This study provides an analysis of the potential for a sub-energy system to provide an electricity balancing service to, in this case, a national energy system with a large share of variable renewable electricity generation. By comparing electricity balancing capacity, CO_{2, eq}-emissions, and costs, three different local residential energy system setups are assessed. The setups contain different combinations of district heating, combined heat and power, thermal energy storage, electric battery storage, heat pumps, and electric boilers. The analysis focuses on system-level integration, heat and electricity cross-sectoral operations, and unconventional production strategies for district heating production. The results show that local sub-energy systems with heat pumps, combined heat and power, and thermal energy storage has the potential to reduce national electricity balancing demand in an economically feasible way, and with modest CO_{2, eq}-emissions. It was also shown that electricity-based heat production without district heating is economically unfavourable, even in the most optimistic scenario; it is not likely to be feasible within a 30-year period.

A battery energy storage system (BESS) is an effective solution to mitigate real-time power imbalance by participating in power system frequency control. However, battery aging resulted from intensive charge–discharge cycles will inevitably lead to lifetime degradation, which eventually incurs high-operating costs. This study proposes a deep reinforcement learning-based data-driven approach for optimal control of BESS for frequency support considering the battery lifetime degradation. A cost model considering battery cycle aging cost, unscheduled interchange price, and generation cost is proposed to estimate the total operational cost of BESS for power system frequency support, and an actor–critic model is designed for optimising the BESS controller performance. The effectiveness of the proposed optimal BESS control method is verified in a three-area power system.

In this study, the performance of downlink simultaneous wireless information and power transfer (SWIPT) networks over Nakagami-m fading is analysed. The SWIPT network is modelled as a two-tier heterogeneous network, where one tier is the information transmission network and the other is the power transmission network. The seamless integration enables both data and energy to be transferred from access points to the users. Using the stochastic geometry theory, the expressions for outage probability at the information receiver are derived in decoupled and integrated SWIPT networks. Also, the average harvested energy at the power receiver is derived assuming a non-linear energy harvesting model. Simulation results validate the analytical expressions and the impacts of various system parameters on the SWITP performance are investigated.

The existing solar cell anti-reflection film technology still cannot adequately meet the light trapping needs of solar cells. In this Letter, double-layered SiN _x :H films were prepared for c-Si solar cells by plasma enhanced chemical vapor deposition (PECVD). Herein, the authors introduce a simple, convenient method to lower the reflectance in silicon solar cells by applying double-layered SiN _x :H film to increase the refractive index of such film. Compared to the single layer film devices, the reflectance of the double-layered SiN _x :H film can be significantly reduced by >30% through enhanced absorption of light in solar cells. This method has achieved an average of 0.08% conversion efficiency, with the highest being 0.18%. In addition, the double-layer film solar cells also showed a better passivation performance than that of the single-layer film, so that the minority carrier lifetime was up to 137 µs. Therefore, the improvement of solar cell efficiency mainly come from the decrease of reflectivity and the improvement in film passivation performance. The work of this Letter demonstrated the light trapping advantages and passivation enhancement performance of double-layer films applied to single crystal silicon solar cells.

An adaptive approach based on a neural network is presented here for the control of a grid interactive solar power generating system. This control approach is based on a summation and a product neuron, which are used to process the non-linearity of load currents. A sigmoidal and Gaussian functions are used to linearise the response of summation and product neurons. In this control, the product and summation neurons, and sigmoidal and Gaussian functions are within a single layer, which reduces the complexity of control. This control algorithm is superior over artificial neural network (ANN)-based control algorithm, which has large number of unknown weights and layers. It reduces the computational burden and complexity of the control algorithm. It shows the inherent improved performance at non-linear balanced and unbalanced loads. The control of net active and reactive components of the power separately improves the efficacy of the control at unbalanced non-linear load and reduces the voltage transients in the grid voltages. The ANN-based control also plays an active role in enhancing the power quality of the grid. The reduced complexity of control and direct power feed forward of photovoltaic array (PVA) to the grid improve the dynamic response of the grid-connected PVA-based system.

The acoustic signal transmission over the underwater channel has a limited sum rate and it consumes high power due to the properties of the underwater environment. This study attempts to use the non-orthogonal multiple access (NOMA) technologies for underwater communications. NOMA can be an attractive candidate for underwater communication due to its high spectral efficiency, resistance for carrier frequency offset, and efficient energy consumption. To cope with the hard-recharging capability of the underwater wireless sensor nodes caused by the ocean environment, this study proposes a novel transmission scheme called time-reversed NOMA (TR-NOMA) for underwater communication. In the proposed TR-NOMA, a single-input multiple-output NOMA scheme with a passive-time reversal technique is proposed to reduce the time–frequency dispersion of the underwater acoustic channels. Consequently, simultaneous wireless information and power transfer (SWIPT) can be applied for underwater TR-NOMA. In this study, a SWIPT-NOMA is postposed to harvest energy in downlink transmission from the transmitted signal. The bit error rate (BER) and the outage probability are used to characterise the performance of the proposed TR-NOMA scheme and simulation results show how the proposed TR-NOMA significantly outperforms the conventional NOMA schemes. Additionally, a mathematical framework for the average BER of TR-NOMA is delineated.

This study presents a review of prognostic methods applied to automotive proton exchange membrane fuel cell (PEMFC). PEMFC durability is strongly affected when it is subjected to automotive load cycling (ALC). ALC is normally composed of four operation modes such as start-up, idle, transient high-current demand and shutdown. All of these operation modes drastically change the internal variables of the system like temperature, pressure, relative humidity etc. causing degradation of the fuel cell components in a short time. Prognostic methods could be a possible solution to tackle the PEMFC's low durability issue because they allow predicting the remaining useful life of the system in order to apply preventive maintenance plans. Therefore, the objective of this study is to review the prognostic techniques applied to PEMFC under ALC. In the first part of this study, a summary of PEMFC degradation mechanisms caused by ALC is realised based on literature review. In the second part, the prognostic methods review for automotive PEMFCs is carried out and a general synthesis and future challenges are given in the third part of the study.

The increasing penetration of electric vehicles (EVs) brings challenges and opportunities for power systems. One particular opportunity concerns the use of parked EVs to provide energy and associated services to the grid. In this work, the potential energy storage capacity of parking lots (PLs) of EVs is computed using the proposed stochastic model which considers the sporadic nature of the EV’ behaviours (i.e. arrival/departure, battery degradation, travel pattern, charge/discharge rates). The analysis was performed for two types of PLs with very different occupancy distributions, i.e. a shopping centre PL, and a workplace PL. In both cases, the available energy storage capacity of EVs was estimated hourly using real household travel data, i-MiEV data and car park occupancy records. The results show that the aggregated energy storage capacity closely follows the occupancy of EVs in the PLs, and is substantial, with little sensitivity to charging rate. The proposed stochastic modelling considered the variations in energy consumption, battery degradation, and user behaviour, predicted 13.4% less peak capacity than deterministic modelling. Moreover, the authors conclude that the shopping centre PL is a viable energy resource to the grid, with their scale and throughput compensating for the relatively low occupancy.

An increasing share of electric vehicles can mean excessive peak loads in low-voltage power distribution networks. Introducing peak shaving mechanisms to the charging systems, such overloads can be mitigated significantly. The first contribution of this study is to quantify the amount of flexibility that electric vehicles can contribute to peak load reduction so that the drivers can still fully charge the batteries of their vehicles. The second contribution is that the study presents and compares two optimisation strategies for peak load reduction. The work is based on real charging data covering about 25,000 charging sessions at various charging sites in the metropolitan area of the Finnish capital city. The main finding is that the peak loads at charging sites can be reduced by up to 55%. Another important result is that load reduction through low-power charging is achievable only if the average parking time at the charging site is >3 h, without affecting the user experience negatively. It is also found out that the average parking time is over 2 h longer than the average charging time, which indicates the enormous potential of electric vehicles in peak shaving.

Solar is the Latin word of the sun and solar energy is radiant light and heat from the sun that is a powerful source of energy. Many applications may use solar energy such as heating, cooling, ventilation, illumination, transport, cooking, water heating, water treatment, fuel production, electricity production, energy storage systems (ESSs) and buildings. It is found, statistically, that the amount of solar energy from the sun falling on the earth in one hour is more than that used by everyone in the world in one year. So, it is an important source of renewable energy, and solar technologies are broadly characterized as either active solar or passive solar depending on how they capture and distribute solar energy or convert it into solar power. Large-scale solar thermal systems, concentrating solar power (CSP) technology, can be used for electricity production. To study and analyse a utility grid supported by such active solar energy, it entails understanding how the solar energy is converted into electricity especially when using PV or CSP technologies.Parabolic troughs, linear Fresnel systems and power towers can be coupled to steam cycles of 10-200 MW of electric capacity, with thermal cycle efficiencies of 30%-40%. The values for parabolic troughs, by far the most mature technology, have been demonstrated in the field.