Predictive control for multi-market trade of aggregated demand response using a black box approach

Aggregated demand response for smart grid services is a growing field of interest especially for market participation. To minimize economic and network instability risks, flexibility characteristics such as shiftable capacity must be known. This is traditionally done using lower level, end user, device specifications. However, with these large numbers, having lower level information, has both privacy and computational limitations. Previous studies have shown that black box forecasting of shiftable capacity, using machine learning techniques, can be done accurately for a homogeneous cluster of heating devices. This paper validates the machine learning model for a heterogeneous virtual power plant. Further it applies this model to a control strategy to offer flexibility on an imbalance market while maintaining day ahead market obligations profitably. It is shown that using a black box approach 89% optimal economic performance is met. Further, by combining profits made on imbalance market and the day ahead costs, the overall monthly electricity costs are reduced 20%

Day-ahead trading of aggregated energy flexibility

Flexibility of small loads, in particular from Electric Vehicles (EVs), has recently attracted a lot of interest due to their possibility of participating in the energy market and the new commercial potentials. Different from existing work, the aggregation technique proposed in this paper produces flexible aggregated loads from EVs taking into account technical market requirements. The flexible aggregated loads can be further traded in the day-ahead market by a Balance Responsible Party (BRP) via the so-called flexible orders. As a result, the BRP can achieve more than 19% cost reduction in energy purchase based on the 2017 real electricity prices from Danish electricity market.Peer ReviewedPostprint (author's final draft

New market designs in electricity market simulation models: Deliverable D4.5

Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ABSTRACT: To integrate a high share of renewables in a future system, several modifications to the electricity market rules may need to be implemented. The most relevant market design concepts were identified from the literature and reported in work package 3. There are several uncertainties, for instance with respect to the questions of whether a future electricity market will provide enough incentives for investment in variable renewable energy sources (vRES) – mainly solar and wind energy – and in flexibility options, especially for long periods with insufficient vRES generation. In this deliverable, the modelling requirements to analyse the new market rules are determined. The modelling efforts will reflect the main policy choices and are based on the strengths of the modelling capabilities from the consortium. The model enhancements to represent the temporal, spatial and sectoral flexibility will be approached in deliverables 4.1 to 4.3. For this reason, these topics will be described only briefly in this deliverable.N/