Periodic Reporting for period 2 - FHP (Flexible Heat and Power, Connecting heat and power networks by harnessing the complexity in distributed thermal flexibility.)
Reporting period: 2018-05-01 to 2019-10-31
The FHP project facilitates the use of distribution-grid connected P2H flexibility for not only mitigating RES curtailment, but as well for offering more demanding profile following services (e.g. balancing) by means of a coordinated control of clusters of heat-pumps and large Power-to-Heat conversion and storage vessels (like those provided by Ecovat). Specifically, it takes the local grid conditions into account to either solve local problems (like congestions) or to offer system services with distribution grid connected flexibility while ensuring that this does not cause local problems.
Specifically, the project aims at offering solutions that make it easier to use P2H flex from heatpumps – also from a building owner perspective – by improving the granularity and determinism of their response (power consumption) to a control action. This way, they should be able to offer more demanding – profile following - flex services like balancing.
Next to this, the project wants to facilitate the harvesting of flexibility from active buildings by introducing a Flex Trading concept where buildings, pro-actively or on request, provide their own consumption and flexibility forecast based on specific real-time date, to a Dynamic Coalition Manager. This information is aggregated in a bottom-up manner to create cluster forecasts that can be used for cluster level optimizations or flex service offerings to grid or market operators. This Flex Trading concept is proposed as an alternative to traditional cluster level forecasting based on historical and statistical data.
To guard local grid security, the project wants to use the combined information of bottom-up aggregated consumption and flex forecasts to determine and propose and optimal flex activation plan within the available flexibility. This Optimal Flex Dispatch is proposed as an alternative or enrichment of USEF that thus not leverage information of available flexibility and therefore can merely ask for A solution.
At the building level, multiple (grey-box and black-box) measurement-driven modeling approaches were developed that can be applied in retro-fit situations without requiring the intervention of a human modelling expert. Specific solutions were developed and demonstrated to overcome the problem of bad and missing data by indirectly estimating them using non-intrusive measurement from data that are available. For the multi-apartment pilot buildings we were able to forecast the overall temperature evolution – except some fast dynamics - in an accurate manner (e.g. error < 3 Degrees Celsius). Using these models, a flexibility forecast is done based on given comfort boundaries, and optimal planning is done within these comfort boundaries.
To determine the optimal amount and placement of sensors, a data-driven methodology was developed that uses statistical tests and advanced kernel independence tests to identify the most relevant sensors in a more robust and informative manner than regular correlation coefficient tests, and without requiring any building specific information.
A Grid Flex Heatpump concept was developed, that aims at enabling profile following services (e.g. balancing) with heatpumps by improving the granularity and determinism of their response to a control signal. Two variants have been proposed. One using an indirect control paradigm (outdoor sensor override) and one using a direct control paradigm (direct compressor speed control). For the direct control paradigm, very high accuracies have been achieved (up to being 98% of the time within a 3% error) but such an interface is not commonly available. The indirect control paradigm has a lesser accuracy (up to being 76% of the time within a 3% error) but it can be applied to almost all heatpumps. It requires though the creation of a heatpump signature model to determine the sensor override control signal for achieving the requested consumption. It was noted that the achievable accuracy is very much heatpump brand and model dependent (i.e. limited by the heatpump internal controller).
A date-driven grid-model free methodology was developed to enable buildings to autonomously decide on local flex activations for the real-time mitigation of local grid problems. This methodology uses a grid sensitivity map that is created from measurements without any knowledge of grid topology or cable characteristics to determine fair droop settings for each of the buildings. These fair droop settings, that are derived form the grid sensitivity map, result in a more equal spread of pains (demand to offer flex) and gains (opportunity to offer flex) than traditional droop settings.
A neighbourhood Impact Analysis Tool was developed that for a specific local grid and scenarios related to future growth of vRES and electrification of heating and transport (= flex) simulates the impact of flex activations on curtailment mitigation, self-consumption and self-sufficiency.
- Advances in human-expert free measurement driven multi-zone dynamic thermal modelling
- Standard-based bidirectional interface between Grid Flexible Heat-pumps and Building Energy Management Systems
- DSO Decision Support Algorithms to decide on optimal flex activations with multiple grid zones / congestion points
- Dynamic Coalition Manager concept acting on flex-graph based flexibility representations
Impact related to ‘Cost effective conversion of excess electricity, avoid curtailment, provide services to the grid’:
- mitigating RES curtailment thereby supporting accelerated further growth in RES and the de-carbonization of the energy mix
- accelerating the adoption of grid flexible heat-pumps thereby contributing to the de-carbonization of heating
- facilitating grid secure flex offering and trading, turning prosumers into active participants of the energy transformation
- stimulating flex offerings by removing barriers related to fear-for-lock-ins (Dynamic Coalition Manager), by enabling human-expert free flex modelling, and by offering fair compensation for flex activations based on flex bids