Electricity Authority of Cyprus (Cyprus)

Access to health data for patients is hindered by a fragmented healthcare system and the absence of unified, patient-centric solutions. Additionally, there are no mechanics for easy sharing of medical records with healthcare providers, risking incomplete diagnoses. To further intensify the problem, when patients seek care abroad, language barriers may prevent foreign doctors from understanding their health data, further complicating treatment. Our study presents the development and evaluation of a mobile application designed to enable users to access and share their health records directly from their device, in multiple languages, ensuring ease of use and convenience. The solution utilizes OpenNCP for translating patient summaries into multiple languages and the FHIR Smart Health Links Protocol for secure sharing. We conducted a user acceptance study with 45 participants to evaluate our mobile app's interface and functionality. The feedback was positive, highlighting the app's user-friendliness and usefulness. The participants felt it would enhance communication between physicians and patients and the features of sharing and translating are going to give more control of their medical data to the patients. Based on the results and participants feedback, our mobile solution significantly enhances healthcare accessibility and efficiency by enabling easy access and sharing of health records in multiple languages, using relevant protocols and standards, reducing medical errors and ensuring personalized care.

An effort to highlight the role of space charges inside dielectrics and their propagation is made in the present paper. Solid insulating materials in various parts of high voltage devices and machines are crucial for the insulating performance and their reliable functioning duration. Space charge distributions and space charge propagation in the form of pulses, called charge packets, are studied in this paper pointing out on one hand the initiating mechanisms and on the other hand the propagation mechanisms of charges inside dielectrics, as they are introduced in the scientific literature. Experimental data are used from other papers, mainly those referring to space charge distributions versus space and simultaneously versus time in a classification attempt of the aforementioned parameters.

Deregulation of electricity markets has ushered in a new era of heightened competition, allowing for the inclusion of fresh market entrants. However, market participation bears challenges related the extremely high volatility of prices that are affected by too many interrelated factors, such as weather conditions, production, consumption, renewable production, fuel prices, unexpected socio-political and health events, etc. Therefore, the electricity market shows unexpected changes that can lead to very high (extremely high) or very low levels (negative) prices, exposing the participants to high financial risks. Given the stochastic nature of electricity energy prices, this work employs the Extreme Learning Machine in conjunction with the Bootstrap method for forecasting electricity prices for the next day. Two distinct cases are examined. In the first case, the prediction model is trained only with historical market price data, while in the second one with forecast data. The reason this is done is to see which data gives better forecasts. The methodology was applied to German and Finnish market data, 2019–2022.

We examine the use of state-of-the-art distributed sensing systems to extract temperature information from the optical fibre infrastructure already of the Electricity Authority of Cyprus power distribution network (~25-year old installation); as a means of optical fibre distributed sensing in the underground cables. The optical fibres are collocated with existing power distribution cables, for the purpose of power line monitoring cable joints that are prone to failure, along with general monitoring for unusual behaviour and potential cable fault conditions. Detection is achieved using DTS: Distributed Temperature Sensors (Silixa Ltd) that use RAMAN-based measurements in combination with BOTDR (Brillouin Optical Time-domain Reflectometry) for high-precision temperature detection. We examine the correlation between the temperature of the power cable with the power consumption provided by the EAC and the weather conditions. Furthermore, our data will give an indication of how important is uniform spacing between power and optical cables. The real-time and continuous monitoring of the temperature of the optical cables through the distributed sensing systems may help identifying abnormal cable behavior (hot spots) and possible future network failures in the power network.

The increasing integration of variable renewable technologies at distribution feeders, mainly solar photovoltaic (PV) systems, presents new challenges to grid operators for accurately forecasting demand. This renders the transitioning from load to net load forecasting (NLF) imperative. A new methodology was proposed in this paper for direct short-term NLF at the distribution level, using a Bayesian neural network model. The proposed model was optimized with decision heuristics based on a statistical post-processing stage (i.e., clustering of daily irradiance patterns) for improved performance. Model validation was performed using historical numerical weather predictions and net load data from three distribution feeders (with PV shares ranging from 2.5% to 34.2%) in Cyprus. The optimally constructed model achieved high forecasting accuracies, exhibiting normalized root mean square error (nRMSE) <5% when applied to the distribution feeders. Statistical post-processing further improved the model's forecasting accuracy, achieving nRMSE values <1.3%. Finally, the results demonstrated the suitability of the NLF methodology for distribution feeders with diverse PV penetration shares, rendering the proposed method applicable to distribution system operators for decision making and efficient planning.

ABSTRACT By 2030 Cyprus committed to reduce greenhouse gas emissions by 40% and increase renewables’ energy share by 19% according to the European engagements. Difficulties appear due to the continuous increase in domestic energy consumption, the large dependency on fossil fuels and the adverse climate. A number of macroeconomic, demographic and climatic indicators that influence Cyprus’s electricity consumption (EC) was analysed for years 2000–2018 using augmented Dickey–Fuller unit root test and the autoregressive distributed lag model (ARDL). The ARDL model revealed that an increase in population could radically increase EC in the long-run and short-run. Results posit that a 1% increase in urban population, electricity price and unemployment decrease domestic EC in the long-run by 17.25%, 0.48% and 0.30%, respectively. In the short-run smaller elasticities were found because time was too short for adjustments to be made. However, their decrease could not outweigh the population growth effect on increasing domestic EC.

Electrical energy storage (EES) constitutes a potential candidate capable of regulating the power generation to match the loads via time-shifting. Optimally planned, EES facilities can meet the increasing requirement of reserves to manage the variability and uncertainty of renewable energy sources (RES) whilst improving the system operation efficiency and economics. In this work, the impact of intermittent RES on total production cost (TPC) is evaluated in the presence and absence of storage, using annual data regarding the non-interconnected power system of the island of Cyprus. Performing weekly simulations for the entire year of 2017, TPC is computed by solving the unit commitment based on a constrained Lagrange Relaxation method. Seven selected EES technologies are modeled and evaluated via a life-cycle cost analysis, based on the most realistic technical and cost data found in the literature. The results derived from the uncertainty analysis performed, show that zinc-air (Zn-air) battery offers the highest net present value (NPV). Lead-acid (Pb-acid) and sodium-sulfur (Na-S) are considered viable solutions in terms of mean NPV and investment risk. Lithium-ion (Li-ion) battery exhibits a particularly expensive choice. Dominated by its increased capital cost which still governs its overall cost performance Li-ion achieves a negative mean NPV far below zero. However, to strengthen the benefits derived from EES integration, further research and development is needed improving the performance and costs of storage. The uncertainty governing the majority of EES technologies, in turn, will be reduced, increasing their participation and RES contribution in autonomous power system operations.