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Heating, ventilation and air-conditioning (HVAC) systems constitute the majority of the demands in modern power systems for aggregated buildings. However, HVAC integrated with renewable energy sources (RES) face notable issues, such as uneven demand–supply balance, frequency oscillation and significant drop in system inertia owing to sudden disturbances in nearby generation for a longer period. To overcome these challenges, load frequency control (LFC) is implemented to regulate the frequency, maintain zero steady-state error between the generation and demand, reduce frequency deviations and balance the active power flow with neighboring control areas at a specified value. In view of this, the present paper investigates LFC with a proposed centralized single control strategy for a micro-grid (µG) system consisting of RESs and critical load of a HVAC system. The proposed control strategy includes a newly developed cascaded two-degree-of-freedom (2-DOF) proportional integral (PI) and proportional derivative filter (PDF) controller optimized with a very recent meta-heuristic algorithm—a modified crow search algorithm (mCSA)—after experimenting with the number of performance indices (PICs). The superiority of both the proposed optimization algorithm and the proposed controller is arrived at after comparison with similar other algorithms and similar controllers, respectively. Compared to conventional control schemes, the proposed scheme significantly reduces the frequency deviations, improving by 27.22% from the initial value and reducing the performance index criteria (ƞISE) control error to 0.000057. Furthermore, the demand response (DR) is implemented by an energy storage device (ESD), which validates the suitability of the proposed control strategy for the µG system and helps overcome the challenges associated with variable RESs inputs and load demand. Additionally, the improved robustness of the proposed controller for this application is demonstrated through sensitivity analysis with ±20% μG coefficient variation.

Thermal energy storage (TES) is a technology that stocks thermal energy by heating or cooling a storage medium so that the stored energy can be used at a later time for heating and cooling applications and power generation. TES systems are used particularly in buildings and in industrial processes. This paper is focused on TES technologies that provide a way of valorizing solar heat and reducing the energy demand of buildings. The principles of several energy storage methods and calculation of storage capacities are described. Sensible heat storage technologies, including water tank, underground, and packed-bed storage methods, are briefly reviewed. Additionally, latent-heat storage systems associated with phase-change materials for use in solar heating/cooling of buildings, solar water heating, heat-pump systems, and concentrating solar power plants as well as thermo-chemical storage are discussed. Finally, cool thermal energy storage is also briefly reviewed and outstanding information on the performance and costs of TES systems are included.

Thermoelectric materials are solid-state energy converters whose combination of thermal, electrical, and semiconducting properties allows them to be used to convert waste heat into electricity or electrical power directly into cooling and heating. These materials can be competitive with fluid-based systems, such as two-phase air-conditioning compressors or heat pumps, or used in smaller-scale applications such as in automobile seats, night-vision systems, and electrical-enclosure cooling. More widespread use of thermoelectrics requires not only improving the intrinsic energy-conversion efficiency of the materials but also implementing recent advancements in system architecture. These principles are illustrated with several proven and potential applications of thermoelectrics.

PurposeGround source heat pump (GSHP) utilizes shallow geothermal energy to meet the heating and cooling needs of buildings. It has drawn global attention owing to its environmental friendliness and high energy efficiency. China ranks second and first in terms of the installed capacity and energy use of GSHPs, respectively. This study aims to explore the environmental impacts of GSHP systems in China to identify the key improvements and provide recommendations for optimizing the environmental performance.MethodsThe environmental impact assessment was conducted on the basis of a life cycle assessment framework with the application of the ReCiPe 2016 method. The system boundary of the investigated system was established by applying a cradle-to-gate approach and involved the installment and operational stages. The functional unit was defined as 20 years of heating and cooling by applying GSHP.Results and discussionResults showed that the potential impacts of GSHP systems were mainly concentrated in global warming and human health at the midpoint and endpoint levels, respectively. These environmental burdens were dominated by carbon dioxide emissions from the electricity generation process. Polyethylene pipe production provided additional contributions to partial categories. The comparative analysis results indicated that the energy consumption and carbon emissions of the GSHP system were reduced by 40.53% and 35.23%, respectively, in the entire life cycle compared with those of coal-fired heating and air conditioner cooling systems.ConclusionsFindings of this study indicated that GSHPs could effectively reduce energy consumption and carbon emissions compared with conventional heating and cooling systems. To improve the environmental performance of GSHP systems further, applying renewable energy as electricity sources and substituting polyethylene pipes with steel pipes are suggested.

This study evaluated the performance of a hybrid heat pump system integrating photovoltaic–thermal (PVT) panels with a standing column well (SCW) geothermal system in a strawberry greenhouse. The PVT panels, installed over 10% of the area of a 175 m3 greenhouse, stored excess solar heat in an aquifer to offset the reduced efficiency of the geothermal source during extended operation. The results showed that the hybrid system can supply 11,253 kWh of heat energy during the winter, maintaining the night time indoor temperature at 10 °C even when outdoor conditions dropped to −10.5 °C. The PVT system captured 11,125 kWh of solar heat during heating the off season, increasing the heat supply up to 22,378 kWh annually. Additionally, the system generated 3839 kWh of electricity, which significantly offset the 36.72% of the annual pump system electricity requirements, enhancing the system coefficient of performance (COP) of 3.38. Strawberry production increased by 4% with 78% heating cost saving compared to a kerosene boiler system. The results show that the PVT system effectively supports the geothermal system, improving heating performance and demonstrating the feasibility of hybrid renewable energy in smart farms to enhance efficiency, reduce fossil fuel use, and advance carbon neutrality.

The smart grid paradigm and the development of smart meters have led to the availability of large volumes of data. This data is expected to assist in power system planning/operation and the transition from passive to active electricity users. With recent advances in machine learning, this data can be used to learn system dynamics. This study explores two model-free reinforcement learning (RL) techniques – policy iteration (PI) and fitted Q-iteration (FQI) for scheduling the operation of flexibility providers – battery and heat pump in a residential microgrid. The proposed algorithms are data-driven and can be easily generalised to fit the control of any flexibility provider without requiring expert knowledge to build a detailed model of the flexibility provider and/or microgrid. The algorithms are tested in multi-agent collaborative and single-agent stochastic microgrid settings – with the uncertainty due to lack of knowledge on future electricity consumption patterns and photovoltaic production. Simulation results show that PI outperforms FQI with a 7.2% increase in photovoltaic self-consumption in the multi-agent setting and a 3.7% increase in the single-agent setting. Both RL algorithms perform better than a rule-based controller, and compete with a model-based optimal controller, and are thus, a valuable alternative to model- and rule-based controllers.

Waste heat recovery is common in high‐temperature water electrolysis production, but the latent heat from the hydrogen–water mixture is not fully utilized, and the electric heater to generate steam consumes much energy. In this research, a cascade heat pump is proposed to recover the latent heat from electrolysis products and generate steam. The heat pump can save as much as 65% electric energy compared with a single electric heater. Although in some cases, the latent heat is not enough to generate sufficient steam, it can still save 46.1% of energy. Also, this research emphasizes the significant influence of hydrogen and water proportion in electrolysis products. Compared with 80% water proportion, 50% water proportion can save 1.67 MW energy just in the water vaporization process for a 10 MW electrolyzer. The payback period is 3.43 years, which makes it worth investing.

Focusing on the electricity generation performance of a direct expansion solar PVT heat pump system, a novel T‐type optimized collector/evaporator was designed. The experiment used refrigerant R410a as the working medium and was conducted under the average winter ambient temperature of 16°C in Fuzhou. Through experimental comparison, the differences in panel temperature, electricity generation, and efficiency under different radiation intensities were tested against traditional PV panels and honeycomb PVT panels. The experimental results showed that the T‐type PVT system exhibited significant advantages under various weather conditions. The back panel temperature was reduced by 12.74, 8.52, and 13.06°C compared to traditional PV panels and honeycomb PVT panels, respectively, and it maintained a stable increase in electricity generation. The maximum instantaneous electricity output increased by 56.6% relatively, and the total electricity generation increased by 9.3% to 14.5% compared to traditional PV panels. The photoelectric efficiency reached 21.54% and 22.6%, surpassing traditional PV panels and honeycomb PVT panels, with measured values relatively increased by 1.7%, 2.38%, and 2.76%, respectively. Economic evaluation indicated that the T‐type PVT system could save on self‐generated electricity costs, achieving savings of up to 386.50 and 210.81 yuan/year compared to traditional PV panels and honeycomb PVT panels. The T‐type PVT system demonstrated clear advantages in self‐generated electricity costs, significantly reducing costs for application scenarios and providing strong support for the practical application of renewable energy technologies. Focusing on the electricity generation performance of a direct expansion solar PVT heat pump system, a novel T‐type optimized collector/evaporator was designed. The experiment used refrigerant R410a as the working medium and was conducted under the average winter ambient temperature of 16°C in Fuzhou. Through experimental comparison, the differences in panel temperature, electricity generation, and efficiency under different radiation intensities were tested against traditional PV panels and honeycomb PVT panels. The experimental results showed that the T‐type PVT system exhibited significant advantages under various weather conditions. The back panel temperature was reduced by 12.74, 8.52, and 13.06°C compared to traditional PV panels and honeycomb PVT panels, respectively, and it maintained a stable increase in electricity generation. The maximum instantaneous electricity output increased by 56.6% relatively, and the total electricity generation increased by 9.3% to 14.5% compared to traditional PV panels. The photoelectric efficiency reached 21.54% and 22.6%, surpassing traditional PV panels and honeycomb PVT panels, with measured values relatively increased by 1.7%, 2.38%, and 2.76%, respectively. Economic evaluation indicated that the T‐type PVT system could save on self‐generated electricity costs, achieving savings of up to 386.50 and 210.81 yuan/year compared to traditional PV panels and honeycomb PVT panels. The T‐type PVT system demonstrated clear advantages in self‐generated electricity costs, significantly reducing costs for application scenarios and providing strong support for the practical application of renewable energy technologies.

High temperature Proton-Exchange Membrane Fuel Cells (PEMFC) can replace fossil fuels based heat generation units thanks to their co-produced heat, depending on temperature levels required by users. Indeed, PEMFC can be seen as cogenerative units which can be exploited to mitigate CO2 emissions and improve overall system cogeneration efficiency. Nonetheless, their technical limit is mainly due to the temperature level which can be achieved, besides economic feasibility in the present scenario. In this sense, the aim of the work is to compare different heating providing technologies, including PEMFC, Heat Pumps (HP) and Heat Only Boilers (HOB) to benchmark those simple solutions against PEMFC systems integration with HP or HOB. Levelized cost of electricity (LCOE) and Levelized cost of heat (LCOH) are assessed, in order to conduct a fair comparison. Moreover, the overall cogeneration efficiency of different integrated systems relying on the single technologies is evaluated. The technical and economic comparison is performed as function of power capacity and required temperature, ranking the technology by suitability. Indeed, this can limit the applicability of PEMFC integrated with HP, thus leading to a lower overall cogeneration efficiency. Several fuel composition and cost are used to assess the economic impact of the compared solutions. Finally, a sensitivity analysis is performed to assess present and future scenario result uncertainty. PEMFC based CHP systems feature a higher cogeneration efficiency, but their economic feasibility turns out to be still challenging. In near future they might be competitive with traditional solutions.

Renewable energy sources are extensively studied due to their clean and sustainable nature. Human energy needs can be classified into four categories: heating, cooling, electricity, and transportation. Heating and electricity have garnered more research attention because of their larger share and the variety of technologies used. To address electricity supply issues, societies have considered solar energy, but its reliance on various factors, especially climate, means it doesn't always meet needs in some regions. This study proposes integrating solar thermal and photovoltaic (PV/T), and ground source heat pump (GSHP) systems, controlled dynamically to adjust the share of renewable energy supplied. The system is tested in a residential apartment across four different climatic cities in Iran to assess individual and collective climate impacts. Results indicate that solar ST contributes 100% in hot and humid climates, 59.2% in hot and dry, 30.1% in temperate and humid, and 32% in cold climates. Meanwhile, PV contributes 2057, 2986, 3441, and 4188 kWh/year for electricity and heating demand, respectively. In Chabahar, with its hot and humid climate, solar alone meets energy needs. However, in Yazd, with its hot and dry climate, about 85% of energy comes from the solar system and geothermal heat pump, resulting in economic efficiency of $4965.1 and CO2 reduction by 10.4 million tons annually. This study recommends further research on different financial assumptions, life cycle environmental impact assessments, various technology integrations, and larger consumer bases to fully understand climate impacts.