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.

Zero Emission Neighborhoods (ZEN) is a concept studied in particular in the research center on ZEN in smart cities in Norway to reduce the CO₂ emissions of neighborhoods. One question coming along this concept is how to design the energy system of such neighborhoods to fit the ZEN definition[ 1 ]. From this definition we extract the CO₂ balance, requiring an annual net zero emission of CO₂ in the lifetime of the neighborhood. This paper proposes a MILP model for obtaining cost optimal design of ZEN’s energy system and demonstrates it on a case study. Different technologies are included as investment options and, notably PV as a mean of producing electricity on-site. Wind turbines are not included in this study because they would not be suitable in the context of most cities. The results highlight the importance of PV investment in reaching the ZEN requirements. For example, around 850 kW of solar is needed for our test cases of 10, 000 m² of floor area, for an annual energy demand of around 700 MWh of electricity and 620 MWh of heat. The investments in other technologies are small in comparison.