Coordinated Smart Home Thermal and Energy Management System Using a Co-simulation Framework

The increasing demand for electricity especially during the peak hours threaten the grid reliability. Demand response (DR), changing the load pattern of the consumer in response to system conditions, can decrease energy consumption during periods of high wholesale market price and also maintain system reliability. Residential homes consume 38% of the total electric energy in the U.S., making them promising for DR participation. Consumers can be motivated to participate in DR programs by providing incentives (incentive-based DR), or by introducing a time-varying tariff for electricity consumption (price-based DR). A home energy management system (HEMS), an automated system which can alter the residential consumer’s energy consumption pattern based on the price of electricity or financial incentives, enables the consumers to participate in such DR programs. HEMS also should consider consumer comfort during the scheduling of the heating, ventilation, and air conditioning (HVAC) and other appliances. As internal heat gain of appliances and people have a significant effect in the HVAC energy consumption, an integrated HVAC and appliance scheduling are necessary to properly evaluate potential benefits of HEMS. This work presents the formulation of HEMS considering combined scheduling of HVAC and appliances in time-varying tariff. The HEMS also considers the consumer comfort for the HVAC and appliances while minimizing the total electricity cost. Similarly, the HEMS also considers the detailed building model in EnergyPlus, a building energy analysis tool, to evaluate the effectiveness of the HEMS. HEMS+, a communication interface to EnergyPlus, is designed to couple HEMS and EnergyPlus in this work. Furthermore, a co-simulation framework coupling EnergyPlus and GridLAB-D, a distribution system simulation tool, is developed. This framework enables incorporation of the controllers such as HEMS and aggregator, allowing controllers to be tested in detail in both building and power system domains. Lack of coordination among a large number of HEMS responding to same price signal results in peak more severe than the normal operating condition. This work presents an incentive-based hierarchical control framework for coordinating and controlling a large number of residential consumers’ thermostatically controlled loads (TCLs) such as HVAC and electric water heater (EWH). The potential market-level economic benefits of the residential demand reduction are also quantified

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

HOME ENERGY MANAGEMENT SYSTEM FOR DEMAND RESPONSE PURPOSES

The growing demand for electricity has led to increasing efforts to generate and satisfy the rising demand. This led to suppliers attempting to reduce consumption with the help of the users. Requests to shift unnecessary loads off the peak hours, using other sources of generators to supply the grid while offering incentives to the users have made a significant effect. Furthermore, automated solutions were implemented with the help of Home Energy Management Systems (HEMS) where the user can remotely manage household loads to reduce consumption or cost. Demand Response (DR) is the process of reducing power consumption in a response to demand signals generated by the utility based on many factors such as the Time of Use (ToU) prices. Automated HEMS use load scheduling techniques to control house appliances in response to DR signals. Scheduling can be purely user-dependent or fully automated with minimum effort from the user. This thesis presents a HEMS which automatically schedules appliances around the house to reduce the cost to the minimum. The main contributions in this thesis are the house controller model which models a variety of thermal loads in addition to two shiftable loads, and the optimizer which schedules the loads to reduce the cost depending on the DR signals. The controllers focus on the thermal loads since they have the biggest effect on the electricity bill, they also consider many factors ignored in similar models such as the physical properties of the room/medium, the outer temperatures, the comfort levels of the users, and the occupancy of the house during scheduling. The DR signal was the hourly electricity price; normally higher during the peak hours. Another main part of the thesis was studying multiple optimization algorithms and utilizing them to get the optimum scheduling. Results showed a maximum of 44% cost reduction using different metaheuristic optimization algorithms and different price and occupancy schemes

Upscaling energy control from building to districts: current limitations and future perspectives

Due to the complexity and increasing decentralisation of the energy infrastructure, as well as growing penetration of renewable generation and proliferation of energy prosumers, the way in which energy consumption in buildings is managed must change. Buildings need to be considered as active participants in a complex and wider district-level energy landscape. To achieve this, the authors argue the need for a new generation of energy control systems capable of adapting to near real-time environmental conditions while maximising the use of renewables and minimising energy demand within a district environment. This will be enabled by cloud-based demand-response strategies through advanced data analytics and optimisation, underpinned by semantic data models as demonstrated by the Computational Urban Sustainability Platform, CUSP, prototype presented in this paper. The growing popularity of time of use tariffs and smart, IoT connected devices offer opportunities for Energy Service Companies, ESCo’s, to play a significant role in this new energy landscape. They could provide energy management and cost savings for adaptable users, while meeting energy and CO2 reduction targets. The paper provides a critical review and agenda setting perspective for energy management in buildings and beyond

Topics in Demand Response for Energy Management in Smart Grid

Future electricity grids will enable greater and more sophisticated demand side participation, which refers to the inclusion of mechanisms that enable dynamic modification of electricity demand into the operations of the electricity market, known as Demand Response (DR). The underlying information-flow infrastructures provided by the emerging smart grid enhance the interactions between customers and the market, by which DR will improve electricity grids in several aspects, e.g., by reducing peak demand and reducing need for expensive peaker plants, or by enabling demand to follow supply such as those from volatile renewable resources, etc. Many types of appliances provide flexibilities in power usage which can be viewed as demand response resources, and how to exploit such flexibilities to achieve the benefits offered by DR is a central challenge. In this dissertation, we design algorithms and architectures to bridge the gap between scheduling appliances and the benefits that DR can bring to electricity grid by utilizing the smart grid\u27s underlying information infrastructure. First, we focus on demand response within the consumer premise, where an energy management controller (EMC) schedules appliance operation on behalf of customers to save energy cost. We propose an optimization-based control scheme for the EMC in the building that integrates both the operational flexible appliances such as clothes washer/dryer, dish washer and plug-in electric vehicles (PEVs), but also the thermostatically controlled appliances such as HVAC (heating, ventilation, and air conditioning) systems together with the thermal mass of the building. Model predictive control is employed to account for uncertainty in electricity prices and weather information. Under time-varying pricing, scheduling appliances smartly using our scheme can incur notable energy cost saving for customers. As an alternative, we also propose a communication-based control approach which is a joint appliance access and scheduling scheme in which the control algorithms are embedded into the communication protocols used by appliances. The control scheme is based on a threshold maximum power consumption set by the EMC; and we discuss how this threshold can be chosen so that it integrates the availability of local distributed renewable energy resources.Then we investigate demand response in the retail market level which involves interactions between customers and utilities. Pricing-based control and direct load control (DLC) are two types of approaches that are used or envisioned for this level. To address pricing based control methods, we propose real-time pricing (RTP) signals that can be designed to work with customer premise EMCs. The interaction between these EMCs and the pricing-setting utilities is modeled as a Stackelberg game. We demonstrate that our proposed RTP scheme reduces peak load and alleviates rebound peaks that are the typical shortcomings in existing pricing approaches. To address DLC methods, we propose a distributed DLC scheme based on a two-layer communication network infrastructure for large-scale, aggregate DR implementations. In the proposed scheme, average consensus algorithms are employed to distributively allocate control tasks amongst EMCs so that local appliance scheduling within each home will eventually achieve the aggregated control task, i.e., to alleviate mismatch between electricity supply and demand.Finally, we study how demand response affects the wholesale electricity market. As is conventional when studying interactions between electricity generators, we employ the Cournot game model to analyze how DR aggregators may impact wholesale energy markets. To do so, we assume that DR aggregators employ a computationally efficient, centralized scheduling mechanism to manage deferrable load over a large aggregate set of consumers. The load reduction from deferrable load can be seen as `generation\u27 in terms of balancing the market and is compensated as such under current regulatory mandates. Thus, the DR aggregator competes with other generators in a Cournot-Nash manner to make a profit in the wholesale market; and electricity prices are consequently reduced. We provide equilibrium analysis of the wholesale market that includes DR aggregators and demonstrate that under certain conditions the equilibrium exists and is unique

Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics

We propose a multi-time scale energy management framework for a smart photovoltaic (PV) system that can calculate optimized schedules for battery operation, power purchases, and appliance usage. A smart PV system is a local energy community that includes several buildings and households equipped with PV panels and batteries. However, due to the unpredictability and fast variation of PV generation, maintaining energy balance and reducing electricity costs in the system is challenging. Our proposed framework employs a model predictive control approach with a physics-based PV forecasting model and an accurately parameterized battery model. We also introduce a multi-time scale structure composed of two-time scales: a longer coarse-grained time scale for daily horizon with 15-minutes resolution and a shorter fine-grained time scale for 15-minutes horizon with 1-second resolution. In contrast to the current single-time scale approaches, this alternative structure enables the management of a necessary mix of fast and slow system dynamics with reasonable computational times while maintaining high accuracy. Simulation results show that the proposed framework reduces electricity costs up 48.1% compared with baseline methods. The necessity of a multi-time scale and the impact on accurate system modeling in terms of PV forecasting and batteries are also demonstrated.Watari D., Taniguchi I., Goverde H., et al. Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics. Applied Energy 289, 116671 (2021); https://doi.org/10.1016/j.apenergy.2021.116671

Energy Optimization and Coordination Frameworks for Smart Homes Considering Incentives From Discomfort and Market Analysis

The electricity demand is increasing with the growing use of electricity-based appliances in today’s world. The residential sector’s electricity consumption share is also increasing. Demand response (DR) is a typical way to schedule consumers’ energy consumption and help utility to reduce the peak load demand. Residential demand management can contribute to reduce peak electric demand, decrease electricity costs, and maintain grid reliability. Though the demand management has benefits to the utility and the consumers, controlling the consumers electricity consumption provides inconvenience to the consumers. The challenge here is to properly address the customers’ inconvenience to encourage them to participate and meanwhile satisfy the required demand reduction efficiently. In this work, new incentive-based demand management schemes for residential houses are designed and implemented. This work investigates two separate DR frameworks designed with different demand reduction coordination strategies. The first framework design constitutes a utility, several aggregators, and residential houses participating in DR program. Demand response potential (DRP), an indicator of whether an appliance can contribute to the DR, guides the strategic allocation of the demand limit to the aggregators. Each aggregator aggregates the DRP of all the controllable appliances under it and sends to the utility. The utility allocates different demand limits to the aggregators based on their respective DRP ratios. Participating residential customers are benefited with financial compensation with consideration of their inconvenience. Two scenarios are discussed in this approach with DRP. One where the thermostatically controlled loads (TCLs) are controlled. The thermal comfort of residents and rewards are used to evaluate the demand response performance. The other scenario includes the time-shiftable appliances control with the same framework. The second framework is a three-level hierarchical control framework for large-scale residential DR with a novel bidding scheme and market-level analysis. It comprises of several residential communities, local controllers (LCs), a central controller (CC), and the electricity market. A demand reduction bidding strategy is introduced for the coordination among several LCs under a CC in this framework. Incentives are provided to the participating residential consumers, while considering their preferences, using a continuous reward structure. A simulation study on the 6-bus Roy Billinton Test System with 1;200 residential consumers demonstrates the financial benefits to both the electric utility and consumers

renewable sources integration through the optimization of the load for residential applications

Abstract This work presents the implementation of two different control strategies for the control of Microgrids a Model Predictive Control (MPC) technique coupled with a Mixed-Integer Linear Program (MILP) structure and a Rule Based Control (RBC) strategy both applied to a residential MicroGrid. The validation of the models has been performed with an experimental setup laid out in the laboratory of University of Rome - Tor Vergata. Results obtained show that MicroGrids connected to the main network have enough potential to support grid balancing actions, thus allowing for a greater penetration of renewable sources into the mix, and giving economic benefits for both end users and providers. In particular, using a MPC strategy major benefits can be obtained in terms of reduction of the unbalanced energy exchange with the main grid and a more efficient use of the micro-grid components

Recent techniques used in home energy management systems: a review

Power systems are going through a transition period. Consumers want more active participation in electric system management, namely assuming the role of producers–consumers, prosumers in short. The prosumers’ energy production is heavily based on renewable energy sources, which, besides recognized environmental benefits, entails energy management challenges. For instance, energy consumption of appliances in a home can lead to misleading patterns. Another challenge is related to energy costs since inefficient systems or unbalanced energy control may represent economic loss to the prosumer. The so-called home energy management systems (HEMS) emerge as a solution. When well-designed HEMS allow prosumers to reach higher levels of energy management, this ensures optimal management of assets and appliances. This paper aims to present a comprehensive systematic review of the literature on optimization techniques recently used in the development of HEMS, also taking into account the key factors that can influence the development of HEMS at a technical and computational level. The systematic review covers the period 2018–2021. As a result of the review, the major developments in the field of HEMS in recent years are presented in an integrated manner. In addition, the techniques are divided into four broad categories: traditional techniques, model predictive control, heuristics and metaheuristics, and other techniques.info:eu-repo/semantics/publishedVersio