Energy storage power and energy sizing and specification using HSSPFC

The intermittent nature of renewable sources requires the integration of Energy Storage Systems (ESSs) with appropriate power and energy densities. One of the applications of Hamiltonian Surface Shaping and Power Flow Control (HSSPFC) is to size ESSs for power and energy densities by employing them as sole actuators of Microgrid (MG) systems. This Article provides a comprehensive yet simplified example of utilization of HSSPFC to size ESSs of inverter-based three-phase MG systems under hierarchical control. Here, the distributed Hamiltonian controller is expanded for control of parallel ESSs and power sharing metrics are defined to distribute power between hybrid storage systems according to their power and energy density capabilities. Simulated hybrid ESSs comprising battery and flywheel systems are used as examples to demonstrate the behaviour of the expanded control, verify the power sharing criteria and illustrate ESS design and specification by utilizing HSSPFC

Collective systems:physical and information exergies.

Collective systems are typically defined as a group of agents (physical and/or cyber) that work together to produce a collective behavior with a value greater than the sum of the individual parts. This amplification or synergy can be harnessed by solving an inverse problem via an information-flow/communications grid: given a desired macroscopic/collective behavior find the required microscopic/individual behavior of each agent and the required communications grid. The goal of this report is to describe the fundamental nature of the Hamiltonian function in the design of collective systems (solve the inverse problem) and the connections between and values of physical and information exergies intrinsic to collective systems. In particular, physical and information exergies are shown to be equivalent based on thermodynamics and Hamiltonian mechanics

Integration and optimal control of microcsp with building hvac systems: Review and future directions

Heating, ventilation, and air-conditioning (HVAC) systems are omnipresent in modern buildings and are responsible for a considerable share of consumed energy and the electricity bill in buildings. On the other hand, solar energy is abundant and could be used to support the building HVAC system through cogeneration of electricity and heat. Micro-scale concentrated solar power (MicroCSP) is a propitious solution for such applications that can be integrated into the building HVAC system to optimally provide both electricity and heat, on-demand via application of optimal control techniques. The use of thermal energy storage (TES) in MicroCSP adds dispatching capabilities to the MicroCSP energy production that will assist in optimal energy management in buildings. This work presents a review of the existing contributions on the combination of MicroCSP and HVAC systems in buildings and how it compares to other thermal-assisted HVAC applications. Different topologies and architectures for the integration of MicroCSP and building HVAC systems are proposed, and the components of standard MicroCSP systems with their control-oriented models are explained. Furthermore, this paper details the different control strategies to optimally manage the energy flow, both electrical and thermal, from the solar field to the building HVAC system to minimize energy consumption and/or operational cost

Nonlinear controller for nonlinear wave energy converters

The present invention is directed to a nonlinear controller for nonlinear wave energy converters (WECs). As an example of the invention, a nonlinear dynamic model is developed for a geometrically right-circular cylinder WEC design for the heave-only motion, or a single degree-of-freedom (DOF). The linear stiffness term is replaced by a nonlinear cubic hardening spring term to demonstrate the performance of a nonlinear WEC as compared to an optimized linear WEC. By exploiting the nonlinear physics in the nonlinear controller, equivalent power and energy capture, as well as simplified operational performance is observed for the nonlinear cubic hardening spring controller when compared to an optimized linear controller.https://digitalcommons.mtu.edu/patents/1151/thumbnail.jp

Optimal and decentralized control strategies for inverter-based AC microgrids

This paper presents two control strategies: (i) An optimal exergy destruction (OXD) controller and (ii) a decentralized power apportionment (DPA) controller. The OXD controller is an analytical, closed-loop optimal feedforward controller developed utilizing exergy analysis to minimize exergy destruction in an AC inverter microgrid. The OXD controller requires a star or fully connected topology, whereas the DPA operates with no communication among the inverters. The DPA presents a viable alternative to conventional P−ω/Q−V droop control, and does not suffer from fluctuations in bus frequency or steady-state voltage while taking advantage of distributed storage assets necessary for the high penetration of renewable sources. The performances of OXD-, DPA-, and P−ω/Q−V droop-controlled microgrids are compared by simulation

Multi-resonant feedback control of a single degree-of-freedom wave energy converter

A multi-resonant wide band controller decomposes the wave energy converter control problem into sub-problems; an independent single-frequency controller is used for each sub-problem. Thus, each sub-problem controller can be optimized independently. The feedback control enables actual time-domain realization of multi-frequency complex conjugate control. The feedback strategy requires only measurements of the buoy position and velocity. No knowledge of excitation force, wave measurements, nor wave prediction is needed. As an example, the feedback signal processing can be carried out using Fast Fourier Transform with Hanning windows and optimization of amplitudes and phases. Given that the output signal is decomposed into individual frequencies, the implementation of the control is very simple, yet generates energy similar to the complex conjugate control.https://digitalcommons.mtu.edu/patents/1148/thumbnail.jp

Extending complex conjugate control to nonlinear wave energy converters

This paper extends the concept of Complex Conjugate Control (CCC) of linear wave energy converters (WECs) to nonlinear WECs by designing optimal limit cycles with Hamiltonian Surface Shaping and Power Flow Control (HSSPFC). It will be shown that CCC for a regular wave is equivalent to a power factor of one in electrical power networks, equivalent to mechanical resonance in a mass-spring-damper (MSD) system, and equivalent to a linear limit cycle constrained to a Hamiltonian surface defined in HSSPFC. Specifically, the optimal linear limit cycle is defined as a second-order center in the phase plane projection of the constant energy orbit across the Hamiltonian surface. This concept of CCC described by a linear limit cycle constrained to a Hamiltonian surface will be extended to nonlinear limit cycles constrained to a Hamiltonian surface for maximum energy harvesting by the nonlinear WEC. The case studies presented confirm increased energy harvesting which utilizes nonlinear geometry realization for reactive power generation

Military airborne and maritime application for cooperative behaviors.

As part of DARPA's Software for Distributed Robotics Program within the Information Processing Technologies Office (IPTO), Sandia National Laboratories was tasked with identifying military airborne and maritime missions that require cooperative behaviors as well as identifying generic collective behaviors and performance metrics for these missions. This report documents this study. A prioritized list of general military missions applicable to land, air, and sea has been identified. From the top eight missions, nine generic reusable cooperative behaviors have been defined. A common mathematical framework for cooperative controls has been developed and applied to several of the behaviors. The framework is based on optimization principles and has provably convergent properties. A three-step optimization process is used to develop the decentralized control law that minimizes the behavior's performance index. A connective stability analysis is then performed to determine constraints on the communication sample period and the local control gains. Finally, the communication sample period for four different network protocols is evaluated based on the network graph, which changes throughout the task. Using this mathematical framework, two metrics for evaluating these behaviors are defined. The first metric is the residual error in the global performance index that is used to create the behavior. The second metric is communication sample period between robots, which affects the overall time required for the behavior to reach its goal state