Intelligent Home Energy Management Systems for Distributed Renewable Generators, Dispatchable Residential Loads and Distribted Energy Storage Devices

The high demand for electricity and the consequent increase in electricity price as lead to recentstudy in reducingthe total operating cost of a residential building. This research work focuson energy management in a residential green house.Two innovative approach is proposed to solve excessiveoperating cost of a residential green house, the system inputs which consist of temperature, activity level, and energyconsumption is based on five household occupant in Atlanta, Georgia, also a Chevy volt of 16kWh is used in the case studies.Moreover, for a single residential house, the overall goal is to reduce the total operating costs and the carbon emissions for a future residential house, while satisfying the end-users’ comfort levels. This paper models a wide variety of home appliances and formulates the economic operation problem using mixed integer linear programming. Case studies are performed to validate and demonstrate the effectiveness of the proposed solution algorithm. Simulation results also show the positive impact of dispatchable loads, distributed renewable generators, and distributed energy storage devices on a future residential house.For networked residential houses, we present an optimization of total operating cost of an interconnected nanogrid (ING) considering the effect of V2H andV2G, which helps tominimizethe total operating cost. The major objective is to reduce carbon emission, total operating cost and the peak load demand while satisfying the customer preferences of each nanogrid. A mixed integer linear program (MILP) is formulated to solve the economic operation of the ING. Furthermore, case studies are performed to demonstrate the positive impact INGs have on minimizing total operating cost.Master of Science in EngineeringElectrical Engineering, College of Engineering and Computer ScienceCollege of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/138102/1/Intelligent Home Energy Management Systems for Distributed Renewable Generators, Dispatchable Residential Loads and Distribted Energy Storage Devices.pdfDescription of Intelligent Home Energy Management Systems for Distributed Renewable Generators, Dispatchable Residential Loads and Distribted Energy Storage Devices.pdf : Thesi

A Case Study on Application of Fuzzy Logic based Controller for Peak Load Shaving in a Typical Household\u27s Per Day Electricity Consumption

The cost of electricity for consumers depends on the cost of generation, transmission, and distribution of power. The electrical load consumed by consumers per day is not constant throughout the day. The utilities must be capable of meeting the load demand, which means they must have enough electricity generation potential and necessary infrastructure. This cost is significant. However, the revenue they generate will only be for the actual use of electricity by the consumers. In general, the electrical power generation is done in stages, always generating a base load. As demand changes throughout the day, additional stages of power generation are brought online to meet the changes in demand. This approach of management is known as supply-side management. Theoretically, if it is possible to manage the load such that there is lower peak demand and the difference between peak load and base load were minimized, the generation capability and grid infrastructure required to provide reliable power would be reduced resulting in lower costs for utility companies and ultimately consumers. This management strategy is referred to as demand-side management or demand response. In this research, a small-scale smart grid is modeled in Simulink to mimic the electrical grid. A Smart controller based on fuzzy logic is developed to control charging and discharging of an electric vehicle battery to provide extra power during peak times and to act as load (storing energy) during off-peak time to provide a more manageable and balanced load as seen by the grid. A comparative study is presented of electricity consumption throughout the day with or without the smart controller. The results show the significant reduction in peak demand, much smoother load curve for the grid, and a decrease in per kilowatt cost of electricity for the given day when newer pricing structures are applied

Optimal Operation of Energy Hubs in the Context of Smart Grids

With the rapid growth of energy demand and consequently growth in supply, increasing energy costs, and environmental concerns, there is a critical need to find new ways to make better use of existing energy systems and resources and decelerate the demand growth towards a sustainable energy system. All of these facts are leading to the proposal of novel approaches to optimize the utilization of energy in different sectors to reduce the customer's total energy costs, demand and greenhouse gas (GHG) emissions while taking into account the end-user preferences. Utilities have implemented Demand Side Management (DSM) and Demand Response (DR) programs to better manage their network, offer better services to their customers, handle the increase in electricity demand, and at the same time increase system reliability and reduce environmental impacts. Smart Grid developments such as information technology, communication infrastructure and smart meters improve the effectiveness and capability of Energy Management Systems (EMSs) and facilitate the development of automated operational decision-making structures for energy systems, thus assisting DSM and DR programs to reach their full potential. The literature review indicates that whereas significant work has been done in DSM and DR in utilities, these works have mostly focused on direct load control of particular loads, and there is a lack of a general framework to consider all types of energy hubs in an integrated Energy Hub Management System (EHMS). In this context, mathematical modeling of energy systems for EMSs, which is the main concern of the present work, plays a critical role. This research proposes mathematical optimization models of energy hubs which can be readily incorporated into EHMS in the context of Smart Grids. The energy hub could be a single or multi-carrier energy system in residential, commercial, agricultural and/or industrial sectors. Therefore, mathematical models for energy hubs in residential, commercial, and agricultural sectors have been developed and are presented and discussed in this thesis. In the residential sector, this research presents mathematical optimization models of residential energy hubs which can be readily incorporated into automated decision making technologies in Smart Grids, and can be solved efficiently in a real-time frame to optimally control all major residential energy loads, storage and production components while properly considering the customer preferences and comfort levels. Mathematical models for major household demand, i.e., fridge, freezer, dishwasher, washer and dryer, stove, water heater, hot tub, and pool pumps, are formulated. Also, mathematical models of other components of a residential energy system including lighting, heating, and air-conditioning are developed, and generic models for solar PV panels and energy storage/generation devices are proposed. The developed mathematical models result in a Mixed Integer Linear Programming (MILP) optimization problem, whose objective is to minimize demand, total costs of electricity and gas, emissions and peak load over the scheduling horizon while considering end-user preferences. The application of this model to a real household are shown to result in savings of up to 20% on energy costs and 50% on peak demand, while maintaining the household owner's desired comfort levels. In the commercial sector, mathematical optimization models of produce storage facilities to optimize the operation of their energy systems are proposed. In the storage facilities, climate control of the storage rooms consumes considerable energy; thus, a mathematical model of storage facilities appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing climate controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing climate control systems in storage facilities. The objective is to minimize total energy costs and demand charges while considering important parameters of storage facilities; in particular, inside temperature and humidity should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints. In the agricultural sector, this work presents mathematical optimization models of greenhouses to optimize the operation of their energy systems. In greenhouses, artificial lighting, CO2 production, and climate control consume considerable energy; thus, a mathematical model of greenhouses appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing greenhouse controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing control systems in greenhouses. The objective is to minimize total energy costs and demand charges while considering important parameters of greenhouses; in particular, inside temperature and humidity, CO2 concentration, and lighting levels should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations and robust optimization approach. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints

Dezentrales Lastmanagement zum Ausgleich kurzfristiger Abweichungen im Stromnetz

In dieser Arbeit wird ein vollständig dezentrales Konzept zum Last- und Erzeugungsmanagement vorgestellt, mit dem sich Stromverbraucher und dezentrale Stromerzeugungsanlagen selbst organisieren können, um kurzfristig präzise Laständerungen durchzuführen. Dabei sollen nicht nur größere Stromverbraucher in Industriebetrieben, sondern auch Haushaltsgeräte (z. B. Gefrierschränke) und dezentrale Stromerzeugungsanlagen einbezogen werden

Dezentrales Lastmanagement zum Ausgleich kurzfristiger Abweichungen im Stromnetz

In dieser Arbeit wird ein vollständig dezentrales Konzept zum Last- und Erzeugungsmanagement vorgestellt, mit dem sich Stromverbraucher und dezentrale Stromerzeugungsanlagen selbst organisieren können, um kurzfristig präzise Laständerungen durchzuführen. Dabei sollen nicht nur größere Stromverbraucher in Industriebetrieben, sondern auch Haushaltsgeräte (z. B. Gefrierschränke) und dezentrale Stromerzeugungsanlagen einbezogen werden