Model predictive control strategy of energy-water management in urban households

Abstract

The management of energy-water nexus in buildings is increasingly gaining attention among domestic end-users. In developing countries, potable water supply is unreliable due to increasing demand, forcing end-users to seek alternative strategies such as pumping and storage in rooftop tanks to reliably meet their water demand. However, this is at an increased cost of energy cost. In this paper, the open loop optimal control model and the closed-loop model predictive control (MPC) model, both with disturbances, are compared while minimizing the maintenance cost of the pump. The open loop optimal model is suitable in instances where only random disturbances due to measurement errors are present. However, in case the demand pattern changes for reasons such as occupancy change in the house, the closed-loop MPC model is suitable as it robustly minimizes the pumping cost while meeting the customer demand. Further, MPC proves its robustness as it is able to overcome the turnpike phenomenon. Each of these two models has their own strengths. The open loop model is cost effective and easy to implement for customers that have a steady demand pattern while the closed-loop MPC model is more robust against demand pattern changes and external disturbances. It is recommended that these two models are adopted according to the specific application.Keywords: demand management, energy-water nexus, model predictive control, optimal control, time-of-use (TOU)

Optimal energy-water management in urban residential buildings through grey water recycling

“…The performance index when actual values are used in both open loop and MPC controllers leads to 28.61% cost savings compared to the baseline. Therefore, final relative error [44] that closed-loop MPC is more robust and superior than open-loop controller in dealing with disturbances. This however comes at a higher cost and more complexity as it would need extra components to enable the feedback of height of water in the tanks to take place.…”

“…The closed-loop model predictive control (MPC) strategy is formulated in this paper due to its predictive nature, ability to cope with constraints in the design process and the ability to deal with disturbances that are always there in any system, whether external or errors within the system [44]. The closed-loop MPC uses both the feed forward and feed back measurements from the system to compute the control law on-line [45].…”

Optimal control of conventional hydropower plant retrofitted with a cascaded pumpback system powered by an on-site hydrokinetic system

“…Defining a pump switch as a state transition of a pump from off to on state [18], the problem of optimal switching control of a pumping system to minimise the pumping energy demand has been solved in [19,20]. Since pumping energy demand over a given control period is proportional to the number of switches or the cumulative operating hours, a pumping optimisation problem can be formulated to minimise the number of pump switches [21] as well as the cumulative number of operating hours [18]. The main contribution of this work include: (1) The use of alternative hydrokinetic (HK) energy for pumping operation, (2) cascading pumpback operation to minimise pumping power and energy demand for high head applications and (3) minimisation of the wear and tear costs of the pumps by minimising the number of switches of each of the pumps.…”

A Review on Water-Energy Nexus and Directions for Future Studies: From Supply to Demand End

“…WEN is often expanded to include themes such as food, land, and climate change, namely carbon emissions (Rico-Amoros et al, 2009;Bazilian et al, 2011;Yang and Goodrich, 2014;Biggs et al, 2015;Wong and Pecora, 2015;Cairns and Krzywoszynska, 2016;Gallagher et al, 2016;Wanjiru et al, 2016;Vanham, 2016;Wichelns, 2017). As the nexus incorporates more themes, it becomes more difficult to disentangle interconnections.…”

“…WEN studies focusing on the demand end use data collected from the household (Wanjiru et al, 2016;Vieira and Ghisi, 2016), commercial or industrial sectors (Thiede et al, 2016), including tourism (Becken and McLennan, 2017) and municipal sectors, and are highly likely to be conducted using a bottom-up data collection process. This may allow effectively addressing social issues; for example, the relations between energy (fuel) poverty and water poverty, and might be able to find technological means for reducing poverty in water and energy (Vieira and Ghisi, 2016).…”