Performance Assessment of Black Box Capacity Forecasting for Multi-Market Trade Application

With the growth of renewable generated electricity in the energy mix, large energy storage and flexible demand, particularly aggregated demand response is becoming a front runner as a new participant in the wholesale energy markets. One of the biggest barriers for the integration of aggregator services into market participation is knowledge of the current and future flexible capacity. To calculate the available flexibility, the current aggregator pilot and simulation implementations use lower level measurements and device specifications. This type of implementation is not scalable due to computational constraints, as well as it could conflict with end user privacy rights. Black box machine learning approaches have been proven to accurately estimate the available capacity of a cluster of heating devices using only aggregated data. This study will investigate the accuracy of this approach when applied to a heterogeneous virtual power plant (VPP). Firstly, a sensitivity analysis of the machine learning model is performed when varying the underlying device makeup of the VPP. Further, the forecasted flexible capacity of a heterogeneous residential VPP was applied to a trade strategy, which maintains a day ahead schedule, as well as offers flexibility to the imbalance market. This performance is then compared when using the same strategy with no capacity forecasting, as well as perfect knowledge. It was shown that at most, the highest average error, regardless of the VPP makeup, was still less than 9%. Further, when applying the forecasted capacity to a trading strategy, 89% of the optimal performance can be met. This resulted in a reduction of monthly costs by approximately 20%

A predictive control scheme for automated demand response mechanisms

The development of demand response mechanisms can provide a considerable option for the integration of renewable energy sources and the establishment of efficient generation and delivery of electrical power. The full potential of demand response can be significant, but its exploration still remains a challenge mainly due to the non-homogeneity and the distributed nature of energy resources. Recent advances in information and communication technologies create new opportunities for close to real-time adaptation of the demand for electricity to the actual system needs. However, there have been many different approaches in transforming this vision into practical applications. Herewith, a novel control scheme for automated demand response mechanisms is proposed based on the application of predictive control techniques. The proposed scheme supports the large-scale implementation of demand response programs, and captures the planning phase, the real-time operations, the verification of the energy and service provision, and the financial settlement