Christos D. Korkas

Optimization methods evaluation for the design of radial flux surface PMSM

Conventional high torque low speed drive systems commonly have a mechanical transmission between the induction motor and the load consisting of gears, gear heads, belt/pulleys or camshafts. The main drawbacks of such setups, are the deficiency of the drive system, its high cost and the maintenance needs. An alternative to this, is the replacement of the induction motor and its mechanical transmission elements with a permanent magnet (PM) synchronous motor (PMSM) directly coupled to the load running at low speed. In this context, the paper deals with the design evaluation of a 5kW 50rpm motor and concentrates on two radial flux promising topologies i.e. with surface-mounted permanent magnets with inner and outer rotor. Since the goal is mainly the minimization of the machines active weight (with respect to constraints related to outer dimensions, efficiency and cost), the designs of the PM machines are conducted by solving an optimization problem. Three optimization methods are adopted and three weighted cost functions are presented. The effectiveness of the methods in finding competitive alternative PMSM designs is then evaluated. The presented results are compared with other found in literature and reveal satisfactorily enhanced design solutions and performance of the proposed methods.

Multi-objective control strategy for energy management of grid-connected heterogeneous microgrids

The demand-side energy management of microgrids comprising of buildings of heterogeneous nature (residential, commercial, industrial, etc.) and thus exhibiting heterogeneous occupancy pattern, requires the development of appropriate energy management systems (EMSs) that can integrate the maximum exploitation of the distributed energy resources like photovoltaic panels with the thermal comfort of the occupants. This paper presents a simulation-based optimization approach for the design of an EMS in grid-connected photovoltaic-equipped microgrids with heterogeneous buildings and occupancy schedules. The EMS optimizes a multi-objective criterion that takes into account both the energy cost and the thermal comfort of the aggregate microgrid. A three-building microgrid test case is used to demonstrate the effectiveness of the proposed approach: comparisons with alternative rule-based and optimization-based EMSs show that the proposed EMS strategy exploits the occupancy information to automatically change the energy demand of each building, resulting in improved energy cost and thermal comfort.

A supervisory approach to microgrid demand response and climate control

Microgrids equipped with small-scale renewable-energy generation systems and energy storage units offer challenging opportunity from a control point of view. In fact, in order to improve resilience and enable islanded mode, micro-grid energy management systems must dynamically manage controllable loads by considering not only matching energy generation and consumption, but also thermal comfort of the occupants. Thermal comfort, which is often neglected or oversimplified, plays a major role in dynamic demand response, especially in front of intermittent behavior of the renewable energy sources. This paper presents a novel control algorithm for joint demand response management and thermal comfort optimization in a microgrid composed of a block of buildings, a photovoltaic array, a wind turbine, and an energy storage unit. In order to address the large-scale nature of the problem, the proposed control strategy adopt a two-level supervisory strategy: at the lower level, each building employs a local controller that processes only local measurements; at the upper level, a centralized unit supervises and updates the three controllers with the aim of minimizing the aggregate energy cost and thermal discomfort of the microgrid. Comparisons with alternative strategies reveal that the proposed supervisory strategy efficiently manages the demand response so as to sensibly improve independence of the microgrid with respect to the main grid, and guarantees at the same time thermal comfort of the occupants.

Local4Global Adaptive Optimization and control for System-of-Systems

Over the recent past years research effort has been dedicated towards addressing a generic solution in System of Systems (SoS) control problems. Two are the main obstacles that have to be bypassed in such problem cases, especially in real life applications, rendering the optimization problem into a complicated/challenging one: (i) modelling/simulation tools are usually used in order to construct an as-close-as-possible to reality accurate model, whose construction though requires a considerable amount of time and effort and (ii) furthermore, standard control system designs when applied to SoS exhibit poor performance as they are required to handle very high-dimensional problems. In this paper, we present a first attempt towards addressing these issues. More precisely, a new adaptive optimal control methodology is presented and evaluated. The main attributes of the proposed control methodology is its local nature with minimum requirements for coordination between the constituent system of the SoS and its model-free nature.

Grid-Connected Microgrids: Demand Management via Distributed Control and Human-in-the-Loop Optimization

Abstract Attaining energy-efficiency in microgrids, a localized grouping of controllable loads with distributed energy resources, requires the development of appropriate feedback-based demand management systems (DMS) with the capability of controlling thermostatically controlled loads at the district level, so as to optimize the aggregate energy demand of the microgrid. Since the energy demand is mainly driven by human needs (i.e., human-in-the-loop thermal comfort) and weather conditions, the DMS feedback nature is necessary to exploit occupancy and weather information that might change in real-time as the microgrid is operating. In this work, we aim at creating a distributed DMS (D-DMS) whose crucial characteristics are: the capability of augmenting any rule-based DMS with a feedback action that improves performance in changing (weather or occupancy) conditions; a distributed intelligence monitoring logic to scale up the benefits of single-building DMS (S-DMS) up to district-level microgrids. An original test case, developed in EnergyPlus and composed of a microgrid district with 100 buildings with thermostatically controlled loads to be managed, is presented to assess the performance of the proposed strategy.

An Adaptive Learning-Based Approach for Nearly Optimal Dynamic Charging of Electric Vehicle Fleets

Managing grid-connected charging stations for fleets of electric vehicles leads to an optimal control problem where user preferences must be met with minimum energy costs (e.g., by exploiting lower electricity prices through the day, renewable energy production, and stored energy of parked vehicles). Instead of state-of-the-art charging scheduling based on open-loop strategies that explicitly depend on initial operating conditions, this paper proposes an approximate dynamic programming feedback-based optimization method with continuous state space and action space, where the feedback action guarantees uniformity with respect to initial operating conditions, while price variations in the electricity and available solar energy are handled automatically in the optimization. The resulting control action is a multi-modal feedback, which is shown to handle a wide range of operating regimes, via a set of controllers whose action that can be activated or deactivated depending on availability of solar energy and pricing model. Extensive simulations via a charging test case demonstrate the effectiveness of the approach.

Automatically fine-tuned speed control system for fuel and travel-time efficiency: A microscopic simulation case study

Within the current document a model independent, cognitive and adaptive optimization mechanism, namely CAO, is adopted for providing efficient speed/torque control actions. However since real-life tests were not feasible, a simulation model of a 1.4lt displacement gasoline car, playing the role of the actual car, was adopted while the potential maximum cruising speed levels are chosen so as to emulate a usual suburban route and the vehicle speed control is replicated by a common PID scheme. The control decisions are applied directly to the car throttle/torque pedal itself. The goal of the optimization application was to minimize the vehicle velocity error, while minimizing the total fuel consumption for a certain 10km trip with varying road angles/slopes. It should be noted that CAO module could be applied directly to a car system in a straightforward manner without any preparatory investigations. Initially PID gain values were selected arbitrary while a PID gain — tuning module from Matlab/Simulink was used in order to fine-tune them under certain road conditions. Finally the tuned values were used as initial ones for all CAOs application cases (A, B, C and D). CAO presented substantial improvements in the specified performance index, with respect to the base case speed strategy, as well as to the PID tuning in all simulation scenarios considered.

Adaptive optimization for smart operation of cyber-physical systems: A thermostatic zoning test case

In the current state of the art load management and demand response actions in smart buildings are often predetermined by a field engineer to a fixed set of (rule-based) options. This fixed set of options often neglects the cyber-physical nature of the building dynamics, thermostatic action and building automation system. In this work we will combine a rule-based load management program with a learning feedback load management program that can operate on top of the rules. We demonstrate via extensive simulations the effectiveness of the program for intelligent management of the heating, ventilating and air conditioning (HVAC) loads so as to exploit renewable energy sources, while taking into account human-related constraints like thermal comfort.

Supporting Decision Making for Large-Scale IoTs: Trading Accuracy with Computational Complexity

As systems continue to evolve they rely less on human decision-making and more on computational intelligence. This trend in conjunction to the available technologies for providing advanced sensing, measurement, process control, and communication lead towards the new field of Internet-of-Things (IoT). IoT systems are expected to play a major role in the design and development of future engineering platforms with new capabilities that far exceed today’s levels of autonomy, functionality, and usability. Although these systems exhibit remarkable characteristics, their design and implementation is a challenging issue, as numerous (heterogeneous) components and services have to be appropriately designed. The problem of designing efficient IoT becomes far more challenging in case the target system has to meet also timing constraints. This chapter discusses an advanced framework for implementing decision-making mechanisms for large-scale IoT platforms. In order to depict the efficiency of introduced framework, it was applied to customize the building’s cooling and heating in a smart-grid environment. For this purpose, a number of connected smart thermostats are employed, which should facilitate intelligent control to fulfill occupants’ needs, such as the energy consumption and the comfort level in a building environment. Towards this direction, appropriate mechanisms that enable smart thermostats to have the capability to monitor their own performance, to classify, to learn, and to take proper actions, were developed in a systematic way. Experimentation with various configuration setups highlights the superior of introduced solution compared to static temperature values, as well as existing control techniques. Additionally, the significant low computational complexity enables the sufficient implementation of this mechanism as part of a low-cost embedded system, which can be integrated into existing smart thermostats.