Aggregation of thermostatically controlled loads for flexibility markets

This paper presents a tool for an aggregator of thermostatically controlled loads (TCLs) to optimally combine their flexibilities into a few representative bids to be submitted to flexibility markets. The tool employs a “bottom-up” approach based on physical end-use load models, being the individual flexibility of each individual TCL simulated with a second-order thermal model describing the dynamics of the house. The approach is based on a direct load control (DLC) of thermostat temperature set-point by the aggregator. End-users receive an economic compensation in exchange for the loss of comfort. The applicability of the proposed model is demonstrated in a simulation case study based on an actual power system in Spain.The research leading to this publication has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 691405

Real-Time Flexibility Market Participation of Thermostatically Controlled Loads

The objective of this paper is to demonstrate the feasibility of using the aggregated flexibility of thermostatically controlled loads (TCLs) to provide balancing and congestion management services to system operators through the participation in a real-time flexibility market. To this aim, a TCL aggregation model that employs a bottom-up approach based on physical end-use load models has been developed. A direct load control (DLC) scheme is considered, where the control variable is the thermostat temperature setpoint. This temperature can be manipulated between the upper and lower limits set by end-users, who receive an economic compensation in exchange for the loss of comfort. As output a set of flexibility bids to be sent to the market are obtained. To demonstrate the applicability of the proposed aggregation model and estimate the overall flexibility potential from TCLs, a large-scale case study, based on a future power system in Spain has been considered.H2020, 824414, CoordiNe

Cluster Control of Heterogeneous Thermostatically Controlled Loads Using Tracer Devices

Managing the aggregated demand of large heterogeneous clusters of thermostatically controlled loads (TCLs) is considered a sequential decision-making problem under uncertainty. Recent research indicates that using reduced-order models in combination with a broadcasted control signal offers a viable solution to the tradeoff between computational feasibility, and accurately describing the steady-state and transient cluster response. In this paper, we propose a novel control strategy based on tracer devices, which we define as a limited amount of virtual TCLs that represent the entire cluster of heterogeneous TCLs. These second-order model devices are identified in a nonintrusive manner, and capture both steady-state and transient population dynamics, as well as cluster heterogeneity. Additionally, the dispatch mechanism is included in the optimization, further improving the tracking performance. The parameterizable number of tracer devices enables a covering of the tradeoff domain. Both approaches have been evaluated in two scenarios. In the first small-scale scenario, improvements in price and power deviations are evaluated when using increasing numbers of tracer devices and integrating the dispatch dynamics. Results from the second large-scale scenario show that root mean square dispatch errors can be reduced by more than 10% when integrating the dispatch mechanism in the resulting high-fidelity model.status: publishe

Sequential set-point control of thermostatic loads using extended Markov chain abstraction to improve future renewable energy integration

Additional flexible resources are required to achieve resilience and sustainable power systems. Challenges emerged due to the increasing amounts of renewable generation penetrations at both the bulk power system and the distribution sides. System operators are required to deal with higher levels of variable and uncertain power outputs for various time-scales. Moreover, replacing existing thermal units with other inertial-less technologies, make the system sensitive to even small contingencies. Demand-side control is becoming an ingredient part of our future power system operation. Effective utilization of demand-side resources can make the system more elastic to integrate the future renewable plans. To help in resolving these challenges, this work develops a demand-side control framework on the Thermostatically Controlled Loads (TCLs) to support the grid with minimal impacts on customers\u27 comfort and devices\u27 integrity. The Markov chain abstraction method is used to aggregate the TCLs and describe their collective dynamics. Statistical learning techniques of hidden Markov chain analysis is used to identify the parameters of the resulting Markov chains at fixed temperature set-points. Various sensitivities are conducted to reveal the optimal Markov chain representation. To allow extracting or storing additional thermal energy, this thesis develops an Extended Markov Model(EMM) which describes devices\u27 transition when a new set-point is instructed. The results have shown that the EMM is able to capture both devices\u27 transient and steady-state behaviors under small and large set-point adjustments. Parameters heterogeneity affects the accuracy of the EMM model. In contrast to what proposed in the literature, more comprehensive heterogeneous parameters are defined and considered. The K-mean clustering approach is proposed in our analysis to minimize the heterogeneity error. Devices are divided into multiple clusters based on the power ratings and cycling characteristics. The results have shown that clustering highly improves the EMM performance and minimize the heterogeneity errors. Under temperature set-point control the TCLs\u27 aggregated power experience two main challenges before it converges to the new steady-state value, the abrupt load change, and the power oscillations. This is due to devices\u27 synchronous operations once a new operating set-point is ordered. Such power profiles may cause serious stability issues. Therefore, Model Predictive Control (MPC) with direct ON/OFF switching capability is proposed to apply the set-point control sequentially and prevent any possible power oscillations. The MPC can determine the optimal devices\u27 flow toward the new operating set-point. The results have shown that the proposed modeling and control approaches highly minimize the required switching actions. Control actions are required only during the transition between the set-points and finally converges to zero when all devices reach the new set-point setting. In contrast, the models proposed in the literature require very high switching rates which can cause damage or reducing devices\u27 life expectancy. The last part of this thesis proposes a dispatching framework to utilize the TCLs\u27 flexibility. The developed modeling and control techniques are used to support the grid with three demand response ancillary services. Namely, spinning reserves, load reduction, and load shifting. The three ancillary services are designed as demand response programs and integrated into the Security Constrained Unit Commitment (SCUC) Problem. Three participation scenarios are considered to evaluate the benefits of aggregating the TCLs in the day-ahead markets

Energy Flexibility from Large Prosumers to Support Distribution System Operation:A technical and legal case study on the Amsterdam ArenA stadium

To deal with the rising integration of stochastic renewables and energy intensive distributed energy resources (DER) to the electricity network, alternatives to expensive network reinforcements are increasingly needed. An alternative solution often under consideration is integrating flexibility from the consumer side to system management. However, such a solution needs to be contemplated from different angles before it can be implemented in practice. To this end, this article considers a case study of the Amsterdam ArenA stadium and its surrounding network where flexibility is expected to be available to support the network in the future. The article studies the technical aspects of using this flexibility to determine to what extent, despite the different, orthogonal goals, the available flexibility can be used by various stakeholders in scenarios with a large load from electric vehicle charging points. Furthermore, a legal study is performed to determine the feasibility of the technical solutions proposed by analysing current European Union (EU) and Dutch law and focusing on the current agreements existing between the parties involved. The article shows that flexibility in the network provided by Amsterdam ArenA is able to significantly increase the number of charging points the network can accommodate. Nonetheless, while several uses of flexibility are feasible under current law, the use of flexibility provided by electric vehicles specifically faces several legal challenges in current arrangements