针对可再生能源消纳问题,研究了氨能与广义储能参与的多能耦合系统低碳协调运行方式,利用氨能和燃煤机组煤氨混燃相结合的技术来优化系统碳排放。首先考虑氨能参与的多能流交互与广义储能的灵活调控特性,构建了以运行费用最小、可再生能源利用率最大、碳排放量最小为目标函数的综合效益评价指标。提出基于能流关联的碳流拓扑分析方法,利用碳流信息作为综合效益评估的辅助决策手段。最后对实际区域系统进行仿真分析,通过氨能与广义储能的协同调控,有效改善了系统的综合效益。

To address the problem of renewable energy consumption, the low-carbon coordinated operation mode of a multi-energy-coupled system with ammonia energy and generalized energy storage was studied. The combination of ammonia energy and a coal-fired unit of coal-ammonia co-combustion was used to optimize the carbon emissions of the system. First, considering the flexible regulation characteristics of the multi-energy flow interaction and generalized energy storage with the participation of ammonia energy, a comprehensive benefit evaluation index with the minimum operating cost, maximum utilization rate of renewable energy, and minimum carbon emission as the objective function was constructed. A carbon flow topology analysis method based on the energy flow correlation was proposed. The carbon flow information was used as an auxiliary decision-making method for a comprehensive benefit evaluation, and a simulation analysis of the actual regional system was performed. The comprehensive benefits of the system were effectively improved through the coordinated control of the ammonia energy and generalized energy storage.

Habit formation is a powerful psychological and physiological process that plays a key role in shaping our behaviors and daily routines. Understanding the science behind how habits are formed and maintained is essential for developing sustainable lifestyle changes, especially in the context of health and wellness. This article explores the underlying mechanisms of habit formation, including the role of the brain, the concept of cues, routines, and rewards, and how these elements can be harnessed to build healthy habits. By breaking down the science of habit formation, we provide a framework for individuals looking to adopt healthier habits one step at a time. This approach encourages incremental changes, making the process of lifestyle transformation more manageable and lasting.

Ammonia production currently contributes almost 11% of global industrial carbon dioxide emissions, or 1.3% of global emissions. In the context of global emission targets and growing demand, decarbonization of this process is highly desirable. We present a method to calculate a first estimate for the optimum size of an ammonia production plant (at the process level), the required renewable energy (RE) supply, and the levelized cost of ammonia (LCOA) for islanded operation with a hydrogen buffer. A model was developed to quantitatively identify the key variables that impact the LCOA (relative to a ±10 GBP/tonne change in LCOA): levelized cost of electricity (±0.89 GBP/MWh), electrolyzer capital expenditure (±65 GBP/kW), minimum Haber–Bosch (HB) load (±12% of rated power), maximum rate of HB load ramping, and RE supply mix. Using 2025/2030 estimates results in a LCOA of 588 GBP/tonne for Lerwick, Scotland. The application of the model will facilitate and improve the production of carbon-free ammonia in the future.

在“双碳”背景下,新型电力系统承载电力系统低碳转型的重要任务。为了更好地促进新能源的消纳,同时有效促进需求响应参与电力系统运行,针对主动配电网的运行优化,提出了基于重力储能和需求响应的主动配电网多目标优化模型。目标函数一考虑主动配电网运行和用户用能的总成本,目标函数二选取主动配电网的等效碳排放成本。由于风光出力的不确定性,利用鲁棒优化理论将不确定优化模型转换为确定性优化模型,同时构建了基于NSGA-Ⅱ算法的运行优化求解方法。最后,在三种场景下对模型进行求解,结果表明系统配置重力储能,同时开展需求响应除了能减少碳排放量之外,还能达到削峰填谷的目的,系统有较好的运行经济性。

Under the background of “double carbon target ”, the new power system carries the important task of low-carbon transformation of power system. In order to better promote the absorption of new energy and effectively promote the demand response to participate in the operation of power system, this paper proposes a multi-objective optimization model of active distribution network considering gravity energy storage and demand response. Objective function 1 considers the total cost of active distribution network operation and user energy consumption, and objective function 2 is selected as the equivalent carbon-emission cost of active distribution network. Due to the uncertainty of wind power output, the uncertain optimization model is converted to the deterministic optimization model by using the robust optimization theory, and the operation optimization solution method based on NSGA-Ⅱ algorithm is constructed. Finally, the model is solved in three scenarios. The results show that the system equipped with gravity energy storage and the demand response at the same time can not only reduce carbon emissions, but also achieve the purpose of peak-cutting and valley-filling, and the system has better operation economy.

This paper focuses on the potential use of ammonia as a carbon-free fuel, and covers recent advances in the development of ammonia combustion technology and its underlying chemistry. Fulfilling the COP21 Paris Agreement requires the de-carbonization of energy generation, through utilization of carbon-neutral and overall carbon-free fuels produced from renewable sources. Hydrogen is one of such fuels, which is a potential energy carrier for reducing greenhouse-gas emissions. However, its shipment for long distances and storage for long times present challenges. Ammonia on the other hand, comprises 17.8% of hydrogen by mass and can be produced from renewable hydrogen and nitrogen separated from air. Further more, thermal properties of ammonia are similar to those of propane in terms of boiling temperature and condensation pressure, making it attractive as a hydrogen and energy carrier. Ammonia has been produced and utilized for the past 100 years as a fertilizer, chemical raw material, and refrigerant. Ammonia can be used as a fuel but there are several challenges in ammonia combustion, such as low flammability, high NOx emission, and low radiation intensity. Overcoming these challenges requires further research into ammonia flame dynamics and chemistry. This paper discusses recent successful applications of ammonia fuel, in gas turbines, co-fired with pulverize coal, and in industrial furnaces. These applications have been implemented under the Japanese 'Cross-ministerial Strategic Innovation Promotion Program (SIP): Energy Carriers'. In addition, fundamental aspects of ammonia combustion are discussed including characteristics of laminar premixed flames, counterflow twin-flames, and turbulent premixed flames stabilized by a nozzle burner at high pressure. Furthermore, this paper discusses details of the chemistry of ammonia combustion related to NOx production, processes for reducing NOx, and validation of several ammonia oxidation kinetics models. Finally, LES results for a gas-turbine-like swirl-burner are presented, for the purpose of developing low-NOx single-fuelled ammonia gas turbine combustors. (C) 2019 The Authors. Published by Elsevier Inc. on behalf of The Combustion Institute.

在系统受到扰动而引起频率下降的暂态过程中,如果瞬时频率过低,将会触发不必要的低频减载,导致系统的供电可靠性降低。储能由于其具有快速功率调节能力,可以避免不必要的暂态低频减载。首先,基于电力系统等值频率响应模型提出了适合储能容量配置的具有分段函数约束的多目标动态优化模型,旨在同时考虑储能配置成本和暂态频率调节性能2个相互冲突的目标。然后,采用大M法和隐式梯形积分法将多目标动态优化模型转化为多目标混合整数二次规划模型。采用规格化法平面约束法和CPLEX求解器获得其帕累托最优解。最后,基于IEEE 24节点系统的仿真计算验证了所提多目标储能配置模型的有效性。

During the frequency decline process of a power system subject to a disturbance, unnecessary under-frequency load shedding will be triggered if the instantaneous frequency is too low. This will reduce the power system reliability. Energy storage can be used to mitigate the unnecessary transient under-frequency load shedding due to its fast power regulation ability. According to the equivalent frequency response model of power systems, this paper proposes a multi-objective dynamic optimization model with piecewise function constraints which is suitable for the configuration of energy storage capacity. This model aims to consider two conflict objectives at the same time, such as the cost of energy storage configuration and the performance of transient frequency regulation. The big- M method and implicit trapezoid integration method are used to transform the multi-objective dynamic optimization model into a multi-objective mixed integer quadratic programming model. Then the normalized normal constraint method and CPLEX solver are used to obtain the Pareto optimal solution. Finally, the effectiveness of the proposed multi-objective optimization model of energy storage capacity configuration is validated with the simulation result on the IEEE 24-bus system.