Search for: All records

Distribution network safety should not be compromised when distributed energy resources (DERs) provide balancing services to the grid. Often DER coordination is achieved through an aggregator. Thus, it is necessary to develop network-safe coordination schemes between the distribution network operator (i.e., the utility) and the aggregator. In this work, we introduce a framework in which the utility computes and sends a constraint set on the aggregators’ control commands to the DERs. We propose a policy to adjust the charging/discharging power of distributed batteries, which allows them to be incorporated into the framework. Also, we propose a data-driven approach for the utility to construct a constraint set with probabilistic guarantees on network safety. The proposed approach allows the DERs to provide network- safe services without heavy communication requirements or invasion of privacy. Numerical simulations with distributed batteries and thermostatically controlled loads show that the proposed approach achieves the desired level of network safety and outperforms two benchmark algorithms. 

When providing bulk power system services, a third-party aggregator could inadvertently cause operational issues at the distribution level. We propose a coordination architecture in which an aggregator and distribution operator coordinate to avoid distribution network constraint violations, while preserving private information. The aggregator controls thermostatic loads to provide frequency regulation, while the distribution operator overrides the aggregator’s control actions when necessary to ensure safe network operation. Using this architecture, we propose two control strategies, which differ in terms of measurement and communication requirements, as well as model complexity and scalability. The first uses an aggregate model and blocking controller, while the second uses individual load models and a mode-count controller. Both outperform a benchmark strategy in terms of tracking accuracy. Furthermore, the second strategy performs better than the first, with only 0.10% average RMS error (compared to 0.70%). The second is also able to maintain safe operation of the distribution network while overriding less than 1% of the aggregator’s control actions (compared to approximately 15% by the first strategy). However, the second strategy has significantly more measurement, communication, and computational requirements, and therefore would be more complex and expensive to implement than the first strategy. 

Systems often face constraints at multiple levels. For example, in coordinating a collection of thermostatically controlled loads to provide grid services, the controller must ensure temperature constraints for each load (local constraints) and distribution network constraints (global constraints) are satisfied. In this paper, we leverage invariant sets to ensure safe coordination of systems with both local and global constraints. Specifically, we develop a method for constructing a controlled invariant set for a collection of subsystems, modeled as transition systems, to ensure they indefinitely satisfy the constraints, based on cycles in individual transition systems. Then, we develop a control algorithm that keeps the state inside the maximal controlled invariant set.We apply these algorithms to a demand response problem, specifically, the tracking of a power trajectory (e.g., a frequency regulation signal) by a population of homogeneous air conditioners. The algorithm simultaneously maintains local temperature requirements and aggregate power consumption limits, ensuring the control is nondisruptive to consumers and benign to the distribution network. 

This paper proposes a strategy to control a group of thermostatically controlled loads (TCLs) such that the variability in their aggregate load is reduced. This strategy could be deployed in areas of a distribution network that experience voltage excursions due to net load fluctuations, such as areas with high penetrations of photovoltaic (PV) generation and/or electric vehicles (EVs), We limit variation in the power consumption of a group of TCLs using a control strategy previously developed for large aggregations of switched systems. Using this strategy, we constrain the number of TCLs that are on (i.e., actively consuming power) between upper and lower bounds. In simulations, the control strategy successfully decreases the range over which TCL power consumption varies. Percent reductions in range are greatest for medium group sizes: we find a median reduction of 82% for groups of 50 TCLs, 74% for groups of 1000 TCLs, and 59% for groups of 5 TCLs. Reducing the variability of a distribution network's power injections helps to reduce voltage variability. In a simulation of a distribution line supplying 25 households, half with PV systems, the control strategy reduces the total range of voltage by 0.02 p.u. and prevents a violation of the 0.95 p.u. limit. Lastly, we propose a new control strategy for a more realistic TCL model that includes compressor lockout. The new strategy performs comparably to the original strategy and is demonstrated through simulation. 

Operational issues could arise on a distribution network if a third-party aggregator controls a large number of the network’s loads without visibility of network states or topology. This paper investigates mechanisms by which an aggregator could coordinate with a distribution network operator such that aggregate load actions do not negatively impact the network. We propose two possible frameworks for coordination and develop a specific coordination scheme in which the operator can partially block the aggregator’s control inputs to loads. We design a control strategy for the aggregator assuming it has little to no information about how the operator is blocking its control. The proposed controller estimates the number of loads that are not receiving the aggregator’s commands and compensates accordingly. In a simulation study, the proposed controller consistently outperforms a benchmark controller in terms of average tracking error.