The use of air source heat pump systems for space heating and cooling is a convenient retrofitting strategy for reducing building energy costs. This can be combined with the rooftop installation of photovoltaic panels, which can cover, to a significant degree—or even significantly exceed the building’s electricity needs, moving towards the zero energy building concept. Alternatively, increased capacity for rooftop photovoltaic (PV) installation may support the ongoing process of transforming the Greek power system away from the reliance on fossil fuels to potentially become one of the leaders of the energy transition in Europe by 2030. Standard building energy simulation tools allow good assessment of the Heating, Ventilation and Air Conditioning (HVAC) and PV systems’ interactions in transient operation. Further, their use enables the rational sizing and selection of the type of panels type for the rooftop PV installation to maximize the return on investment. The annual performance of a three-zone residential building in Volos, Greece, with an air-to-water heat pump HVAC system and a rooftop PV installation, are simulated in a TRNSYS environment. The simulation results are employed to assess the expected building energy performance with a high performance, inverter driven heat pump with scroll compressor and high efficiency rooftop PV panels. Further, the objective functions are developed for the optimization of the installed PV panels’ area and tilt angle, based on alternative electricity pricing and subsidies. The methodology presented can be adapted to optimize system design parameters for variable electricity tariffs and improve net metering policies.

Short-term load forecasting is an essential instrument in power system planning, operation, and control. It is involved in the scheduling of capacity dispatch, system reliability analysis, and maintenance planning for turbines and generators. Despite the high level of development of advanced types of machine learning models in commercial codes and platforms, the prediction accuracy needs further improvement, especially in certain short, problematic time periods. To this end, this paper employs public domain electric load data and typical climatic data to make 24-hour-ahead hourly electricity load forecasts of the Greek system based on two types of robust, standard feed-forward artificial neural networks. The accuracy and stability of the prediction performance are measured by means of the modeling error values. The current prediction accuracy levels of mean absolute percentage error, mean value μ = 2.61% with σ = 0.33% of the Greek system operator for 2022, attained with noon correction, are closely matched with a simple feed-forward artificial neural network, attaining mean value μ = 3.66% with σ = 0.30% with true 24-hour-ahead prediction. Specific instances of prediction failure in cases of unexpectedly high or low energy demand are analyzed and discussed. The role of the structure and quality of input data of the training datasets is demonstrated to be the most critical factor in further increasing the accuracy and reliability of forecasting.

Understanding the implications of introducing increasing shares of low-carbon technologies such as heat pumps and electric vehicles on the electricity network demand patterns is essential in today’s fast changing energy mixture. Application of heat pumps for heating and cooling, combined with the rooftop installation of photovoltaic panels, is already considered as a convenient retrofitting strategy towards building electrification. This may further profit from the parallel, rapid electrification of the automotive powertrain, as demonstrated in the present study. Exploitation of the combined battery storage of the house owners’ electric car(s) may help cover, to a significant degree, the building’s and cars’ electricity needs. To this end, an efficient single family house’s energy system with an optimized rooftop PV installation, heat pump heating and cooling, and two high efficiency electric cars is studied by transient simulation. The use of TRNSYS simulation environment makes clear the interaction of the house’s heating, ventilation, and air conditioning (HVAC) system, the house’s and cars’ batteries, and the rooftop PV system in transient operation. The building’s and EV’s energy performance on a daily, monthly, and seasonal level is compared with the respective demand curves and energy sources of the Greek electricity network. The specific design of the house’s energy system makes it a net exporter of electricity to the grid, to an annual amount of 5000 kWh. On the other hand, electricity imports are slightly exceeding 400 kWh and limited to the first two months of the year. In addition to the self-sufficiency of the household, the impact to the electricity grid becomes favorable due to the phase shift of the electricity export towards the late afternoon hours, thus assisting the evening ramp-up and adding to the grid’s stability and resilience. Based on the results of this study, the possibility of combining the financial incentives for the purchase of an EV with those for the installation of rooftop PV in the owners’ house is very promising and worth considering, due to the demonstrated synergy of electrical storage with the rooftop photovoltaic installations.

Near zero energy buildings are increasing worldwide, exploiting low-carbon technologies in heating and electricity self-production. Commercial buildings are increasingly considered as candidates for the installation of smart micro-grids, which may profit from the added storage capacity of the batteries of employees electric vehicles, stationed during daytime in their charging lots. Smart exploitation of the interaction of these electricity sources and sinks may prove essential to address the complex electricity network demand patterns in today’s fast changing energy mixture. The interaction of an efficient office building’s energy system with a big rooftop photovoltaic installation and the aggregate storage capacity of 40 electric cars that are connected in the building’s charging lots is studied by means of transient simulation in TRNSYS environment. The 18-zone building’s heating, ventilation, and air conditioning system, the cars’ batteries, and photovoltaic systems’ interactions are analyzed on a monthly, seasonal, and hourly basis, against the respective demand curves of the Greek network. The results suggest that the specific system’s size may profitably support the operation of a smart micro-grid. The total annual electricity consumption of the building is computed to reach 112,000 kWh, or 20 kWh/m²y. The annual electricity needs of the 40 electric cars, amounting to 101,000 kWh, can be fully met with 30% of the photovoltaic electricity production. Thus, the building becomes a net exporter of electricity to the network, with maximum exported electricity occurring daily between 12:00 and 14:00, which is favorable to meeting the demand curve. Thus, the establishment of smart micro-grids in commercial buildings with large rooftop photovoltaic panels’ capacity and a significant number of electric cars in the employees’ car fleet is quite effective in this direction.

The establishment of near-autonomous micro-grids in commercial or public building complexes is gaining increasing popularity. Short-term storage capacity is provided by means of large battery installations, or, more often, by the employees’ increasing use of electric vehicle batteries, which are allowed to operate in bi-directional charging mode. In addition to the above short-term storage means, a long-term storage medium is considered essential to the optimal operation of the building’s micro-grid. The most promising long-term energy storage carrier is hydrogen, which is produced by standard electrolyzer units by exploiting the surplus electricity produced by photovoltaic installation, due to the seasonal or weekly variation in a building’s electricity consumption. To this end, a novel concept is studied in this paper. The details of the proposed concept are described in the context of a nearly Zero Energy Building (nZEB) and the associated micro-grid. The hydrogen produced is stored in a high-pressure tank to be used occasionally as fuel in an advanced technology hydrogen spark ignition engine, which moves a synchronous generator. A size optimization study is carried out to determine the genset’s rating, the electrolyzer units’ capacity and the tilt angle of the rooftop’s photovoltaic panels, which minimize the building’s interaction with the external grid. The hydrogen-fueled genset engine is optimally sized to 40 kW (0.18 kW/kWp PV). The optimal tilt angle of the rooftop PV panels is 39°. The maximum capacity of the electrolyzer units is optimized to 72 kW (0.33 kWmax/kWp PV). The resulting system is tacitly assumed to integrate to an external hydrogen network to make up for the expected mismatches between hydrogen production and consumption. The significance of technology in addressing the current challenges in the field of energy storage and micro-grid optimization is discussed, with an emphasis on its potential benefits. Moreover, areas for further research are highlighted, aiming to further advance sustainable energy solutions.

One of the most promising concentrated solar power technologies is the central receiver tower power station with heliostat field, which has attracted renewed research interest in the current decade. The introduction of the sCO₂ recompression Brayton cycles in the near future installations instead of the Rankine cycle is very probable, due to the prospects of a significant efficiency improvement, process equipment size and capital cost reduction. In this study, energy and exergy analysis of a recompression Brayton cycle configuration for a central receiver power station are performed. Special emphasis is given to the computation of actual performance for the High-Temperature Recuperator and the Low-Temperature Recuperator. The results define realistic thermal and exergetic efficiency limits for the specific cycle configurations applied on a central receiver solar power plant with variable turbine entry temperature. Thermal efficiency, predicted with the improved accuracy of heat exchanger computations, does not exceed the 50% target. Overall, a realizable total power plant efficiency of 37% at 900 K turbine entry temperature is predicted, which is a significant improvement on the current state-of-the-art with steam Rankine cycles.