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Smart thermostats have become a promising device to control electric baseboard heaters' energy consumption while considering their flexibility in the demand response (DR) context. This article applies a shrinked-space search method to identify the tuning parameters of line voltage communicating thermostats (LVCTs). The proposed approach based on Bayesian optimization (BO) algorithm takes account of a reference model to drastically shrink the search space while enforcing the identification of a single set of parameters compatible with all arising dynamics of the controller and helping to establish an interpretable model. Furthermore, a subsequent integral tracking strategy has been adopted to convexify the objective function (for identification purposes) while considering the logical constraints governing the thermostat dynamics. This helps to recover the updating logic of the integral part of the controller model. The experimental validation results of eight LVCTs operating in an inhabited house show the effectiveness of the proposed method since it leads to establishing digital twins for the studied controllers. In addition, a case study is presented to demonstrate the usefulness of the reconstructed model in a DR framework.

New grid management schemes have created exciting opportunities for end customers to maximize their utility by becoming active participants. In particular, Transactive Energy Systems (TES) allow customers to cooperate and negotiate in energy markets, increasing social welfare. These interactions also reduce demand-side uncertainties and simplify grid balancing at different levels. In TES, it is beneficial to employ forward contracts because they establish conditions for future energy supply, allowing grid maintainers to plan a cost-effective operation. Thus, end customers interact in local retail markets in advance to agree on service conditions that fulfill their needs. This paper presents a comprehensive analysis of the risks involved in those forward contracts with the aim of providing valuable information to participants. The TES environment modifies the typical risks of electricity contracts due to the information exchange in the negotiation and execution stages. Indeed, reliable data and realistic forecasting assumptions become a primary concern for each participant since they constitute the main threat of contract defaulting. Risk management strategies are presented in bow-tie and Ishikawa diagrams to elicit the decisions for market participants. Case study results demonstrate that forecasting errors impact the conditional value at risk of the contracts, in proportion to the demand uncertainty.