In this work, a photovoltaic (PV) system integrated with a non-inverting DC-DC buck-boost converter to extract maximum power under varying environmental conditions such as irradiance and temperature is considered. In order to extract maximum power (via maximum power transfer theorem), a robust nonlinear arbitrary order sliding mode-based control is designed for tracking the desired reference, which is generated via feed forward neural networks (FFNN). The proposed control law utilizes some states of the system, which are estimated via the use of a high gain differentiator and a famous flatness property of nonlinear systems. This synthetic control strategy is named neuro-adaptive arbitrary order sliding mode control (NAAOSMC). The overall closed-loop stability is discussed in detail and simulations are carried out in Simulink environment of MATLAB to endorse effectiveness of the developed synthetic control strategy. Finally, comparison of the developed controller with the backstepping controller is done, which ensures the performance in terms of maximum power extraction, steady-state error and more robustness against sudden variations in atmospheric conditions. Full article

The City of Surrey in British Columbia, Canada has recently launched a district energy network (DEN) to supply residential and commercial buildings in the Surrey Centre area with hot water for space and domestic hot water heating. The network runs on natural gas boilers and geothermal exchange. However, the City plans to transition to low-carbon energy sources and envisions the DEN as a key development in reaching its greenhouse gas emissions (GHG) reduction targets in the building sector. Harvesting and utilizing waste heat from industrial sites using a mobile thermal energy storage (M-TES) is one of the attractive alternative energy sources that Surrey is considering. In this study, a techno-economic analysis (TEA) was conducted to determine the energy storage density (ESD) of the proposed M-TES technology, costs, and the emission reduction potential of integrating waste heat into Surrey’s DEN. Three transportation methods were considered to determine the most cost-effective and low-carbon option(s) to transfer heat from industrial waste heat locations at various distances (15 km, 30 km, 45 km) to district energy networks, including: (i) a diesel truck; (ii) a renewable natural gas-powered (RNG) truck, and (iii) an electric truck. To evaluate the effectiveness of M-TES, the cost of emission reduction ($/tCO2e avoided) is compared with business as usual (BAU), which is using a natural gas boiler only. Another comparison was made with other low carbon energy sources that the city is considering, such as RNG/biomass boiler, sewer heat recovery, electric boiler, and solar thermal. The minimum system-level ESD required to makes M-TES competitive when compared to other low carbon energy sources was 0.4 MJ/kg. Full article