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The paper presents the energy optimization of the cold water loop of the heat pump heating system using analytical-numerical procedure. The aim of the study is obtain the maximum COP of the heating system by optimum of the wall water mass-flow rate and well pump power. The objective function is the heating system’s COP. All components of the heating system: evaporator, condenser, compressor, circulation pump, and well pump are described by steady-state, lumped mathematical model. The model’s equations are coupled, non-linear, multivariable and algebraic, the solution is feasible using an iterative numerical method. The MATLAB’s program with Gauss elimination and Newton-linearization method is applied for solving the model. The obtained numerical data are presented in 3-D graphics. The optimum value of the cold-well water mass-flow rate is obtained from the graphics or by using a selection algorithm. The results of the study are the adequate mathematical model for energy optimization of the heating system, the numerical algorithm for solving the model and the ultimate goal to obtain the optimum of the power of well pump and compressor. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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Utility service providers are often challenged with the synchronization of thermostatically controlled loads. Load synchronization, as a result of naturally occurring and demand-response events, has the potential to damage power distribution equipment. Because thermostatically controlled loads constitute most of the power consumed by the grid at any given time, the proper control of such devices can lead to significant energy savings and improved grid stability. The contribution of this paper is the development of an optimal control algorithm for commonly used variable speed heat pumps. By means of selective peer-to-peer communication, our control architecture allows for the regulation of home temperatures while simultaneously minimizing aggregate power consumption, and aggregate load volatility. An optimal centralized controller is also explored and compared against its decentralized counterpart. [ABSTRACT FROM AUTHOR]

With the large-scale deployment of smart meters worldwide, research in non-intrusive load monitoring (NILM) has seen a significant rise due to its dual use of real-time monitoring of end-user appliances and user-centric feedback of power consumption usage. NILM is a technique for estimating the state and the power consumption of an individual appliance in a consumer's premise using a single point of measurement device such as a smart meter. Although there are several existing NILM techniques, there is no meaningful and accurate metric to evaluate these NILM techniques for multi-state devices such as the fridge, heat pump, etc. In this paper, we demonstrate the inadequacy of the existing metrics and propose a new metric that combines both event classification and energy estimation of an operational state to give a more realistic and accurate evaluation of the performance of the existing NILM techniques. In particular, we use unsupervised clustering techniques to identify the operational states of the device from a labeled dataset to compute a penalty threshold for predictions that are too far away from the ground truth. Our work includes experimental evaluation of the state-of-the-art NILM techniques on widely used datasets of power consumption data measured in a real-world environment. [ABSTRACT FROM AUTHOR]

The capability of domestic heat pumps to provide dynamic frequency response to an electric power system was investigated. A thermal model was developed to represent a population of domestic heat pumps. A decentralized dynamic control algorithm was developed, enabling the heat pumps to alter their power consumption in response to a system frequency. The control algorithm ensures a dynamic relationship between the temperature of building and grid frequency. The availability of heat pumps to provide low-frequency response was obtained based on data supplied by Element Energy. Case studies were carried out by connecting a representative model of the aggregated heat pumps to the regional Great Britain (GB) transmission system model, which was developed by National Grid. It was shown that the dynamically controlled heat pumps distributed over GB zones have a significant impact on the GB system frequency and reduce the dependency on frequency services that are currently supplied by expensive frequency-sensitive generators. The rate of change of frequency was also reduced when there is a reduction in system inertia. [ABSTRACT FROM AUTHOR]