Microbial fuel cells (MFCs) have undergone great technological development in the last 20 years, but very little has been done to commercialize them. The simultaneous power production and wastewater treatment are features those greatly increase the interest in the use of MFCs. This kind of distributed power generation is renewable and friendly and can be easily integrated into a smart grid. However, there are some key issues with their commercialization: high construction costs, difficulty in developing high power structures, MFC lifespan, and maintaining a high level of efficiency. The objective of this article is to explore the possibilities of using MFCs in urban wastewater not only regarding the technical criteria of their application, but also mainly from an economic point of view, to determine the conditions through which the viability of the investment is ensured and the possibilities of their integration in a smart grid are identified. Initially, this article explores the implementation/configuration of a power plant with MFCs within an urban wastewater treatment plant on a theoretical basis. In addition, based on the corresponding physical quantities for urban wastewater treatment, the construction and operational costs are determined and the viability of the investment is examined based on classic economic criteria such as net present value, benefit–cost ratio, internal rate of return, and discounted payback period. Furthermore, sensitivity analysis is carried out, concerning both technical parameters, such as the percentage of organic matter removal, power density, sewage residence time, MFC efficiency, etc., and economical parameters, such as the reduction of construction costs due to change of materials, change of interest rate, and lifetime. The advantages and disadvantages of their use in smart grids is also analyzed. The results show that the use of MFCs for power generation cannot be utopian as long as they are integrated into the structure of a central wastewater treatment plant on the condition that the scale-up technical issues of MFCs are successfully addressed.

Daylight utilization significantly contributes to energy savings in office buildings. However, daylight integration requires careful design so as to include variations in daylight availability and maintain a balance between factors such as lighting quality and heat gain or loss. Designers with proper planning can not only improve the visual environment and create higher-quality spaces, but simultaneously minimize energy costs for buildings. The utilization of photosensors can exploit the benefits of daylighting by dimming the lighting system, so that no excessive luminous flux is produced, thus leading to energy savings as well as visual contentment. However, the human factor is crucial for the proper function of a lighting control system. Without its acceptance from the users, energy savings can be minimized or even negligible. The objective of this paper is to present a post-occupancy evaluation regarding occupant satisfaction and acceptance in relation to daylighting in offices equipped with automated daylight controls. In addition, the response of the users was compared with lighting measurements that were performed during the post-occupancy evaluation. Three case studies of office buildings with installed daylight-harvesting systems were examined. The age of the occupants was a crucial factor concerning their satisfaction in relation to the lighting levels. Aged users were more comfortable with lighting levels over 500lx, while young users were satisfied with 300lx. The impact of different control algorithms was outlined, with the integral reset algorithm performing poorly. The acceptance of the users for the closed loop systems maintained the expected energy savings of the daylight harvesting technique. Most of the occupants preferred to use daylight as a light source combined with artificial light but having the control to either override or switch it on and off at will. The results shown that a post-occupancy survey along with lighting measurements are significant for making an office environment a humancentric one.

To describe an artificial neural network (ANN) methodology in order to estimate the critical flashover voltage on polluted insulators is the objective here. The methodology uses as input variables characteristics of the insulator such as diameter, height, creepage distance, form factor and equivalent salt deposit density, and it estimates the critical flashover voltage based on an ANN. For each ANN training algorithm, an optimisation process is conducted regarding the values of crucial parameters such as the number of neurons and so on using the training set. The success of each algorithm in estimating the critical flashover voltage is measured by the correlation index between the experimental and estimated values for the evaluation set, and finally the ANN with the correlation index closest to 1 is specified. For this ANN and the respective algorithm, the critical flashover voltage of the test set insulators is estimated and the respective confidence intervals are calculated through the re-sampling method.

Energy communities constitute the main collective form for energy consumers to participate in the current energy transition. The purpose of this research paper is to present a tool that assists energy communities to achieve fair and sustainable daily operation. In this context, the proposed algorithm (i) assesses the day-ahead operation cost (or profit) of energy communities, taking into consideration photovoltaic (PV) production, battery energy storage system (BESS), and flexible loads, as well as the potential profit from selling energy to the power system, under the net billing scheme, and (ii) compares the derived cost for each member with the cost for non-cooperative operation, as single prosumers. Taking the aforementioned costs or profits into consideration, the developed algorithm then proposes three cost-sharing options for the members, peer-to-peer (P2P), so that their participation in the community is more beneficial than individual operation. The algorithm is tested on a hypothetical energy community in Greece, highlighting the importance of the cooperation amongst the members of the community for their mutual benefit; for the simulated case of different PV shares, the cooperation can result in a 24.5% cost decrease, while having a BESS can reduce the cost by 25.0%.