Decentralized sliding mode control and estimation for large-scale systems

This thesis concerns the development of an approach of decentralised robust control and estimation for large scale systems (LSSs) using robust sliding mode control (SMC) and sliding mode observers (SMO) theory based on a linear matrix inequality (LMI) approach. A complete theory of decentralized first order sliding mode theory is developed. The main developments proposed in this thesis are: The novel development of an LMI approach to decentralized state feedback SMC. The proposed strategy has good ability in combination with other robust methods to fulfill specific performance and robustness requirements. The development of output based SMC for large scale systems (LSSs). Three types of novel decentralized output feedback SMC methods have been developed using LMI design tools. In contrast to more conventional approaches to SMC design the use of some complicated transformations have been obviated. A decentralized approach to SMO theory has been developed focused on the Walcott-Żak SMO combined with LMI tools. A derivation for bounds applicable to the estimation error for decentralized systems has been given that involves unknown subsystem interactions and modeling uncertainty. Strategies for both actuator and sensor fault estimation using decentralized SMO are discussed.The thesis also provides a case study of the SMC and SMO concepts applied to a non-linear annealing furnace system modelderived from a distributed parameter (partial differential equation) thermal system. The study commences with a lumped system decentralised representation of the furnace derived from the partial differential equations. The SMO and SMC methods derived in the thesis are applied to this lumped parameter furnace model. Results are given demonstrating the validity of the methods proposed and showing a good potential for a valuable practical implementation of fault tolerant control based on furnace temperature sensor faults

Investigation of Distributed Model Predictive Control for Economic Load Shifting in Building HVAC Systems

One of the major challenges that building owners and operators face is maintaining a low cost of operation. In certain markets within the U.S., electrical cost varies throughout the day; it is higher during times of peak demand. This leaves the customer the incentive to cut back electrical use during peak demand periods. Since 40% of the peak electrical demand is due to the operation of the building HVAC system alone, the opportunity exists for shifting the building cooling load to off-peak hours. This can be done by pre-cooling the space, thereby using the building mass as a sort of thermal battery, which can then discharge later, alleviating the cooling load off the HVAC system during peak times. It is in this thesis that a peak load reduction strategy is presented using model predictive control (MPC). Furthermore, the system modeled in this paper is a two-zone system, each having a dedicated controller. First the problem is explored with a single, centralized MPC which calculates the optimal trajectory for the entire building. Secondly, the load reduction strategy control is distributed to each individual controller. The advantage to distributed control is the reduction of computing resources which brings with it a cost reduction on its own. Lastly, both MPC approaches are compared to the traditional PI-only control scheme. Results showed that the distributed scheme proved favorable next to the centralized MPC benchmark, and both MPC approaches produced favorable results over the traditional PI-only control

Control in distribution networks with demand side management

The way in which electricity networks operate is going through a period of significant change. Renewable generation technologies are having a growing presence and increasing penetrations of generation that are being connected at distribution level. Unfortunately, a renewable energy source is most of the time intermittent and needs to be forecasted. Current trends in Smart grids foresee the accommodation of a variety of distributed generation sources including intermittent renewable sources. It is also expected that smart grids will include demand management resources, widespread communications and control technologies required to use demand response are needed to help the maintenance in supply-demand balance in electricity systems. Consequently, smart household appliances with controllable loads will be likely a common presence in our homes. Thus, new control techniques are requested to manage the loads and achieve all the potential energy present in intermittent energy sources. This thesis is focused on the development of a demand side management control method in a distributed network, aiming the creation of greater flexibility in demand and better ease the integration of renewable technologies. In particular, this work presents a novel multi-agent model-based predictive control method to manage distributed energy systems from the demand side, in presence of limited energy sources with fluctuating output and with energy storage in house-hold or car batteries. Specifically, here is presented a solution for thermal comfort which manages a limited shared energy resource via a demand side management perspective, using an integrated approach which also involves a power price auction and an appliance loads allocation scheme. The control is applied individually to a set of Thermal Control Areas, demand units, where the objective is to minimize the energy usage and not exceed the limited and shared energy resource, while simultaneously indoor temperatures are maintained within a comfort frame. Thermal Control Areas are overall thermodynamically connected in the distributed environment and also coupled by energy related constraints. The energy split is performed based on a fixed sequential order established from a previous completed auction wherein the bids are made by each Thermal Control Area, acting as demand side management agents, based on the daily energy price. The developed solutions are explained with algorithms and are applied to different scenarios, being the results explanatory of the benefits of the proposed approaches

A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems

This paper addresses the challenge of minimizing training time for the control of Heating, Ventilation, and Air-conditioning (HVAC) systems with online Reinforcement Learning (RL). This is done by developing a novel approach to Multi-Agent Reinforcement Learning (MARL) to HVAC systems. In this paper, the environment formed by the HVAC system is formulated as a Markov Game (MG) in a general sum setting. The MARL algorithm is designed in a decentralized structure, where only relevant states are shared between agents, and actions are shared in a sequence, which are sensible from a system’s point of view. The simulation environment is a domestic house located in Denmark and designed to resemble an average house. The heat source in the house is an air-to-water heat pump, and the HVAC system is an Underfloor Heating system (UFH). The house is subjected to weather changes from a data set collected in Copenhagen in 2006, spanning the entire year except for June, July, and August, where heat is not required. It is shown that: (1) When comparing Single Agent Reinforcement Learning (SARL) and MARL, training time can be reduced by 70% for a four temperature-zone UFH system, (2) the agent can learn and generalize over seasons, (3) the cost of heating can be reduced by 19% or the equivalent to 750 kWh of electric energy per year for an average Danish domestic house compared to a traditional control method, and (4) oscillations in the room temperature can be reduced by 40% when comparing the RL control methods with a traditional control method

Scaling energy management in buildings with artificial intelligence

L'abstract è presente nell'allegato / the abstract is in the attachmen

Automatic Mode Switching for A Multi-functional Variable Refrigerant Flow System

Multi-functional variable refrigerant flow system (MFVRF) is designed to realize simultaneous heating and cooling for individual zones. It is desirable to use existing measurements to determine switching between different modes under changes of ambient and load conditions, i.e. reversing the mode of indoor unit (IDU) and/or outdoor unit (ODU) heat exchangers (HX), as well as the bumpless transfer for controller switching. In this study, a set of mode switching logic is proposed for a four-zone MFVRF system, which involves both IDU and ODU mode switching actions. For the ODU-HX mode switching, thermodynamic analysis under different load changes reveals the qualitative trend for the air-side and refrigerant-side characteristics as the operation approaches to marginal scenarios. The ODU mode switching is thus based on the air-side temperature difference. Mode switching involving IDU action only is studied with a 1H3C (one heating three cooling) mode, in which IDU-1 is in heating mode and IDU-2, IDU-3 and IDU-4 are in cooling mode. For a given zone load conditions, when the zone temperature of IDU is higher than upper limit of a preset cooling mode hysteresis band, IDU enters the cooling mode by simultaneously opening all related cooling mode valves and closing heating-mode valves within time duration. On the other hand, the cooling mode is turned off by closing all related cooling valves when the zone temperature is lower than the lower limit of the cooling mode temperature band. Similarly, when the zone temperature is lower than lower limit of heating mode temperature band, IDU enters its heating mode. When the zone temperature for IDU is higher than the upper limit of heating mode temperature band, the heating mode is turned off. For ODU Mode Switching, it is proposed in this paper to use the temperature difference between the inlet and outlet air of the ODU HX. To justify the use of ODU air-side temperature differential as the indicator variable for ODU mode switching, several cases of 2H2C (two-heating two-cooling) mode are first simulated, in which the IDU-1 and IDU-2 are operated in heating mode and IDU-3 and IDU-4 are operated in cooling mode. A negative ramp of load change applied to IDU-3 within 1000 seconds. For the ODU-HX, the air inlet temperature is fixed at the ambient 20oC, while the air outlet temperature approaches closer and closer to 20oC under reducing cooling load in IDU-3. Simulation results have revealed the decreasing trend of COP. The T-s diagram for the refrigerant cycle of 2H2C mode is evaluated under several scenarios of reduction in IDU-3 cooling load. It reveals that a decreasing temperature difference at the air side or refrigerant side can be candidate probing variables for mode switching of ODU HX. Also, similar study is conducted when the ODU HX works as evaporator, with the MFVRF system operated in 3H1C (three heating one cooling) mode. Simulations for ODU HX mode switch case have been performed, and the results validate the effectiveness of the proposed scheme of mode switching