A Two-Layer Decentralized Control Architecture for DER Coordination

Abstract

This paper presents a two-layer distributed energy resource (DER) coordination architecture that allows for separate ownership of data, operates with data subjected to a large buffering delay, and employs a new measure of power quality. The two-layer architecture comprises a centralized model predictive controller (MPC) and several decentralized MPCs each operating independently with no direct communication between them and with infrequent communication with the centralized controller. The goal is to minimize a combination of total energy cost and a measure of power quality while obeying cyberphysical constraints. The global controller utilizes a fast AC optimal power flow (OPF) solver and extensive parallelization to scale the solution to large networks. Each local controller attempts to maximize arbitrage profit while following the load profile and constraints dictated by the global controller. Extensive simulations are performed for two distribution networks under a wide variety of possible storage and solar penetrations enabled by the controller speed. The simulations show that (i) the two-layer architecture can achieve tenfold improvement in power quality relative to no coordination, while capturing nearly all of the available arbitrage profit for a moderate amount of storage penetration, and (ii) both power quality and arbitrage profits are optimized when the solar and storage are distributed more widely over the network, hence it is more effective to install storage closer to the consumer.

Energy control of HVAC units for provable ancillary service provision

“…This information along with the nominal power consumption of HVAC unit assumed to be P rated = 1.7KW can be plugged in Eqn. (19) to solve for the unknown parameter. The resulting values are found to be R = 7.9926K/J and C = 0.1242J/K Utilizing these estimated parameters, the model in Eqn.…”

“…Thus indirect load control method results in deviations from the power schedules, especially when the cluster of loads is not diverse enough or if a significant fraction of them are operating at their limits [13], [14]. Some other control methods combining these different methods include passivity-based distributed optimization [15], [16], [17], multistage robust optimization considering detailed HVAC models [18], two layered-control distributing the decision-making between local and global controllers [19]. However, these approaches do not consider the practical limitations of the controllers or the availability of sensor measurements.…”

Unified value-based feedback, optimization and risk management in complex electric energy systems

“…For instance, the protocols for control design to ensure stable operation of smaller electric power grids, capable of operating autonomously, also referred to as microgrids, have been studied in [22][23][24][25][26][27][28][29]. The distribution grids coordinating millions of small KW-level devices have been studied in the context of quantifying the aggregate flexibility [30][31][32][33][34].…”

“…The performance of this is however contingent upon the law of large numbers [30,31]. A few other studies in the literature have relied on utilizing fast expensive batteries to ensure aggregate performance by also considering uncertainties in retail market prices [32,72,73]. A compara-tive analysis of demand response utilizing stochastic and robust optimization methods have been done in [74] by considering price uncertainty.…”

Energy Resource Control via Privacy Preserving Data

“…However, the push for renewable energy sources has brought about the rise of distributed energy resources (DERs) that lie under the control of many smaller and disparate users causing a paradigm shift in the flow of information in the grid. The successful operation of DERs and other smart grid technologies depends on the exchange of large amounts of data from many different end users [1]- [3]. Unfortunately, it may be unrealistic to assume the data will be available without consideration of the privacy concerns the data owners may face.…”