LITMIR.BIZ

      Preface

       Background

      Demand response (DR) is associated with significant environmental and economic benefits when looking at how the electricity grid can operate optimally. Adding flexibility in power consumption provides a sound basis for improving the grid’s environmental performance and efficiency. For example, reducing peak loads at grid level could lead to a lower level of operation for generation plants with a high running cost, low efficiency and low environmental performance. Furthermore, as the storage of electricity is bound to technical and economic constraints, the absorption of excess electricity from renewable energy sources is feasible through a demand following generation concept.

      DR is gradually gaining ground with respect to (1) the reduction of peak loads; (2) grid balancing; and (3) dealing with the volatility of renewable energy sources (RES). In this context, demand side management techniques such as peak clipping, valley filling, load shifting and flexible load shape are already being employed. Also, various DR programs are being designed and implemented, including critical peak pricing, capacity bidding, thermostat/direct load control and fast DR dispatch/ancillary services.

      In DR, the consumer becomes a prosumer with an important active role in the exchange of energy on an hourly basis. This transition calls for high environmental awareness and new tools and services which will improve the dynamic, as well as secure multidirectional exchange of energy and data. Overall, DR is identified as an important field for technological and market innovations aligned with climate change mitigation policies and the transition to sustainable smart grids in the foreseeable future.

       Why this book?

      This book provides an insight into various intrinsic aspects related to the assessment of DR potential, at the building and the community level. Issues pertaining to the use of building energy models, compared to actual performance, and smart monitoring are addressed. Furthermore, temperature set-point adjustment, which is a standard practice in controlling heating, ventilation and air conditioning (HVAC) systems, is assessed with the aid of simulation, to investigate the optimality in multidynamic systems’ operation. On the other hand, the book focuses on load shifting optimization at the community level on the basis of time of use (ToU) and real-time pricing (RTP). The rationale behind this is that energy markets should be operated in a transparent manner inducing higher efficiency of power grids through the promotion of renewable energy. In this context, it is foreseen that a high penetration of RES, given their minimal operational expenses and environmental advantages, should be reflected in the time slots of low costs and consumer prices. In this way, all consumers will be provided with a clear roadmap and the necessary motivational factors in order to adjust our consumption when possible and take advantage of electric energy availability from clean resources.

       Who is this book for?

      This book focuses on near-zero energy buildings (NZEBs), smart communities and microgrids. Therefore, on one hand, it would be valuable for experts, professionals and postgraduates with an interest in (1) highly efficient buildings and communities; (2) smart monitoring systems; and (3) building energy modeling. On the other hand, the book would be beneficial for professionals with an interest in building or community level power predictions and optimization, as well as about how such tools and techniques can be utilized to evaluate DR at the building and/or district level.

       Structure

      Firstly, a comprehensive approach for evaluating the performance of industrial and residential smart energy buildings/NZEBs is presented. A detailed audit of construction characteristics, installed systems and controls is conducted and presented. Subsequently, holistic data from advanced metering and sensor equipment are explored to verify energy consumption and actual building energy performance. Dynamic energy models are developed, validated and tested to explore key aspects of the operational behavior of buildings and systems, and draw essential knowledge about their performance. Consumption data based on real measurements is compared, on one hand, with dynamic building model simulation results and on the other hand, with the initial annual energy consumption, obtained via the building’s energy efficiency certification scheme prior to construction. Findings are explored to address the actual performance gap, reflect on the limitations of each approach and highlight important conclusions.

Secondly, the book focuses on how DR can be applied at the building level. A novel evaluation and optimization methodology, in the context of the building level DR, is presented. To this end, DR is assessed with the aid of an RTP scheme based on the actual energy market data. In this context, HVAC system performance is evaluated according to the energy consumption, the corresponding energy costs and the indoor thermal comfort.

      Thirdly, the book describes how DR can be applied at the community level by exploiting predictions of day-ahead consumption and/or production and load shifting. The benefits of this approach are evaluated in terms of the economic savings based on a flat versus ToU tariff and an RTP scheme. The reliable prediction of power consumption and/or production 24 hours ahead is performed using artificial neural network modeling, whereas load shifting optimization is conducted using a genetic algorithm dual-objective optimization algorithm.

      In Chapter 2, the smart and zero energy building facilities used as case studies for evaluating DR at the building and the community levels are presented.

      Chapter 3 provides a thorough analysis of the performance of residential and industrial buildings with the aid of measurements and how they can be utilized for building energy modeling and validation purposes.

      Chapter 4 presents a newly developed approach for optimizing the operation of HVAC systems from a DR perspective.

      Chapter 5 presents a novel approach for the community level prediction and optimization in a DR setting.

      Finally, the overall conclusions and recommendations arising from the findings of this research are presented.

       Acknowledgments

      The editors express their deepest appreciation to all the authors for their contribution and to the European Commission, for allocating the funds in order for the Smart GEMS project to be implemented. Special thanks are owed to Dr. Cristina Cristalli, Head of Research for Innovation in the Loccioni Group and to the Loccioni Group for providing access and support for research activities in the framework of Smart GEMS project to be conducted in their industrial high-end facilities.

      Nikos KAMPELIS

      September 2020