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

SALSA: A Formal Hierarchical Optimization Framework for Smart Grid

The smart grid, by the integration of advanced control and optimization technologies, provides the traditional grid with an indisputable opportunity to deliver and utilize the electricity more efficiently. Building smart grid applications is a challenging task, which requires a formal modeling, integration, and validation framework for various smart grid domains. The design flow of such applications must adapt to the grid requirements and ensure the security of supply and demand. This dissertation, by proposing a formal framework for customers and operations domains in the smart grid, aims at delivering a smooth way for: i) formalizing their interactions and functionalities, ii) upgrading their components independently, and iii) evaluating their performance quantitatively and qualitatively.The framework follows an event-driven demand response program taking no historical data and forecasting service into account. A scalable neighborhood of prosumers (inside the customers domain), which are equipped with smart appliances, photovoltaics, and battery energy storage systems, are considered. They individually schedule their appliances and sell/purchase their surplus/demand to/from the grid with the purposes of maximizing their comfort and profit at each instant of time. To orchestrate such trade relations, a bilateral multi-issue negotiation approach between a virtual power plant (on behalf of prosumers) and an aggregator (inside the operations domain) in a non-cooperative environment is employed. The aggregator, with the objectives of maximizing its profit and minimizing the grid purchase, intends to match prosumers' supply with demand. As a result, this framework particularly addresses the challenges of: i) scalable and hierarchical load demand scheduling, and ii) the match between the large penetration of renewable energy sources being produced and consumed. It is comprised of two generic multi-objective mixed integer nonlinear programming models for prosumers and the aggregator. These models support different scheduling mechanisms and electricity consumption threshold policies.The effectiveness of the framework is evaluated through various case studies based on economic and environmental assessment metrics. An interactive web service for the framework has also been developed and demonstrated

Variable Weighted Multi-Objective Multi-Dimensional Genetic Algorithm for Demand Response Scheduling in a Smart Grid

This research presents the optimized scheduling of demand response loads of a residential community of 30 houses using a multi-objective multi-dimensional genetic algorithm (MOMD-GA) with a variable weighted objective function. Incorporating day ahead hourly real time pricing (RTP), the MOMD-GA attempts to present possible optimized dispatch patterns with their associated penalties and constraints (environmental, consumers and suppliers) thus providing system operators (SOs) and distribution network operators (DNOs) sufficient data for real time decision making. The variable weights for each considered component of the cost function is chosen to force the MOMD-GA towards exploring optimum solutions with lower environmental cost. Further shown are the trade-offs in selecting particular dispatch bias (consumer, supplier, environmental and optimized) and the impact of the various dispatch scenarios on the cost of overall electricity bill of the community

Energy Technology and Management

The civilization of present age is predominantly dependent on energy resources and their utilization. Almost every human activity in today's life needs one or other form of energy. As world's energy resources are not unlimited, it is extremely important to use energy efficiently. Both energy related technological issues and policy and planning paradigms are highly needed to effectively exploit and utilize energy resources. This book covers topics, ranging from technology to policy, relevant to efficient energy utilization. Those academic and practitioners who have background knowledge of energy issues can take benefit from this book

Ten questions concerning integrating smart buildings into the smart grid

Recent advances in information and communications technology (ICT) have initiated development of a smart electrical grid and smart buildings. Buildings consume a large portion of the total electricity production worldwide, and to fully develop a smart grid they must be integrated with that grid. Buildings can now be ‘prosumers’ on the grid (both producers and consumers), and the continued growth of distributed renewable energy generation is raising new challenges in terms of grid stability over various time scales. Buildings can contribute to grid stability by managing their overall electrical demand in response to current conditions. Facility managers must balance demand response requests by grid operators with energy needed to maintain smooth building operations. For example, maintaining thermal comfort within an occupied building requires energy and, thus an optimized solution balancing energy use with indoor environmental quality (adequate thermal comfort, lighting, etc.) is needed. Successful integration of buildings and their systems with the grid also requires interoperable data exchange. However, the adoption and integration of newer control and communication technologies into buildings can be problematic with older legacy HVAC and building control systems. Public policy and economic structures have not kept up with the technical developments that have given rise to the budding smart grid, and further developments are needed in both technical and non-technical areas

Hydrodynamics-Biology Coupling for Algae Culture and Biofuel Production

International audienceBiofuel production from microalgae represents an acute optimization problem for industry. There is a wide range of parameters that must be taken into account in the development of this technology. Here, mathematical modelling has a vital role to play. The potential of microalgae as a source of biofuel and as a technological solution for CO2 fixation is the subject of intense academic and industrial research. Large-scale production of microalgae has potential for biofuel applications owing to the high productivity that can be attained in high-rate raceway ponds. We show, through 3D numerical simulations, that our approach is capable of discriminating between situations where the paddle wheel is rapidly moving water or slowly agitating the process. Moreover, the simulated velocity fields can provide lagrangian trajectories of the algae. The resulting light pattern to which each cell is submitted when travelling from light (surface) to dark (bottom) can then be derived. It will then be reproduced in lab experiments to study photosynthesis under realistic light patterns