Policy and new technologies are transforming the energy landscape in the UK. Centralised control of electrical generation and unidirectional distribution have a finite part in a sustainable energy system. Subsidies have encouraged an increase in distributed resources. At the same time closure of larger fossilfuelled power plants is reducing system inertia on energy networks. In this study, a decentralised proactive approach to demand-side response exploiting building thermal inertia is presented using machine learning methods and a real-time adaptation algorithm. This paper proposes a dynamic 2-step energy consumption prediction scheme that can be configured to provide efficiency opportunities and the potential to reduce energy costs in buildings. The approach adopted optimises energy usage through existing demand-side response mechanisms utilising decentralised frequency regulation. The paper concludes with a discussion on the future direction of research.
This paper explored the possibility of applying Demand Side Management (DSM) strategies in meeting the energy demands of rural healthcare centres in Africa. Using an energy management system, it is possible to increase renewable energy penetration, optimize energy costs and reduces carbon emissions. DSM is a process of managing energy consumption to optimize available and planned resources for power generation. The DSM strategies employed in this study incorporates all activities that influence the rural healthcare facilities use of electricity, hence leading to manageable demand. The main focus of this study is on feasibility of increasing penetration of off-grid hybrid renewable energy in delivering basic healthcare services in rural areas with limited or no electricity access. Renewable energy resources (RES) such as solar is considered abundant in many rural places, it is also environmental friendly, hence suitable for providing electricity in rural healthcare facilities where there is no electricity access or limited supply. To meet the facility’s energy need, an optimum PV-Gen-battery hybrid system was designed using HOMER (Hybrid Optimization of Multiple Electric Renewables), with COE of $0.224/kWh, Net Present Cost (NPC) of $61,917.6 and initial capital cost of $16,046.5. DSM measures were applied to reduce the peak and average demand. With this new load profile, an optimum hybrid system was obtained, producing a COE of $0.166/kWh, NPC of $18,614.7 and initial capital cost of $10,070.8 after the DSM. The cost saving realized for the considered rural healthcare center is $0.057/kWh, representing 25.8% reduction from the current COE and 70% reduction in Total NPC. The research provides novel insights which may be applicable worldwide. It has the potential to significantly advance the development of high-quality and timely evidence to underpin current and future developments in the rural energy sector and contribute to the implementation of SDG7..
This paper presents the development of a digital Proportional Integral Derivative (PID)-like adaptive controller and its application on Heating, Ventilating and Air Conditioning (HVAC) systems. The HVAC process model is often approximately described as a first-order-plus-deadtime (FOPDT) model, with process parameters which can vary with time due to changing operating conditions, nonlinearities and other environmental factors. By using the recursive least squares (RLS) algorithm, upto-date estimates of the process parameters can be adaptively obtained while the system operates. A simple but effective design method for an adaptive control strategy in such a situation is described in this paper. The design method easily compensates a time delay and is robust to non-minimum phase behaviours. Based on the estimated model parameters, the overall control strategy is then able to adaptively track the setpoint with a pre-specified response without needing to be retuned or reconfigured later if the operating conditions vary. As HVAC systems sometimes have a zero, an implementation of the proposed control algorithm is applied to minimum and non-minimum phase HVAC models, and favourable results were obtained in comparison with another adaptive control scheme found in literature. The digital PID-like adaptive control algorithm was also applied to PT326 – Heat Process Trainer, and a good control performance was obtained.
This study investigates a particular hybrid configuration of Indirect Expansion Photovoltaic Thermal Heat Pump (IEPVT/HP) system based on a detailed thermodynamic and heat transfer analysis. The effect of solar irradiance on the electrical and thermal efficiencies of the system, PVT temperature and the water outlet temperature is studied. Results show that an increase in solar irradiation increases the temperature of the PVT and thus decreases the electrical efficiency of the PVT. It was found that a greater solar irradiance corresponds to an increase in the thermal efficiency of the PVT. For a fixed change in solar irradiance the variance in the thermal efficiency decreases as the solar irradiance increases.
Since Membrane Distillation (MD) is an energy-intensive separation process, therefore, it is vital to consider a realistic measure of energy evaluation for the MD system. Exergy analysis of Xzero f ...
