This dissertation studies power control of microgrids utilizing multi-agent systems (MASs) concept and technology. The major focus of this work is in addressing proportional power sharing of inverter-based Distributed Generators (DGs) in AC microgrids under variations in maximum power capacity of the DGs. A microgrid can include renewable energy resources such as wind turbines, solar panels, fuel cells, etc. The intermittent nature of such energy resources causes variations in their maximum power capacities. DGs are cyber-physical systems which can be regarded as multi-agent systems. Considering these factors, a consensus algorithm is designed to have the DGs generate their output power in proportion to their maximum capacities under capacity fluctuations. A change in power capacity of a DG triggers the consensus algorithm which uses a communication map at the cyber layer to estimate the corresponding change in a distributed manner. Convergence rate of the algorithm is analytically established and bounds on allowable capacity fluctuations are derived based on practical constraints. During the transient time of reaching a consensus, the delivered power may not match the load power demand. To eliminate this mismatch, a control law is augmented that consists of a finite-time consensus algorithm embedded within the overarching power sharing consensus algorithm. The effectiveness of the distributed controller is assessed through simulation of a microgrid consisting of a realistic model of inverter-based DGs. Details of the microgrid model, its controller structures, and a comprehensive list of parameters are provided. Next, the problem of load power fluctuations in DC microgrids is considered and the consensus algorithms developed for the AC microgrids is extended to this scenario. Here, the DGs reach a consensus on the load power under fluctuations, leading to a distributed tracking algorithm. Another aspect of power control in microgrids is the latency or delays of some renewable energy resources such as fuel-cells, in response to changes in the load demand which results in load and power generation mismatch. To address this issue Energy Storage Systems (ESSs) are incorporated in microgrids. To this end, we systematically study shaping the transient step response of nonlinear systems to satisfy a class of integral constraints. Such constraints are inherent in hybrid energy systems consisting of energy sources and storage elements. While typical transient specifications aim to minimize overshoot, this problem is unique in that it requires the presence of an appreciable overshoot to satisfy the foregoing constraints. The problem was previously studied in the context of linear systems and this dissertation extends that work to nonlinear systems. A combined integral and feedforward control, that requires minimal knowledge of the plant model, is shown to make the system amenable to meeting such constraints. Broadly, the compensation is effective for nonlinear plants with stable open-loop step response and a positive DC gain. However, stability of the resulting closed-loop system mandates bounds on the integral gain. In this regard, we state and prove generalized stability theorems for first and higher-order nonlinear plants. Finally, the proposed compensation is applied on a realistic DG using MATLAB Simscape toolbox.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Mechanical and Aerospace Engineering
Length of Campus-only Access
Doctoral Dissertation (Campus-only Access)
Aalipour Hafshejani, Farzad, "Power Sharing Control of Microgrids Using Multi-Agent Systems Methodology" (2021). Electronic Theses and Dissertations, 2020-. 628.
Restricted to the UCF community until August 2024; it will then be open access.