Electricity accounts for 25% of global greenhouse gas emissions. Reducing emissions related to electricity consumption requires accurate measurements readily available to consumers, regulators and investors. In this case study, we propose a new real-time consumption-based accounting approach based on flow tracing. This method traces power flows from producer to consumer thereby representing the underlying physics of the electricity system, in contrast to the traditional input-output models of carbon accounting. With this method we explore the hourly structure of electricity trade across Europe in 2017, and find substantial differences between production and consumption intensities. This emphasizes the importance of considering cross-border flows for increased transparency regarding carbon emission accounting of electricity. AT BE BG CZ DE DK1 DK2 EE ES FI FR GB GR HU IE IT LT LV ME NL NO PL PT RO RS SE SI SK

In a real-time electricity pricing context where consumers are sensitive to varying prices, having the ability to anticipate their response to a price change is valuable. This paper proposes models for the dynamics of such price-response, and shows how these dynamics can be used to control electricity consumption using a one-way price signal. Estimation of the price-response is based on data measurable at grid level, removing the need to install sensors and communication devices between each individual consumer and the price-generating entity. An application for price-responsive heating systems is studied based on real data, before conducting a control by price experiment using a mixture of real and synthetic data. With the control objective of following a constant consumption reference, peak heating consumption is reduced by nearly 5%, and 11% of the mean daily heating consumption is shifted.

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An optimized heat pump control for building heating was developed for minimizing CO 2 emissions from related electrical power generation. The control is using weather and CO 2 emission forecasts as inputs to a Model Predictive Control (MPC)—a multivariate control algorithm using a dynamic process model, constraints and a cost function to be minimized. In a simulation study, the control was applied using weather and power grid conditions during a full-year period in 2017–2018 for the power bidding zone DK2 (East, Denmark). Two scenarios were studied; one with a family house and one with an office building. The buildings were dimensioned based on standards and building codes/regulations. The main results are measured as the CO 2 emission savings relative to a classical thermostatic control. Note that this only measures the gain achieved using the MPC control, that is, the energy flexibility, not the absolute savings. The results show that around 16% of savings could have been achieved during the period in well-insulated new buildings with floor heating. Further, a sensitivity analysis was carried out to evaluate the effect of various building properties, for example, level of insulation and thermal capacity. Danish building codes from 1977 and forward were used as benchmarks for insulation levels. It was shown that both insulation and thermal mass influence the achievable flexibility savings, especially for floor heating. Buildings that comply with building codes later than 1979 could provide flexibility emission savings of around 10%, while buildings that comply with earlier codes provided savings in the range of 0–5% depending on the heating system and thermal mass.

A machine learning algorithm is developed to forecast the CO 2 emission intensities in electrical power grids in the Danish bidding zone DK2, distinguishing between average and marginal emissions. The analysis was done on data set comprised of a large number (473) of explanatory variables such as power production, demand, import, weather conditions etc. collected from selected neighboring zones. The number was reduced to less than 30 using both LASSO (a penalized linear regression analysis) and a forward feature selection algorithm. Three linear regression models that capture different aspects of the data (non-linearities and coupling of variables etc.) were created and combined into a final model using Softmax weighted average. Cross-validation is performed for debiasing and autoregressive moving average model (ARIMA) implemented to correct the residuals, making the final model the variant with exogenous inputs (ARIMAX). The forecasts with the corresponding uncertainties are given for two time horizons, below and above six hours. Marginal emissions came up highly independent of any conditions in the DK2 zone, suggesting that the marginal generators are located in the neighbouring zones.The developed methodology can be applied to any bidding zone in the European electricity network without requiring detailed knowledge about the zone.