Smart Distribution: Multi-Agent System Based Distributed Controlfor Peer-to-Peer Microgrids

Control of distribution networks are facing significant challenges with the increasing penetration of distributed energy resources. The focus of this thesis is to develop active voltage control systems provided by photovoltaic (PV) and PV-battery inverters to mitigate or eliminate voltage problems of distribution networks. Various approaches that combine local and centralized voltage control techniques have been proposed in literature. These techniques suffer from different problems; centralized voltage control systems have poor reliability and scalability; and local voltage control systems suffer from degraded performance. To overcome the drawbacks of centralized and local voltage control systems, this thesis develops novel peer-to-peer-based grid voltage support functions (P2P-based GVSFs). Smart inverters equipped with P2P-based GVSFs interact with each other in a P2P fashion and form a distributed voltage control (DVC) system. Two novel methodologies are proposed in the thesis to design a P2Pbased DVC system. The first one is based on real-time distributed optimization, while the second one is based on offline robust optimization.

In the first methodology, two gossip-based decomposition techniques are developed: the fully distributed Dual Decomposition (DD) method and the Jacobi-Proximal Alternating Direction Method of Multipliers (JP-ADMM). The fully distributed DD algorithm allows for simple implementation, but in most cases requires large number of iterations to converge. Results show that this algorithm can be used to implement a DVC system with only few agents (e.g. 10 agents or less). The JP-ADMM algorithm requires more local computations and communication; yet it converges much faster than the DD algorithm. The JP-ADMM-based DVC algorithm with 50 agents, for example, needs around 1.25 minutes to converge, whereas for the same number of agents, the DD-based DVC algorithm needs around 59.4 minutes to converge. To experimentally validate the performance of the real-time optimization-based DVC system, the thesis develops a novel laboratory-based P2P voltage control testbed.

There are several challenges for the decomposition-based voltage control technique. First major issue is its vulnerability to control instability due to inappropriate control parameters tuning. Second, implementing a real-time DVC system using decomposition algorithms requires the GVSFs to solve a complex optimization problem every iteration (e.g. every 5 s). This motivates studying the simplification of the implementation of DVC systems. In the second methodology, the thesis simplifies the implementation of a DVC system and reduces its computational and communication burden by applying robust optimization to learn linear voltage control policies on a day-ahead basis. In doing so, the inverters communicate with each other in real-time and solve only a set of linear equations to control voltages in a distributed manner.

In the final phase of this work, the thesis combines policy-based DVC system with frequency containment reserve (FCR), to increase value from PV-battery systems. To this end, the thesis develops a robust mathematical optimization program that enables PV-battery systems to simultaneously provide FCR service (to their synchronous area), and voltage regulation service (to their distribution network).