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Abstract Integration of photovoltaics (PV) with electrical energy storage (battery) is a straightforward approach to turn intermittent power source into stable power supply. Power coupling, or power matching, between PV-device, a battery, and a load is most frequently performed with aid of maximum power point tracking (MPPT) electronics. MPPT electronics provides high flexibility as for PV and load impedances, and irradiance, however, it brings in additional cost, and complexity, power overhead, potential reliability issues, and interference signals. On the other hand, direct coupling via preselection of PV and battery parameters is a simple scalable and highly efficient alternative to MPPT for a specific set of conditions. We explore with modeling how far a directly coupled PV-battery unit can stay power-matched under various conditions, and demonstrate feasibility of excellent power matching over orders of magnitude of irradiance and a wide range of load resistances. Both a PV-harvester in an office room with low irradiance, non-demanding load, and high autonomy, and a PV-system on a roof with high irradiance, demanding load, and partial autonomy, can operate efficiently without MPPT electronics if an appropriate battery is included. This result emphasizes the role of a battery as an impedance matching element besides storage functionality in a directly matched PV-system.

Abstract Integration of solar photovoltaic (PV) and battery storage systems is an upward trend for residential sector to achieve major targets like minimizing the electricity bill, grid dependency, emission and so forth. In recent years, there has been a rapid deployment of PV and battery installation in residential sector. In this regard, optimal planning of PV-battery systems is a critical issue for the designers, consumers, and network operators due to high number of parameters that can affect the optimization problem. This paper aims to present a comprehensive and critical review on the effective parameters in optimal planning process of solar PV and battery storage system for grid-connected residential sector. The key parameters in process of optimal planning for PV-battery system are recognized and explained. These parameters are economic and technical data, objective functions, energy management systems, design constraints, optimization algorithms, and electricity pricing programs. A timely review on the state-of-the-art studies in PV-battery optimal planning is presented. The challenges, trends and latest developments in the topic are discussed. At the end, scopes for future studies are developed. It is found that new guidelines should be provided for the customers based on various electricity rates and demand response programs. Also, several design considerations like grid dependency and resiliency need further investigation in the optimal planning of PV-battery systems.

A membrane assisted process for green hydrogen production from a bioethanol derived feedstock is here developed and evaluated, starting from the conventional Steam Methane Reforming (SMR) process. Such a process is suitable for centralized hydrogen production, and is here analyzed for a large-scale H2 production unit with the capacity of 40.000 Nm3/h. The basic Steam Ethanol Reforming (SER) process scheme is modified in a membrane assisted process by integrating the Pd-membrane separation steps in the most suitable reaction steps. The membrane assisted process, configured in three alternative architectures (Open architecture, Membrane Reactor and Hybrid architecture) was evaluated in terms of efficiencies and hydrogen yields, obtaining a clear indication of improved process performance. The alternative membrane assisted process architectures are compared to the basic SER process and to the benchmark SMR process fed by natural gas, for an overall comparative assessment of the efficiency and specific CO2 emissions and for an economic analysis based on the operating expenditures.

Abstract The upward energy demand, along with the depletion of conventional energy sources, demands improved utilization of renewable energy resources. Among all renewable energy resources, solar energy is the most appropriate alternative to conventional energy sources owing to its inexhaustibility and green property. Solar collectors are devices that convert solar radiation into heat or energy. However, the efficiency of the solar collector is still not adequate. The competent step to enhance the efficiency of the solar collector is to use nanofluids. This study is carried out different phases viz. characterization and stabilization while both qualitative and quantitative methods used to evaluate the stability of nanofluids thermophysical properties of Al2O3 and CNC nanofluids such as thermal conductivity measured at four different temperature using KD2 Pro, viscosity and specific heat determined at similar temperature range by viscometer and differential scanning calorimetry respectively. The experiment is executed with a fixed flow rate and in steady-state conditions under extensive solar radiation. The experimental study has revealed that up to 2.48% and 8.46% efficiency of solar collector enhanced by using 0.5% Al2O3 and 0.5% CNC nanofluids respectively. Moreover, nanofluids show good to moderate stability performance. Besides, the thermal conductivity of nanofluids increased while viscosity is in a decreasing trend with increasing temperature. Nanofluids could enhance the efficiency of a flat-plate solar collector.

AbstractA common control strategy for the production of recombinant proteins in Pichia pastoris using the alcohol oxidase 1 (AOX1) promotor is to separate the bioprocess into two main phases: biomass generation on glycerol and protein production via methanol induction. This study reports the establishment of a soft sensor for the prediction of biomass concentration that adapts automatically to these distinct phases. A hybrid approach combining mechanistic (carbon balance) and data‐driven modeling (multiple linear regression) is used for this purpose. The model parameters are dynamically adapted according to the current process phase using a multilevel phase detection algorithm. This algorithm is based on the online data of CO2 in the off‐gas (absolute value and first derivative) and cumulative base feed. The evaluation of the model resulted in a mean relative prediction error of 5.52% and R² of .96 for the entire process. The resulting model was implemented as a soft sensor for the online monitoring of the P. pastoris bioprocess. The soft sensor can be used for quality control and as input to process control systems, for example, for methanol control.

AbstractSmart Grids are electrical grids that require a decentralised way of controlling electric power conditioning and thereby control the production and distribution of energy. Yet, the integration of Distributed Renewable Energy Sources (DRESs) in the Smart Grid introduces new challenges with regards to electrical grid balancing and storing of electrical energy, as well as additional monetary costs. Furthermore, the future smart grid also has to take over the provision of Ancillary Services (ASs). In this paper, a distributed ICT infrastructure to solve such challenges, specifically related to ASs in future Smart Grids, is described. The proposed infrastructure is developed on the basis of the Smart Grid Architecture Model (SGAM) framework, which is defined by the European Commission in Smart Grid Mandate M/490. A testbed that provides a flexible, secure, and low-cost version of this architecture, illustrating the separation of systems and responsibilities, and supporting both emulated DRESs and real hardware has been developed. The resulting system supports the integration of a variety of DRESs with a secure two-way communication channel between the monitoring and controlling components. It assists in the analysis of various inter-operabilities and in the verification of eventual system designs. To validate the system design, the mapping of the proposed architecture to the testbed is presented. Further work will help improve the architecture in two directions; first, by investigating specific-purpose use cases, instantiated using this more generic framework; and second, by investigating the effects a realistic number and variety of connected devices within different grid configurations has on the testbed infrastructure.

Abstract The need to increase energy system flexibility, alongside the need to lower fossil fuel use in the food sector, and the importance of refrigeration infrastructure presents an opportunity for Liquid Air Energy Storage (LAES) integrated with refrigerated warehouses. To quantify this opportunity in Europe we analyse energy scenarios and existing refrigeration infrastructure for four countries with diverse energy systems (UK, Spain, Bulgaria and Germany). We find that with growing levels of electricity generation from variable renewable sources and numerous refrigerated warehouses, LAES has the potential to provide value in many areas of the EU through the 2020s. However, LAES is still pre-commercial, and with the proportion of electricity from variable renewable sources still low in many countries it is likely that LAES will not be deployed widely alongside refrigerated warehouses under current market conditions. Countries such as the UK and Spain, which have the greatest need for additional energy system flexibility and the most refrigerated warehouses are likely to gain the most value initially.