Optimal control and performance of photovoltachromic switchable glazing for building integration in temperate climates

The development of adaptive building envelope technologies, and particularly of switchable glazing, can make significant contributions to decarbonisation targets. It is therefore essential to quantify their effect on building energy use and indoor environmental quality when integrated into buildings. The evaluation of their performance presents new challenges when compared to conventional “static” building envelope systems, as they require design and control aspects to be evaluated together, which are also mutually interrelated across thermal and visual physical domains. This paper addresses these challenges by presenting a novel simulation framework for the performance evaluation of responsive building envelope technologies and, particularly, of switchable glazing. This is achieved by integrating a building energy simulation tool and a lighting simulation one, in a control optimisation framework to simulate advanced control of adaptive building envelopes. The performance of a photovoltachromic glazing is evaluated according to building energy use, Useful Daylight Illuminance, glare risk and load profile matching indicators for a sun oriented office building in different temperate climates. The original architecture of photovoltachromic cell provides an automatic control of its transparency as a function of incoming solar irradiance. However, to fully explore the building integration potential of photovoltachromic technology, different control strategies are evaluated, from passive and simple rule based controls, to optimised rule based and predictive controls. The results show that the control strategy has a significant impact on the performance of the photovoltachromic switchable glazing, and of switchable glazing technologies in general. More specifically, simpler control strategies are generally unable to optimise contrasting requirements, while more advanced ones can increase energy saving potential without compromising visual comfort. In cooling dominated scenarios reactive control can be as effective as predictive for a switchable glazing, differently than heating dominated scenarios where predictive control strategies yield higher energy saving potential. Introducing glare as a control parameter can significantly decrease the energy efficiency of some control strategies, especially in heating dominated climates.This work was conducted as part of a PhD research sponsored by UK EPSRC and Wintech Ltd. The authors acknowledge the support of the COST Action TU1403 – Adaptive Facades Network (www.adaptivefacade.eu) and the University of Sydney (IPDF fund). The experimental data used as an input in this work were partially supported by Regione PUGLIA (APQ Reti di Laboratorio, project “PHOEBUS” cod. 31) and by Italian Minister for Education and Research which funded the R&D program “MAAT” (PON02_00563_3316357 − CUP B31C12001230005). The devices were fabricated at the Center for Biomolecular Nanotechnologies of Istituto Italiano di Tecnologia and characterized in the laboratories of CNR-Nano in Lecce. The contribution of the fourth author to the work reported in this paper was supported by the Australian Research Council through its Future Fellowship scheme (FT140100130).This is the final version of the article. It first appeared from Elsevier at http://dx.doi.org/10.1016/j.apenergy.2016.06.107

Robust Controller for Delays and Packet Dropout Avoidance in Solar-Power Wireless Network

Solar Wireless Networked Control Systems (SWNCS) are a style of distributed control systems where sensors, actuators, and controllers are interconnected via a wireless communication network. This system setup has the benefit of low cost, flexibility, low weight, no wiring and simplicity of system diagnoses and maintenance. However, it also unavoidably calls some wireless network time delays and packet dropout into the design procedure. Solar lighting system offers a clean environment, therefore able to continue for a long period. SWNCS also offers multi Service infrastructure solution for both developed and undeveloped countries. The system provides wireless controller lighting, wireless communications network (WI-FI/WIMAX), CCTV surveillance, and wireless sensor for weather measurement which are all powered by solar energy

Smart home energy management: An analysis of a novel dynamic pricing and demand response aware control algorithm for households with distributed renewable energy generation and storage

Home energy management systems (HEMS) technology can provide a smart and efficient way of optimising energy usage in residential buildings. One of the main goals of the Smart Grid is to achieve Demand Response (DR) by increasing end users’ participation in decision making and increasing the level of awareness that will lead them to manage their energy consumption in an efficient way. This research presents an intelligent HEMS algorithm that manages and controls a range of household appliances with different demand response (DR) limits in an automated way without requiring consumer intervention. In addition, a novel Multiple Users and Load Priority (MULP) scheme is proposed to organise and schedule the list of load priorities in advance for multiple users sharing a house and its appliances. This algorithm focuses on control strategies for controllable loads including air-conditioners, dishwashers, clothes dryers, water heaters, pool pumps and electrical vehicles. Moreover, to investigate the impact on efficiency and reliability of the proposed HEMS algorithm, small-scale renewable energy generation facilities and energy storage systems (ESSs), including batteries and electric vehicles have been incorporated. To achieve this goal, different mathematical optimisation approaches such as linear programming, heuristic methods and genetic algorithms have been applied for optimising the schedule of residential loads using different demand side management and demand response programs as well as optimising the size of a grid connected renewable energy system. Thorough incorporation of a single objective optimisation problem under different system constraints, the proposed algorithm not only reduces the residential energy usage and utility bills, but also determines an optimal scheduling for appliances to minimise any impacts on the level of consumer comfort. To verify the efficiency and robustness of the proposed algorithm a number of simulations were performed under different scenarios. The simulations for load scheduling were carried out over 24 hour periods based on real-time and day ahead electricity prices. The results obtained showed that the proposed MULP scheme resulted in a noticeable decrease in the electricity bill when compared to the other scenarios with no automated scheduling and when a renewable energy system and ESS are not incorporated. Additionally, further simulation results showed that widespread deployment of small scale fixed energy storage and electric vehicle battery storage alongside an intelligent HEMS could enable additional reductions in peak energy usage, and household energy cost. Furthermore, the results also showed that incorporating an optimally designed grid-connected renewable energy system into the proposed HEMS algorithm could significantly reduce household electricity bills, maintain comfort levels, and reduce the environmental footprint. The results of this research are considered to be of great significance as the proposed HEMS approach may help reduce the cost of integrating renewable energy resources into the national grid, which will be reflected in more users adopting these technologies. This in turn will lead to a reduction in the dependence on traditional energy resources that can have negative impacts on the environment. In particular, if a significant proportion of households in a region were to implement the proposed HEMS with the incorporation of small scale storage, then the overall peak demand could be significantly reduced providing great benefits to the grid operator as well as the households

A review of community electrical energy systems

This paper is aimed at new entrants into the field of electrical community energy. It reviews some of the work that is underway into community electrical energy projects. This review includes a summary of key issues and components which need consideration including some or all of the following; demand side management, energy storage (including vehicle to grid) and renewable generation. The paper looks further into the energy management schemes of these projects and summarises previously published methodology in the area

Simulation-Based Evaluation and Optimization of Control Strategies in Buildings

Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.Research leading to these results has been partially supported by the Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability (MOEEBIUS) project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 680517. Georgios Giannakis and Dimitrios Rovas gratefully acknowledge financial support from the European Commission H2020-EeB5-2015 project “Optimised Energy Efficient Design Platform for Refurbishment at District Level” under Contract #680676 (OptEEmAL). Georgios Kontes and Christopher Mutschler gratefully acknowledge financial support from the Federal Ministry of Education and Research of Germany in the framework of Machine Learning Forum (grant number 01IS17071). Georgios Kontes, Natalia Panagiotidou, Simone Steiger and Gunnar Gruen gratefully acknowledge use of the services and facilities of the Energie Campus Nürnberg. The APC was funded by MOEEBIUS project. This paper reflects only the authors’ views and the Commission is not responsible for any use that may be made of the information contained therein