Smart Energy System for Human Body Control

By S. M. Ali, G. Mokryani, B. Khan, C. A. Mehmood, M. Jawad

Consumers (humans) are homoeothermic organism obeying Second-Law of Thermodynamics. They exchange energy with environment and environment effect consumer living-standards and consumer psychology. In the consumer body, with mutual energy exchange during transients in environmental parameters, the primary goal of the consumer brain is to force consumer towards the following actions: (a) move towards a comfortable zone (indoor or outdoor), (b) develop a comfortable surrounding environment, and (c) adapt the consumer body to existing transient-environment. The objectives defined in part (b) and part (c) are demanding to model and investigate. The consumers will Switch ON/OFF devices and increase/decrease the intensity of surrounding temperature to optimum comfortable limits. This operation may be self-controlled or automatically wirelessly controlled between existing environment temperature and consumer body temperature. With above action, energy consumption will vary accordingly.

In Figure 1, an idea for the future smart energy consumption model is suggested that incorporates the mutual-interactions with the environment perturbing thermal-regulation of the consumer body. The model consists of a multi-feedback, closed-loop, non-linear device control system with environment parameters and consumer psychology is described as input effecting blood dynamics of consumer body, thus producing thermo-regulatory brain sensations. Brain sensations of Central Nervous System (CNS) will generate voluntary motor action compelling the consumer to re-gain optimum comfort level. For maximum comfort level, with varying blood dynamics, brain forces, to attain steady-state blood-flow by controlling the surrounding environment with heating, ventilation and air conditioning control devices. This environment-driven brain-forced consumer action will change the operational range of energy consumption through device control..

For consumer body heat exchange, there are four body layers, namely: (a) skin, (b) fat (c) muscle, and (d) core. The control system for internal body processes is divided into Central Control System and Local Control System as shown in Figure 1(a). Central control system constitutes upper brain and lower brain sections. Local Control System exchanges information through thermoreceptors located mainly in the skin and in the hypothalamus, but also found on various body parts such as spinal cord, abdomen and thorax. Both control systems mutually exchange information to attain maximum consumer comfort. With changes in the external environment, skin temperature and core temperature vary accordingly. These heat and cold signals are transmitted to deeper fat and muscle layers. Figure 1(b) describes blood-flow in vessels causing either vasodilation or vasoconstriction whenever environment temperature varies. The diameter of the blood-vessels varies accordingly with increased or decreased skin and core temperatures.

Let us now analyze the un-bounded link between the environment, consumer psychology, body dynamics, comfort level, and energy consumption. Environment and consumer psychology are two major inputs to consumer body as shown in Figure 1(c). An external control system is further introduced between consumer surrounding environment and consumer body. The primary objective is to achieve maximum comfort. The brain signals will compel a consumer towards maximum comfort zone. Table 1 summarizes various comfort level indices with respect to energy consumptions. During consumer movement from ‘outdoor-to-indoor’, differences of temperature exists between consumer body and indoor environment. The comfort threshold factor is feedback to controller for generating triggering signals that triggers the actuator. Net energy consumption is the sum of normal energy consumption (energy consumption without consumer body comfort) and energy consumption based on environment-driven consumers body comfort.

Smart Energy System for Human Body Control figure 1

Figure 1: Variation in consumer body with environmental inputs. (a) Central Control System and Local Control System of consumer body. (b) Vasomotion action during steady and transient states. (c) Energy consumption closed-loop feedback system between consumer body and environmental inputs.

Table 1: Thermal Sensation based Comfort Level Indices and Energy Consumption Variations.

Smart Energy System for Human Body Control table 1

This means that when a person is in a comfortable surrounding then appliance-based energy consumption will be minimal. If he/she moves outside in sun’s heat or walks in snowy environment, then his/her body dynamics will vary due to change in body blood-flows. With this principle, when a person moves inside building or room, there exists a temperature difference between consumer body and his/her surrounding environment. The consumer will start switching the heating/cooling appliance and energy consumption will vary.

With ‘long run’ and ‘short run’ climatic drifts effect consumer adaptability, consumer decisions, consumer appliances purchase, and consumer movement towards comfortable environmental regions. Grid policy-makers should include weather-driven-price-plans for enhanced consumer empowerment. Mutual interactions between consumer and utility will be more effective by incorporating: (a) two-way communication (interactions) between consumer body and indoor energy appliances, considering comfort level at highest priority, and (b) building restructuring for bi-directional consumer body, comfort level and energy consumption exchanges. With this model, there will be a positive social impact on society. Moreover, with this implementation, energy exchange models and buildings can be re-designed and re-structured for various home pets, such as dogs and cats. This concept can be further extended to Livestock Farms. In Livestock Farms, when environment is comfortable, irritating behavior of animals can be calmed and their habitats can be enhanced. Furthermore, valuable energy demand outcomes may be drawn out with vacant homes.

For a downloadable copy of the February 2019 eNewsletterwhich includes this article, please visit the IEEE Smart Grid Resource Center  
Sahibzada Muhammad Ali

Sahibzada Muhammad Ali received his Doctoral degree from North Dakota State University, ND, USA. He received his Master’s degree from UET Peshawar, Pakistan. Dr. Ali is currently an Assistant Professor in COMSATS University Islamabad, Abbottabad Campus. He is an active member of Smart Grid Group in the above University. He owns over 80 publications in prestigious Conferences and journals. His research interests include: Smart Grids, utility and prosumers energy management systems, grid-interfaced wind and solar energy models, exact feedback linearization control, and consumers bio-medical interactions for comfort level.

Dr Geev Mokryani

Dr Geev Mokryani is Lecturer in Electrical Power systems and Smart Grids at the Faculty of Engineering and Informatics of the University of Bradford, UK. Before joining the University of Bradford as a lecturer in February 2015, Dr Mokryani was a Postdoctoral Research Associate at Imperial College London for 2 years. His research interests focus on optimisation, planning, operation, and control of distribution networks with high penetration of renewable energy sources and energy storage and risk and resilience analysis of future networks. He has been involved in several smart grids and energy projects including SITARA and PV2025 through national funding bodies and industry. Currently he is principle investigator of a Royal Academy of Engineering project on optimal operation of microgrids with high penetration of renewable energy sources and energy storage systems. He has published 3 books, 2 book chapters and 48 papers in highly prestigious journals and conferences (IEEE Transactions, IET and Elsevier journals). His H-index is 15 according to Google Scholar with greater than 6000 citations. He is associate editor of IET Renewable Power Generation, IET Generation Transmission and Distribution, IET Journal of Engineering and editor of the IEEE Smart Grid Newsletter. Dr Mokryani is Member of CIGRE working group C1.40 and joint working groups C1/C4.36 and C1/C6/CIRED.37. He is also a Senior Member of the IEEE and a Fellow of Higher Education Academy.

Bilal Khan

Bilal Khan did his Ph.D. in Control Systems from Sheffield University UK. He is an Assistant Professor at COMSATS University Islamabad, Abbottabad Campus, Pakistan. Khan’s research interest includes Predictive Control, Nonlinear Control, optimizations and smart grids

Chaudhry Arshad Mehmood

Chaudhry Arshad Mehmood received his Ph.D. in Electrical Engineering from North Dakota State University, US, in 2014. Currently he is an Assistant Professor in the Electrical and Computer Engineering Department of COMSATS University Islamabad, Abbottabad Campus, Pakistan. His research interests include smart grids and AC/DC drives.

Muhammad Jawad

Muhammad Jawad received a BS degree in computer engineering from COMSATS University Islamabad, Lahore Campus, Pakistan in 2007, received the MSc degree in control systems from University of Manchester, UK in 2009, and PhD Degree in electrical and computer engineering from North Dakota State University, Fargo, ND, USA in 2015. Currently, he is working as an assistant professor in the department of electrical and computer engineering of COMSATS University Islamabad, Lahore Campus, Pakistan. His research interests include control system design and analysis, smart grids, power system network analysis, optimization, robustness, and energy efficiency.

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