Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments

The increased penetration of uncertain and variable renewable energy presents various resource and operational electric grid challenges. Micro-level (household and small commercial) demand-side grid flexibility could be a cost-effective strategy to integrate high penetrations of wind and solar energy, but literature and field deployments exploring the necessary information and communication technologies (ICTs) are scant. This paper presents an exploratory framework for enabling information driven grid flexibility through the Internet of Things (IoT), and a proof-of-concept wireless sensor gateway (FlexBox) to collect the necessary parameters for adequately monitoring and actuating the micro-level demand-side. In the summer of 2015, thirty sensor gateways were deployed in the city of Managua (Nicaragua) to develop a baseline for a near future small-scale demand response pilot implementation. FlexBox field data has begun shedding light on relationships between ambient temperature and load energy consumption, load and building envelope energy efficiency challenges, latency communication network challenges, and opportunities to engage existing demand-side user behavioral patterns. Information driven grid flexibility strategies present great opportunity to develop new technologies, system architectures, and implementation approaches that can easily scale across regions, incomes, and levels of development

Model-Based Predictive Control Scheme for Cost Optimization and Balancing Services for Supermarket Refrigeration Systems

A new formulation of model predictive control for supermarket refrigeration systems is proposed to facilitate the regulatory power services as well as energy cost optimization of such systems in the smart grid. Nonlinear dynamics existed in large-scale refrigeration plants challenges the predictive control design. It is however shown that taking into account the knowledge of different time scales in the dynamical subsystems makes possible a linear formulation of a centralized predictive controller. A realistic scenario of regulatory power services in the smart grid is considered and formulated in the same objective as of cost optimization one. A simulation benchmark validated against real data and including significant dynamics of the system are employed to show the effectiveness of the proposed control scheme

Investigation of temporal mismatch of the energy consumption and local energy generation in the domestic environment

Conventional energy sources are not only finite and depleting rapidly, but are a major source of global warming because they are key contributors of greenhouse gases to the atmosphere. Renewable energy sources are one important approach to these challenges. Distributed micro-generation energy sources are expected to increase the diversity of energy sources for the grid, but also increase the flexibility and resilience of the grid. Furthermore, it could reduce the domestic energy demand from the grid by enabling local consumption of energy generated through renewable sources. The most widely installed renewable energy generation systems in domestic environments, in UK, are based on solar power. However, there is a common recurring issue related to output intermittency of most promising renewable energy generation methods (e.g. solar and wind), resulting in a temporal energy mismatch between local energy generation and energy consumption. Current state-of-the-art technologies/solutions for tackling temporal energy mismatch rely on various types of energy storage technologies, most of which are not suitable for the domestic environments because they are designed for industrial scale application and relatively costly. As such energy storage system technologies are generally not deemed as economically viable or attractive for domestic environments. This research project seeks to tackle the temporal energy mismatch problem between local PV generated energy and domestic energy consumption without the need for dedicated energy storage systems; without affecting the householders comfort and/or imposing operational burdens on the householders. Simulation has been chosen as the major vehicle to facilitate much of the research investigation although data collated from related research projects in the UK and Jordan have been used in the research study. Solar radiation models have been established for predicting the solar radiation for days with clear-sky for any location at any time of the year. This model has achieved a correlation factor of 0.99 in relating to the experimental data-set obtained from National Energy Research Centre Amman/Jordan. Such a model is an essential component for supporting this research study, which has been employed to predict the amount of solar power that could be obtained in different locations and different day(s) of the year. A Domestic Energy Ecosystem Model (DEEM) has been established, which is comprised of two sub-models, namely “PV panels” and “domestic energy consumption” models. This model can be configured with different parameters such as power generation capacity of the photovoltaic (PV) panels and the smart domestic appliances to model different domestic environments. The DEEM model is a vital tool for supporting the test, evaluation and validation of the proposed temporal energy mismatch control strategies. A novel temporal energy mismatch control strategy has been proposed to address these issues by bringing together the concepts of load shifting and energy buffering, with the support of smart domestic appliances. The ‘What-if’ analysis approach has been adopted to facilitate the study of ‘cause-effect’ under different scenarios with the proposed temporal energy mismatch control strategy. The simulation results show that the proposed temporal energy mismatch control strategy can successfully tackle the temporal energy mismatch problem for a 3 bedroom semi-detached house with 2.5kWp PV panels installed, which can utilise local generated energy by up to 99%, and reduce the energy demand from the grid by up to 50%. Further analysis using the simulation has indicated significant socio-economic impacts to the householders and the environment could be obtained from the proposed temporal energy mismatch control strategy. It shows the proposed temporal energy mismatch control strategy could significantly reduce the annual grid energy consumption for a 3 bedrooms semi-detached house and produce significant carbon reductions

CleanTX Analysis on the Smart Grid

The utility industry in the United States has an opportunity to revolutionize its electric grid system by utilizing emerging software, hardware and wireless technologies and renewable energy sources. As electricity generation in the U.S. increases by over 30% from today’s generation of 4,100 Terawatt hours per year to a production of 5,400 Terawatt hours per year by 2030, a new type of grid is necessary to ensure reliable and quality power. The projected U.S. population increase and economic growth will require a grid that can transmit and distribute significantly more power than it does today. Known as a Smart Grid, this system enables two- way transmission of electrons and information to create a demand-response system that will optimize electricity delivery to consumers. This paper outlines the issues with the current grid infrastructure, discusses the economic advantages of the Smart Grid for both consumers and utilities, and examines the emerging technologies that will enable cleaner, more efficient and cost- effective power transmission and consumption.IC2 Institut