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With further penetration of low-carbon energy conversion, microgrids (MGs) have become a necessary tool for expanding the consumption of renewable energies. In this paper, an optimal operation model for a microgrid-based multi-agent system is proposed. The goal is to save the total energy cost, which is expressed as a sum of locally observable convex functions. Therefore, improving the operational efficiency of microgrids is the key to promoting renewable energy development. This paper develops a three-layer multi-agent system model considering energy storage system and power thermal load demand response to solve the energy management problem of microgrids. In order to investigate the effect of energy storage system and demand response in microgrids, this paper designs three simulation cases, namely infrastructure case, energy storage case and demand response case. In order to prove the effectiveness of the proposed method, this paper uses the proposed method to solve three cases and compare the result with other meta-heuristic algorithms. The comparison results show that: (1) the multi-agent system model can realize the joint optimization of "resource, network, load and storage". (2) The introduction of energy storage and demand response system in microgrids can stabilize the output. Renewable energy units promote the use of renewable energy and reduce the overall operating cost of microgrids. Moreover, the results clearly demonstrate that the proposed algorithm has far better performance than other optimization methods. Also, the analysis obtained from the results shows that the cost is reduced by 1.82%. PV and WT output increased by 14.54% and 2.42%. In addition, their standard deviation decreases after ESS participation. The proposed approach is very effective through a simulation case study, which shows high potential for applications. [ABSTRACT FROM AUTHOR]

This paper investigates conversion of a Nordic oil-heated townhouse into carbon-neutral by different energy efficiency (EE) improvements and an off-grid system including solar photovoltaics (PV), wind power, and battery and hydrogen energy storage systems (BESS and HESS). A heat-pump-based heating system including waste heat recovery (WHR) from the HESS and an off-grid electrical system are dimensioned for the building by applying models developed in MATLAB and Microsoft Excel to study the life cycle costs (LCC). The work uses a measured electrical load profile, and the heat generation of the new heating system and the power generation are simulated by commercial software. It is shown that the EE improvements and WHR from the HESS have a positive effect on the dimensioning of the off-grid system, and the LCC can be reduced by up to €2 million. With the EE improvements and WHR, the component dimensioning can be reduced by 22%–41% and 13%–51% on average, respectively. WHR can cover up to 57% of the building's annual heat demand, and full-power dimensioning of the heat pump is not reasonable when WHR is applied. Wind power was found to be very relevant in the Nordic conditions, reducing the LCC by 32%. [Display omitted] • Energy efficiency improvements notably reduce dimensioning of system components. • Waste heat from FC and electrolyzer covers up to 57% of the annual heat demand. • A full-power-dimensioned GSHP does not reduce the fuel cell dimensioning. • Seasonal storage on a small scale makes the system not economically viable. • Small-scale wind power is expensive, but it significantly reduces the system LCC. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze through simulation how the energy efficiency of a single electrolysis stack at various loads is affected by shunt currents and to determine the energy-optimal load distribution between lines in a multiline electrolysis system under various magnitudes of shunt current losses. A dynamic energy and mass balance model of an industrial 3 MW , 16 bar alkaline water electrolyzer (AWE) process was developed using MATLAB. The optimization goal is to determine the power supply for each AWE line so that it can meet any hydrogen demand while minimizing the global specific energy consumption (SEC). The Particle Swarm Optimization (PSO) algorithm is used to minimize the objective function. According to the results of the single stack investigation, shunt current reduction could significantly improve the energy efficiency of partial-load operation. In addition, the optimization study revealed that whenever two or more lines are required to run in order to satisfy the hydrogen demand, the global SEC is minimized when the lines operate at equal loads. [ABSTRACT FROM AUTHOR]

With the objective to reduce fossil fuels consumption and greenhouse gas emissions, the integration of solar thermal energy into energy-intensive processes represents the most effective solution. This paper investigates the best solar integration scenarios to an industrial dryer unit through an exergy analysis. The exergetic balance for each subsystem is conducted, and a sensitivity parametric analysis was carried out. The optimization using the Genetic Algorithm was then conducted to define the optimum solar operating conditions. The exergetic evaluation showed an important exergy destruction at the dryer and combustion chamber, accounting for 58% and 42% of the inlet exergy flow, respectively. The sensitivity analysis outcomes showed that the exergy efficiency is clearly increased by increasing both drying temperature, decreasing the inlet moisture content, with preheating the combustion air temperature. Considering these variables, the optimization problem enabled enhancing the exergy efficiency by 40% and 30% for the overall process and flash dryer, respectively, when increasing drying temperature to 850 °C, an inlet moisture content of 14.9% and a preheated air combustion of 150 °C. Finally, the solar field could be integrated to the industrial dryer to preheat the ambient air for the combustion chamber to the setpoint temperature of 150 °C. [ABSTRACT FROM AUTHOR]

With the development of fluid transmission and distribution network in the direction of intelligent pipe network, it is urgent to solve the power supply problem of many instruments in the pipe network. However, solving the problem of power supply for intelligent pipe network systems in remote areas and poor site conditions is a challenging task, and the power supply problem can be solved if the fluid energy in the pipe network can be harvested to power instruments such as sensors. Valve is essential equipment in pipe network system, and installing impeller in their spool can harvest fluid energy for power generation. In this paper, in order to improve the ability of the impeller to harvest fluid energy, the blade profile is optimized by parametrically defined method based on the Isight platform. The energy harvesting under different parameters is obtained through CFD simulation software, the Kriging model is established according to the sample data, and the NSGA-II algorithm is used to optimize. After optimization, the impeller shaft power is increased by 7.22%, the energy harvesting efficiency is increased by 7.50%, and the optimal blade profile is obtained. The ability of the impeller to harvest fluid energy is significantly improved. [ABSTRACT FROM AUTHOR]

Filling phase change material (PCM) in the glazed system is crucial to achieving thermal inertia enhancement and solar radiation regulation. However, most previous investigations focused on the energy performance of the PCM glazed system (PCMGS) in typical periods, and the optimization of PCMGS was mainly conducted by control variables to obtain the optimal value of a single parameter, which cannot accurately guide for the long-term application of PCMGS. In this paper, a global optimization program coupled with a numerical model of PCMGS and differential evolution algorithm (DEA) was developed. The annual energy performance of PCMGS was simulated as the objective function to obtain the optimal values of 11 parameters. Subsequently, the climatic applicability of PCMGS was demonstrated by comparing the annual energy performance with that of the traditional double-glazed system (TDGS). The results indicate that the objective function value converges to optimal after 2000 iterations. PCMGS performs preeminent adaptability in hot regions, followed by intermediate regions. The energy consumption of the building when PCMGS is used is reduced by 13.80% and 1.58% respectively compared to the TDGS. However, in cold regions, PCMGS reveals deplorable adaptability, as the energy consumption increased by 36.58% compared to the TDGS. [ABSTRACT FROM AUTHOR]