The Value of Energy Flexibility: An Assessment of Residential Aggregator Services

With the transition of energy supply from controllable fossil fuel based generators to more sustainable options, aggregators are developing to be one of the new stakeholders to inject more flexibility in the new energy sector. An aggregator brings together residential, commercial and small industrial demand response (DR) and distributed generation, as a Virtual Power Plant (VPP), to actively participate in electricity markets and enter demand response programs offered by the operators of the energy system. Two main barriers for these aggregator services have been identified: Knowledge of VPP characteristics and determining the value of aggregator services. This leads to the main research question addressed in this dissertation: How can the value of aggregated flexibility be assessed? To assess the value of a VPPs' flexibility, large sets of data are required to adequately evaluate its potential. This was addressed by developing a set of device and user models which realistically simulate a residential VPP at a resolution of under a minute, configurable to cover most residential devices, capable of smart control, scalable to large numbers of devices and validated with measured device data. Using these models most types of residential VPPs can be realistically simulated. From here, to determine the value, the characteristics of a VPP must be quantified to determine which services a VPP is capable of offering. Barriers such as end user privacy and processing time, limit the use of low level, system specific, data. Therefore. a black box forecasting model to estimate the characteristics of a VPP, namely flexible capacity, was developed using only top level aggregated information. A sensitivity analysis of the artificial neural network (ANN) model was performed by investigating the impact of VPP makeup on its performance error. It was shown that for all VPPs the error was at most 10% confirming the versatility of the forecasting model for residential aggregators. Finally, a mathematical set-up to determine the value of a VPP’s flexible capacity on the wholesale energy markets was defined. Using this formulation, a methodology to assess internal VPP as well as external influences, such as market design and environmental factors, on the potential value is presented. Finally, by this methodology two trading algorithms were assessed to determine how to improve trading. It was shown that with the assessment the performance is improved by 36%.status: publishe

Multi-Goal Optimization of Competing Aggregators using a Web-of-Cells Approach

© 2017 IEEE. There is a trend to deviate from centrally optimized to localization of smart grid services. One new concept considered is the Web-of-Cells concept where cells are defined as a group of interconnected loads, generation plants, distributed energy resources and storage units corresponding to a physical section of the grid. Each cell will be responsible for monitoring, as well as activation capabilities for ancillary services. Further, exchanges of resources between connected cells is enabled to provide system wide optimization. This paper addresses the impact of this control architecture for local network congestion services while cells also participate on the wholesale energy markets. Two cells are realized by two independent lab setups: TNO's Hybrid Energy System Integration (HESI) Facility and University of Leuven's energy laboratory, EnergyVille. It is shown that the Web-of-Cells concept not only enables network stability by coordinating congestion efforts but also expands market trading capabilities therefore increasing overall system efficiency.status: publishe

Value Assessment of Aggregated Energy Flexibility when Traded on Multiple Markets

© 2017 IEEE. Aggregated demand response for smart grid services is a growing field of interest especially for market participation. A growing trend in research is to utilize aggregated demand response for multiple smart grid services. This could be multi-market trading (day ahead, intraday and imbalance) or a combination of market and ancillary services such offering reserve power or congestion management. However, there is a potential conflict of interest when offering the same resource for simultaneous services. This work investigates the impact, both from a monetary and network stability perspective, of applying a predictive control trading strategy which actively offers aggregated flexibility of electric vehicles on both the German EPEX day ahead and intraday markets. An artificial neural network was used to forecast the available ramp up and down capacity of a Virtual Power Plant (VPP) of 1000 electric vehicles. Using this information, the available flexibility is traded to ramp up in one quarter and down in the next depending on the price delta seen in the intraday market. A number of simulation runs are done, each with different levels of flexibility traded. In every run, one week of realistic VPP behaviour is simulated. The total earnings on the intraday market are calculated as well as imbalance cost and imbalance power generated over this period. It was seen that with an increased offer of available flexibility, there was an increase in both total revenues up to ∼4200 euros for one week of trade as well as imbalance generated, ∼1.6 MWh. Therefore, there is a clear need for effective regulation that limits imbalance without losing the future grid-stabilising effects of the flexibility aggregator.status: publishe

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

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. 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%.status: publishe

Applying Machine Learning Techniques for Forecasting Flexibility of Virtual Power Plants

© 2016 IEEE. Previous and existing evaluations of available flexibility using small device demand response have typically been done with detailed information of end-user systems. With these large numbers, having lower level information has both privacy and computational limitations. We propose a black box approach to investigating the longevity of aggregated response of a virtual power plant using historic bidding and aggregated behaviour with machine learning techniques. The two supervised machine learning techniques investigated and compared in this paper are, multivariate linear regression and single hidden layer artificial neural network (ANN). Both techniques are used to model a relationship between the aggregator portfolio state and requested ramp power to the longevity of the delivered flexibility. Using validated individual household models, a smart controlled aggregated virtual power plant is simulated. A hierarchical market-based supply-demand matching control mechanism is used to steer the heating devices in the virtual power plant. For both the training and validation set of clusters, a random number of households, between 200 and 2000, is generated with day ahead profile scaled accordingly. Further, a ramp power (power deviation) is assigned at various hours of the day and requested to hold for the remainder of the day. Using only the bidding functions and the requested ramp powers, the ramp longevity is estimated for a number of different cluster setups for both the artificial neural network as well as the multi-variant linear regression. It is found that it is possible to estimate the longevity of flexibility with machine learning. The linear regression algorithm is, on average, able to estimate the longevity with a 15% error. However, there was a significant improvement with the ANN algorithm achieving, on average, a 5.3% error. This is lowered 2.4% when learning for the same virtual power plant. With this information it would be possible to accurately offer residential VPP flexibility for market operations to safely avoid causing further imbalances and financial penalties.status: publishe

Predictive Control for Multi-Market Trade of Aggregated Demand Response using a Black Box Approach

© 2016 IEEE. 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%.status: publishe