Enhancing the efficiency of electricity utilization through home energy management systems within the smart grid framework

The concept behind smart grids is the aggregation of “intelligence” into the grid, whether through communication systems technologies that allow broadcast/data reception in real-time, or through monitoring and systems control in an autonomous way. With respect to the technological advancements, in recent years there has been a significant increment in devices and new strategies for the implementation of smart buildings/homes, due to the growing awareness of society in relation to environmental concerns and higher energy costs, so that energy efficiency improvements can provide real gains within modern society. In this perspective, the end-users are seen as active players with the ability to manage their energy resources, for example, microproduction units, domestic loads, electric vehicles and their participation in demand response events. This thesis is focused on identifying application areas where such technologies could bring benefits for their applicability, such as the case of wireless networks, considering the positive and negative points of each protocol available in the market. Moreover, this thesis provides an evaluation of dynamic prices of electricity and peak power, using as an example a system with electric vehicles and energy storage, supported by mixed-integer linear programming, within residential energy management. This thesis will also develop a power measuring prototype designed to process and determine the main electrical measurements and quantify the electrical load connected to a low voltage alternating current system. Finally, two cases studies are proposed regarding the application of model predictive control and thermal regulation for domestic applications with cooling requirements, allowing to minimize energy consumption, considering the restrictions of demand, load and acclimatization in the system

Assessing the Value of Uncertainty-Aware Transactive Control Framework for Commercial and Residential Buildings

With the increasing adoption of renewable energy and electric vehicles in the power grid, dealing with uncertainty in both supply and demand is critical to ensuring reliable and efficient operations. In this study, we discuss the value of a twostage stochastic control framework for an aggregator to address such problems by promoting improved decisionmaking and performance despite inherent uncertainty. An uncertaintyaware transactive control framework was developed to account for uncertainties in future conditions due to occupancy patterns, weather conditions, onsite power generation, and realtime pricing schemes. In the dayahead period, the aggregator decides the electricity procurement plan considering the possible realtime control strategies for operation of the commercial building thermal energy storage (TES) assets and residential building electric water heaters. During realtime operations, the aggregator modulates controllable loads based on transactive market mechanisms with model predictive control (MPC). In order to evaluate the performance, this study quantified the expected value of perfect information (EVPI) and the value of the stochastic solution (VSS) to analyze the cost of uncertain information and potential benefits of solving the stochastic optimal control problem. This paper demonstrates how the stochastic solution of the developed framework can provide useful information for customers and grid operators in the management of uncertain situations to support grid reliability and sustainability

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

Economic Sizing of Batteries for the Smart Home

Growing rooftop photovoltaics (PV) adoption is beginning to challenge the electric power grid in some locations today. During many hours of the year, power is flowing out of PV-enabled buildings and back into grid. When this happens in many homes, the entire feeder voltage is raised significantly, which can be unsafe for grid assets and home appliances. Battery storage provides a good solution to this problem. By storing energy within the home, less energy flows back onto the grid. Scheduling a battery to charge during times a home would otherwise export energy, and discharge when it would otherwise import energy, the bidirectional flow of energy on the feeder is reduced. However, batteries are still expensive and need to be introduced optimally. Battery sizing is not well studied in the literature; most research uses rule of thumb to determine the battery size whereas others use tool-based methods. This paper presents a methodology to economically size a residential battery based on parametric analysis using a home energy management system (HEMS) software to optimally dispatch the battery along with controllable loads under several use cases. The study accounts for connected equipment, controls, renewable resources, and accounts for other factors such as occupancy patterns, and house characteristics. The paper defines an initial analytical pathway for such a sizing tool, develops initial sizing guidance, and clarifies technical and market opportunities for home batteries in the context of existing and emerging equipment and control technologies. One of the unique contributions of our paper is that we demonstrate how dynamic control of building equipment may change the selection, operation, lifespan and economics of solar and battery storage. We use the HEMS to optimally control the same solar-enabled residential building in three different use cases: a) with controllable loads, b) with controllable battery, and c) with controllable loads and battery. As more loads can be interactively controlled, they can relieve the burden of some of the battery’s use. This may change the cycling use cases for the battery, and possibly enable smaller batteries to be used in conjunction with equipment homeowners are already purchasing to achieve the same outcomes. In our initial study, a parametric analysis that included 132 scenarios has been performed based on different combinations of pertinent parameters. Results indicate that four parameters dominate the decision-making process: utility tariffs, application scenarios, existence of HEMS, and the anticipated payback time. Life-cycle cost analysis indicated in the absence of utility incentives, batteries plus HEMS haveat least 10-year payback time for new construction under time-of-use rate structure and feed-in tariff; larger batteries have longer payback time but may provide more benefits to utilities on power backfeed reduction under certain circumstances

Application of heat pumps and thermal storage systems for improved control and performance of microgrids

The high penetration of renewable energy sources (RES), in particular, the rooftop photovoltaic (PV) systems in power systems, causes rapid ramps in power generation to supply load during peak-load periods. Residential and commercial buildings have considerable potential for providing load exibility by exploiting energy-e_cient devices like ground source heat pump (GSHP). The proper integration of PV systems with the GSHP could reduce power demand from demand-side. This research provides a practical attempt to integrate PV systems and GSHPs e_ectively into buildings and the grid. The multi-directional approach in this work requires an optimal control strategy to reduce energy cost and provide an opportunity for power trade-o_ or feed-in in the electricity market. In this study, some optimal control models are developed to overcome both the operational and technical constraints of demand-side management (DSM) and for optimum integration of RES. This research focuses on the development of an optimal real-time thermal energy management system for smart homes to respond to DR for peak-load shifting. The intention is to manage the operation of a GSHP to produce the desired amount of thermal energy by controlling the volume and temperature of the stored water in the thermal energy storage (TES) while optimising the operation of the heat distributors to control indoor temperature. This thesis proposes a new framework for optimal sizing design and real-time operation of energy storage systems in a residential building equipped with a PV system, heat pump (HP), and thermal and electrical energy storage systems. The results of this research demonstrate to rooftop PV system owners that investment in combined TSS and battery can be more profitable as this system can minimise life cycle costs. This thesis also presents an analysis of the potential impact of residential HP systems into reserve capacity market. This research presents a business aggregate model for controlling residential HPs (RHPs) of a group of houses that energy aggregators can utilise to earn capacity credits. A control strategy is proposed based on a dynamic aggregate RHPs coupled with TES model and predicting trading intervals capacity requirements through forecasting demand and non-scheduled generation. RHPs coupled with TES are optimised to provide DSM reserve capacity. A rebound effect reduction method is proposed that reduces the peak rebound RHPs power