate a network of energy resources. Associated technologies present operators with greater control over system elements and more detailed information on the system state. While these features may improve the theoretical optimal operating performance, determining the optimal operating strategy becomes more difficult. In this paper, we show how a decision tree ensemble or ‘forest’ can produce a near-optimal control strategy in real time. The approach substitutes the decision forest for the simulation–optimisation sub-routine commonly employed in receding horizon controllers. The method is demonstrated on a small and a large network, and compared to controllers employing particle swarm optimisation and evolutionary strategies. For the smaller network the proposed method performs comparably in terms of total energy usage, but delivers a greater demand deficit. On the larger network the proposed method is superior with respect to all measures. We conclude that the method is useful when the time required to evaluate possible strategies via simulation is high. [Copyright &y& Elsevier]