An Aggregated and Equivalent Home Model for Power System Studies with Examples of Building Insulation and HVAC Control Improvements

Introducing new technologies into existing residences provides opportunities for the utility to enhance a community\u27s power system performance. To validate the benefits of such technologies, an energy model is required in which their integration with a community\u27s power system may be simulated. It can be difficult or impossible to properly model a power system without sufficient sample data. This paper proposes a method that uses Gaussian kernel density estimation (GKDE) to calculate the aggregated net power flow of a community\u27s distribution system with only limited sample points at each time step. Example case studies that confirm the usefulness of the GKDE method are presented in which the power system benefits of improved insulation and heating, ventilation, and air conditioning (HVAC) system control are analyzed. This analysis was performed using an EnergyPlus (EP) house model that was created and calibrated based upon an individual house. This power representation of the individual house was determined by accurately estimating the aggregated net power flow of a community in Glasgow, KY with GKDE. Simulation results based on the equivalent house energy model show that improved insulation reduces the energy consumption and the peak power at the power system level. Simulation results also show that the HVAC control reduces the peak power for the entire community

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Electric Water Heater Modeling, DR Approaches Analysis and Study of Consumer Comfort for Demand Response

With the smart energy management system household residential appliances is able to participate in the demand response events. To reduce peak load demand and complexities in the local infrastructure DR can play an important role now a days. This paper presents a study and analysis of several papers on residential EWH DR modeling and implementation. It shows an overview of analysis of the most used and recent DR models for EWH. It also shows the analysis of the used methods to model this and the used approach in several papers. Additionally, the discussed consumer comforts and obtainable benefits in several papers by participating in DR events is also shown here. The study and analysis in this paper will contribute to the future research and encourage the end users to participate in households DR events.The present work was done and funded in the scope of the following projects: H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794); SIMOCE (ANI|P2020 17690); and UID/EEA/00760/2019 funded by FEDER Funds through COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

Increase Microgrid's Consumer Comfort by Using Fuzzy and Optimization Algorithms

Whereas the most important fundamental factor for today’s human is energy and wasting energy leads to increasing costs and destruction of natural resources, it is attempted through using modern and electronic methods to optimize the energy consumption and preventing of wasting energy. According to technological advancements and level of knowledge of people and having different electronic means, it is applied from several methods including: wireless sensor networks at home automation, energy management system, BEMS system and intelligent electrical keys on building to respond the requirements of users that leads to comfort of users, reducing costs, optimization of energy consumption and prevention of wasting energy. In this article, it is benefit from intelligent control methods by using optimization algorithms (PSO & GA) and fuzzy logic for controlling energy of building in order to obtain the maximum welfare and comfort of inhabitants in a building using from new pneumatic and solar recyclable resources. In order to show this performance, it is benefit from simulation at MATLAB environment

Aggregation of thermostatically controlled loads for flexibility markets

This paper presents a tool for an aggregator of thermostatically controlled loads (TCLs) to optimally combine their flexibilities into a few representative bids to be submitted to flexibility markets. The tool employs a “bottom-up” approach based on physical end-use load models, being the individual flexibility of each individual TCL simulated with a second-order thermal model describing the dynamics of the house. The approach is based on a direct load control (DLC) of thermostat temperature set-point by the aggregator. End-users receive an economic compensation in exchange for the loss of comfort. The applicability of the proposed model is demonstrated in a simulation case study based on an actual power system in Spain.The research leading to this publication has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 691405

Control of Residential Space Heating for Demand Response Using Grey-box Models

Certain advanced control schemes are capable of making a part of the thermostatic loads of space heating in buildings flexible, thereby enabling buildings to engage in so-called demand response. It has been suggested that this flexible consumption may be a valuable asset in future energy systems where conventional fossil fuel-based energy production have been partially replaced by intermittent energy production from renewable energy sources. Model predictive control (MPC) is a control scheme that relies on a model of the building to predict the future impact on the temperature conditions in the building of both control decisions (space heating) and phenomena outside the influence of the control scheme (e.g. weather conditions). MPC has become one of the most frequently used control schemes in studies investigating the potential for engaging buildings in demand response. While research has indicated MPC to have many useful applications in buildings, several challenges still inhibit its adoption in practice. A significant challenge related to MPC implementation lies in obtaining the required model of the building, which is often derived from measurements of the temperature and heating consumption. Furthermore, studies have indicated that, although demand response in buildings could contribute to the task of balancing supply and demand, suitable tariff structures that incentivize consumers to engage in DR are lacking. The main goal of this work is to contribute with research that addresses these issues. This thesis is divided into two parts.The first part of the thesis explores ways of simplifying the task of obtaining the building model that is required for implementation of MPC. Studies that explore practical ways of obtaining the measurement data needed for model identification are presented together with a study evaluating the suitedness of different low-order model structures that are suited for control-purposes.The second part of the thesis presents research on the potential of utilizing buildings for demand response. First, two studies explore and evaluate suitable incentive mechanisms for demand response by implementing an MPC scheme in a multi-apartment building block. These studies evaluate two proposed incentive mechanisms as well as the impact of building characteristics and MPC scheme implementation. Finally, a methodology for bottom-up modelling of entire urban areas is presented, and proved capable of predicting the aggregated energy demand of urban areas. The models resulting from the methodology are then applied in an analysis on demand response