This work introduces a generic methodology to determine the hourly average CO_2eq. intensity of the electricity mix of a bidding zone. The proposed method is based on the logic of input–output models and avails the balance between electricity generation and demand. The methodology also takes into account electricity trading between bidding zones and time-varying CO_2eq. intensities of the electricity traded. The paper shows that it is essential to take into account electricity imports and their varying CO_2eq. intensities for the evaluation of the CO_2eq. intensity in Scandinavian bidding zones. Generally, the average CO_2eq. intensity of the Norwegian electricity mix increases during times of electricity imports since the average CO_2eq. intensity is normally low because electricity is mainly generated from hydropower. Among other applications, the CO_2eq. intensity can be used as a penalty signal in predictive controls of building energy systems since ENTSO-E provides 72 h forecasts of electricity generation. Therefore, as a second contribution, the demand response potential for heating a single-family residential building based on the hourly average CO_2eq. intensity of six Scandinavian bidding zones is investigated. Predictive rule-based controls are implemented into a building performance simulation tool (here IDA ICE) to study the influence that the daily fluctuations of the CO_2eq. intensity signal have on the potential overall emission savings. The results show that control strategies based on the CO_2eq. intensity can achieve emission reductions, if daily fluctuations of the CO_2eq. intensity are large enough to compensate for the increased electricity use due to load shifting. Furthermore, the results reveal that price-based control strategies usually lead to increased overall emissions for the Scandinavian bidding zones as the operation is shifted to nighttime, when cheap carbon-intensive electricity is imported from the continental European power grid.

Conventional key performance indicators (KPI) assessed in building simulation lack specific measures of how the building interacts with the grid and its energy flexibility. This paper aims to provide an overview of specific energy flexibility performance indicators, together with supporting control strategies. If applied correctly, the indicators help improving the building performance in terms of energy flexibility and can enable minimization of operational energy costs. Price-based load shifting, self-generation and self-consumption are among the most commonly used performance indicators that quantify energy flexibility and grid interaction. It has been found that the majority of performance indicators, specific to energy flexibility, are combined with rule-based control. Only a limited amount of specific energy flexibility KPIs are used in combination with optimal control or model predictive control. Both of these advanced control approaches often have a couple of economic or comfort objectives that do not take into account an energy flexibility KPI. There is evidence that recent model predictive control approaches incorporate some aspects of building energy flexibility to minimize operational cost in conjunction with time varying pricing.

An office building in Trondheim, Norway, was used as a case study to test the influence of Predictive Control (PC) for the optimization of energy use on the employees’ thermal comfort. A predictive control was implemented in the Building Energy Management System (BEMS) by operating on the supply temperature of the radiator circuit. A questionnaire was given to the employees to evaluate to what extent the operation of the predictive control influenced their perceived thermal comfort. Several factors known to influence employees’ satisfaction (such as office type, perceived noise level, level of control, perceived luminous environment, perceived indoor air quality, adaptation strategies, well-being) were investigated in the questionnaire. The evaluation shows that the occupants rated the perceived thermal comfort as equally good compared to the business-as-usual operation. This is an important finding toward the user acceptance of such predictive control schemes.