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

Abstract

Abstract: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%.

Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment

“…A flexible load is one that can be adjusted in response to changes in the time of day, the weather, and the smart-grid control signal [99]. Studies have estimated the DR heat-load flexibility of homogeneous [100], [101], [102] and heterogeneous [103] VPP heat-load clusters. Both the DA market and other energy markets are open to trading the estimated flexibility [100], [103].…”

Lessons From 10 Years of Demand Response Research: Smart Energy for Customers?

“…Longevity is not only an issue with boilers, but with all types of flexible devices. MacDougall et al provide a detailed analysis of the longevity of heat pumps for residential use [5]. Additionally, when flexibility has been used, there might be a certain time-period required to recover the flexibility (e.g.…”