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

An Energy management system structure for Neighborhood Networks

The accelerated integration of Renewable Energy Resources (RES) and Dispersed Generations (DGs) has contributed to big shifts in the power grid. The incorporation of home-scale electricity generators (HSEGs) into the Neighborhood Networks is considered and has contributed to the development of more stable and efficient smart grids. The implementation of this system includes an integrated control system along with a power electronic converter. In this paper, a power electronic-based HEMS scheme is presented for the neighborhood network including adjacent HSEGs to achieve an energy positive/neutral neighborhood. A multi home energy hub neighborhood network (MHEHNN) is classified in terms of system structure, functionalities, and energy management system. Different scenarios are investigated to evaluate the significance of proposed control strategy for the case study (a system with two HEHs and two conventional buildings (CBs)) using MATLAB/SIMULINK simulations. A large-scale MHEHNN is often simulated in order to test system performance on a broader scale. The findings obtained reveal that the HEHs offer more resources to the MHEHNNN under the suggested scheme (about 98% more than conventional scheme). As a result, by the sale of surplus power, they gain more. By avoiding import payments from the grid, the energy cost of CBs is minimised. Around 98% of the entire day is also decreased by the grid load

Coordinated Clustering Algorithms to Support Charging Infrastructure Design for Electric Vehicles

The confluence of several developments has created an opportune moment for energy system modernization. In the past decade, smart grids have attracted many research activities in different domains. To realize the next generation of smart grids, we must have a comprehensive understanding of interdependent networks and processes. Next-generation energy systems networks cannot be effectively designed, analyzed, and controlled in isolation from the social, economic, sensing, and control contexts in which they operate. In this paper, we develop coordinated clustering techniques to work with network models of urban environments to aid in placement of charging stations for an electrical vehicle deployment scenario. We demonstrate the multiple factors that can be simultaneously leveraged in our framework in order to achieve practical urban deployment. Our ultimate goal is to help realize sustainable energy system management in urban electrical infrastructure by modeling and analyzing networks of interactions between electric systems and urban populations

Experimental Wireless Wattmeter for Home Energy Management Systems

Part 11: Energy: ManagementInternational audienceThis paper presents a novel prototype device for domestic load energy consumption monitoring. Zigbee-based wireless connectivity is included as a basic feature of the prototype. The proposed device allows individual tracking of major energy consumption loads. Real time energy data is acquired and transmitted through a RF link to a wireless terminal unit, which works as a data logger and as a human-machine interface. Both voltage and current measurements are implemented using Hall Effect principle based transducers, while C code is developed on two 16-bit RISC MCU. The experimental setup is described and tests are conducted in order to assess its performance