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This paper addresses the following question: How can smart energy management system (SEMS) influence the residential electricity consumption at both individual household and national level? First, we developed an hourly optimization model for individual households. The energy cost of an individual household is minimized under given assumptions on outside temperature, radiation, (dynamic) electricity price, and feed-in tariff. By comparing the optimization to the reference scenario, we show the impact of SEMS on grid-electricity consumption and photovoltaic (PV) self-consumption at the individual household level. Second, to aggregate the results to the national level, we constructed a detailed building stock taking Austria as an example. By aggregating the results of 2112 representative households, we investigate the impact of SEMS in the residential building stock on the national electricity system. As a result, we found that for individual single-family-houses (SFHs) with PV (no battery) and heat pump adoption, SEMS can significantly reduce the grid-electricity consumption up to 40.7% for a well-insulated building. At the national level we found that, for the buildings with 5 kWp PV but without hot water tank or battery storage, SEMS can still reduce the grid-electricity consumption by 7.4% by using the building mass as thermal storage.

Aim of the paper is to present a theoretical investigation into the energy performance of a novel PV/T module that employs the MPCM (Micro-encapsulated Phase Change Material) slurry as the working fluid. This involved (1) development of a dedicated mathematical model and computer program; (2) validation of the model by using the published data; (3) prediction of the energy performance of the MPCM (Microencapsulated Phase Change Material) slurry based PV/T module; and (4) investigation of the impacts of the slurry flow state, concentration ratio, Reynolds number and slurry serpentine size onto the energy performance of the PV/T module. It was found that the established model, based on the Hottel–Whillier assumption, is able to predict the energy performance of the MPCM slurry based PV/T system at a very good accuracy, with 0.3–0.4% difference compared to a validated model. Analyses of the simulation results indicated that laminar flow is not a favorite flow state in terms of the energy efficiency of the PV/T module. Instead, turbulent flow is a desired flow state that has potential to enhance the energy performance of PV/T module. Under the turbulent flow condition, increasing the slurry concentration ratio led to the reduced PV cells' temperature and increased thermal, electrical and overall efficiency of the PV/T module, as well as increased flow resistance. As a result, the net efficiency of the PV/T module reached the peak level at the concentration ratio of 5% at a specified Reynolds number of 3,350. Remaining all other parameters fixed, increasing the diameter of the serpentine piping led to the increased slurry mass flow rate, decreased PV cells' temperature and consequently, increased thermal, electrical, overall and net efficiencies of the PV/T module. In overall, the MPCM slurry based PV/T module is a new, highly efficient solar thermal and power configuration, which has potential to help reduce fossil fuel consumption and carbon emission to the environment.

Most of the power-to-heat and thermal energy storage technologies are mature and impact the European energy transition. However, detailed models of these technologies are usually very complex, making it challenging to implement them in large-scale energy models, where simplicity, e.g., linearity and appropriate accuracy, are desirable due to computational limitations. In the literature, the main power-to-heat and thermal energy storage technologies across all sectors have not been clearly identified and characterized. Their potential roles have not been fully discussed from the European perspective, and their mathematical modeling equations have not been presented in a compiled form. This paper contributes to the research gap in three main parts. First, it identifies and classifies the major power-to-heat and thermal energy storage technologies that are climate-neutral, efficient, and technologically matured to supplement or substitute the current fossil fuel-based heating. The second part presents the technology readiness levels of the identified technologies and discusses their potential role in a sustainable European energy system. The third part presents the mathematical modeling equations for the technologies in large-scale optimization energy models. We identified electric heat pumps, electric boilers, electric resistance heaters, and hybrid heating systems as the most promising power-to-heat options. We grouped the most promising thermal energy storage technologies under four major categories. Low-temperature electric heat pumps, electric boilers, electric resistance heaters, and sensible and latent heat storage show high technology readiness levels to facilitate a large share of the heat demand. Finally, the mathematical formulations capture the main effects of the identified technologies.

To reach ambitious European CO$_2$ emission reduction targets, most scenarios of future European electricity systems rely on large shares of wind and solar photovoltaic power generation. We interpolate between two concepts for balancing the variability of these renewable sources: balancing at continental scales using the transmission grid and balancing locally with storage. This interpolation is done by systematically restricting transmission capacities from the optimum level to zero. We run techno-economic cost optimizations for the capacity investment and dispatch of wind, solar, hydroelectricity, natural gas power generation and transmission, as well as storage options such as pumped-hydro, battery, and hydrogen storage. The simulations assume a 95% CO$_2$ emission reduction compared to 1990, and are run over a full historical year of weather and electricity demand for 30 European countries. In the cost-optimal system with high levels of transmission expansion, energy generation is dominated by wind (65%) and hydro (15%), with average system costs comparable to today's system. Restricting transmission shifts the balance in favour of solar and storage, driving up costs by a third. As the restriction is relaxed, 85% of the cost benefits of the optimal grid expansion can be captured already with only 44% of the transmission volume. 15 pages, 10 figures, accepted by Energy

Abstract Solar aided coal-fired power system has been proven to be a promising way to utilise solar energy in large scale. In this paper, the performances of the solar tower aided coal-fired power (STACP) system at 100% load, 75% load, and 50% load for different days are investigated and the maximum solar power that the boiler can absorb under different plant loads are explored. Then, the effects of solar multiple (SM) and the thermal energy storage (TES) hour on the daily performance of STACP system are investigated. Results show that the maximum solar power that a 600 MWe boiler can absorb at 100% load, 75% load and 50% load are 76.4 MWth, 54.2 MWth and 23.0 MWth, respectively. Due to the augmented energy from the solar field, the maximum standard coal consumption rate is reduced by 13.53 g/kWh, 12.81 g/kWh and 8.22 g/kWh at 100% load, 75% load and 50% load, respectively. With an increase of solar power input, the boiler efficiency, overall system efficiency and solar thermal-to-electricity efficiency show a downward trend. In addition, the daily coal consumption of summer solstice is the lowest while the winter solstice is the highest for a particular SM and TES hour.

The electrification of transportation is currently seen as one of the key strategies in the sustainable energy transition. This work aims to identify the role of the electrification of transportation from a perspective of the mid-to-long term. The main focus is on the coupling of the deployment of electric vehicles (EV) and power system decarbonization. The paper combines a detailed bottom-up model for the road transportation sector, which features a detailed classification of vehicle types, with the EnergyPLAN tool, a cross-sector and cross-region energy system model of future scenarios consisting of different planning strategies and vehicle charging modes. A case study of the Beijing-Tianjin-Hebei region shows that without increasing renewable power capacity, a 100 % EV penetration can create energy savings and reduce carbon dioxide emissions by a minimum of 11 % in 2050. Moreover, considering the foreseeable technological progress in the battery, a 25 % reduction in the EV initial investment is feasible in all the investigated future scenarios.

Because of their attractive economic and environmental benefits, integrated energy systems (IESs), especially electricity-gas coupled energy systems (EGCESs), have received great interest. In this study, to minimize carbon trading and generation costs, a model-free deep-reinforcement-learning (DRL) method is integrated into the low-carbon economic autonomous energy management system of an EGCES. Unlike previous works, this work proposes an innovative transformer-deep deterministic policy gradient (TDDPG) that combines the superior feature extraction ability of the transformer network with the strong decision-making ability of a state-of-the-art TDDPG. The proposed method is tailored to the specific energy management problem to meet the requirements of multi-dimensional and continuous control. To validate the advantages of the TDDPG, the proposed method is compared with benchmark optimization methods. The simulation results illustrate that TDDPG performs more effectively than the examined DRL approaches in terms of optimizing low-carbon and economy targets, computation efficiency, and optimization of the results. Besides, the TDDPG method achieves lower average comprehensive costs than DDPG and requires less training time for real-time energy scheduling.