Notwendigkeit und Vermarktung von Flexibilitätsoptionen im Energiesystem der Zukunft

Demands on production, distribution and consumption of electrical energy change fundamentally with the energy revolution. Energy purchasing costs for inflexible consumers are rising and proceeds of inflexible producers are sinking. Companies are able to reduce costs by marketing operational flexibility options. This article sets out the need of flexibility in the energy system and the new marketing options. Flexibility potentials within infrastructure plants of a chemical park are analyzed, evaluated regarding marketing and activated in the presented research project FlexChemistry.     

Supply chain network design under the risk of uncertain disruptions

Facility disruptions in the supply chain often lead to catastrophic consequences, although they occur rarely. The low frequency and non-repeatability of disruptive events also make it impossible to estimate the disruption probability accurately. Therefore, we construct an uncertain programming model to design the three-echelon supply chain network with the disruption risk, in which disruptions are considered as uncertain events. Under the constraint of satisfying customer demands, the model optimises the selection of retailers with uncertain disruptions and the assignment of customers and retailers, in order to minimise the expected total cost of network design. In addition, we simplify the proposed model by analysing its properties and further linearise the simplified model. A Lagrangian relaxation algorithm for the linearised model and a genetic algorithm for the simplified model are developed to solve medium-scale problems and large-scale problems, respectively. Finally, we illustrate the effectiveness of proposed models and algorithms through several numerical examples.     

Economic Evaluation of Power System Flexibility by Commercial Air Conditioner Control with Large Penetration of Photovoltaic Generation

High penetration of intermittent renewable energy such as photovoltaic (PV) and wind power could cause shortage of power system flexibility. Demand response is expected to help supply ancillary service instead of the conventional power plant. Commercial air conditioners are a promising responsive load for demand response because they account for a large proportion of power consumption in the power system. We calculate a system operation cost and hourly operation pattern of each power plant by using the optimal power generation model considering flexibility supply from controlling commercial air conditioner. We obtained the following results as an effect of commercial air conditioner control. (1) The power generation of oil fired power plants decreases at peak time and annual fuel cost of oil fired plant is reduced by approximately 30% at most in Kanto area. (2) The percentage of rated operation mode of LNG combined cycle plants increases. (3) Curtailed energy rate of PV decreases because a power storage amount by pumped hydropower generation increases. (4) Required battery capacity to reduce PV curtailed amount decreased by combining battery energy storage system in case of high penetration of PV.     

Viscosity reduction of isotonic solutions of the photosensitizer TPCS2a by cyclodextrin complexation

Meso-tetraphenyl chlorin disulphonate (TPCS_2a) is a photosensitizer (PS) particularly developed and patented for use in the technology of photochemical internalization (PCI) against cancer. TPCS_2a is known to aggregate in aqueous media even at low concentrations (≥0.1?µM) and to form a high-viscosity network at clinically relevant concentrations (mM). The aim of this work was to evaluate the effect of two hydroxypropylated cyclodextrin derivatives of beta and gamma type, respectively i.e. HPβCD and HPγCD, on the aggregation and solubilization of TPCS_2a in isotonic solutions. Samples containing micromolar concentrations of TPCS_2a were studied spectrophotometrically, while samples containing a clinical relevant concentration (10?mM?=?9?mg/ml) of TPCS_2a were evaluated by dynamic viscosity measurements. HPβCD was determined to be a more suitable solubilizer of TPCS_2a than HPγCD in aqueous media both in the absence and presence of salt. The complexation stoichiometry between TPCS_2a/HPβCD at micromolar to millimolar concentrations of TPCS_2a was determined to be 1:3 and 1:2 in the absence and presence of isotonic NaCl, respectively. The network of TPCS_2a (10?mM) was broken down in the presence of 3% w/v (=?20?mM) HPβCD, i.e. a 1:2 molar ratio between TPCS_2a and the cyclodextrin. Formation of the inclusion complex resulted in low viscosity samples both in water and in the presence of isotonic NaCl or phosphate buffered saline (PBS) at 25?°C and 37?°C.     

新型冠状病毒疫情对我国电力行业的影响分析

全球经验表明,疫情会对经济社会产生负面影响。此次中国武汉市新型冠状病毒疫情爆发的时间节点、波及的领域范围,以及为应对疫情釆取的相关部署安排,既考验着电力行业保障电力安全可靠供应的能力,也会直接或间接通过电力供求形势变化影响电力企业的生产运行。结合疫情因素和近年来中国经济运行中电力供求变化特点,本文认为疫情将会降低工业和商业用电负荷,医疗机构新增用电和居民生活用电将会显著增长,但鉴于工商业用电比重较高的事实,全社会用电量同期同比增速将明显放缓。考虑到近年来用电增速放缓的经验事实,此次疫情或将放大电力行业积聚的矛盾和困难,因此为发挥市场在电力资源配置中的决定性作用而加快推进电力体制改革和全国统一电力市场建设愈发重要与迫切。     

上海天然气市场和液化天然气的现状与展望

2009年,上海能源供应体系自从引入液化天然气以来,进口液化天然气对全市天然气供应总量和来源多元化做出了重要贡献。2013年,液化天然气消费在全市天然气消费总量中占比达50%,在天然气供应体系中发挥了越来越重要的作用。近年来,上海天然气市场发生了深刻变化,天然气需求增长放缓,与2013年之前十多年的两位数增长形成了鲜明对比。为了更好地理解市场的基本面,首先回顾了上海天然气消费的历史趋势,随后分析了影响需求增长的驱动因素和约束条件,以判断未来天然气需求的趋势和液化天然气在市场中扮演的角色,认为天然气需求增长的潜力主要在民用及商业领域。结合分析可能的供应选择,其结论是未来液化天然气将继续作为上海天然气供应体系的主力气源,同时提供市场急需的灵活性以保障高峰时段的用气需求。     

The potential of energy flexibility of space heating and cooling in Portugal

This study assesses the potential of energy flexibility of space heating and cooling for a typical household under different geographical conditions in Portugal. The proposed approach modifies the demand through the optimization of the thermostat settings using a genetic algorithm to reduce either operational costs or interaction with the grid. The results show that the used energy flexibility indicator expresses the available potential and that flexibility depends on several factors, namely: i) thermal inertia of the archetypical household; ii) the time of use electricity tariffs; iii) users’ comfort boundaries; and iv) the geographical location of the houses.     

Optimal siting and sizing of demand response in a transmission constrained system with high wind penetration

This paper describes algorithms that use demand-side management to address large-scale integration of wind power. In particular, demand response (DR) is used to manage wind power intermittency by shifting the time that electrical power system loads occur in response to real-time prices and wind availability. An economic dispatch with transmission, DR capacity and operational constraints is used to model the operation of a transmission constrained system with a high penetration of wind power. This optimization model is used to determine the optimal sizing and distribution of DR given a fixed budget for customer incentives and the installation of enabling technology. We demonstrate the effectiveness of the operational model based on a simple PJM 5-bus system and an IEEE 118-bus system. Simulation results show that transmission constraints have a greater effect on sizing of DR capacity than the location of wind power, which means that buses electrically close to congested lines tend to have higher incentives to deploy DR resources than other buses. The second part of the work examines optimal siting of technology that enables DR based on the frequency of DR based load changes, which are generally a function of the network location.     

Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing

Although predictive machine learning for supply chain data analytics has recently been reported as a significant area of investigation due to the rising popularity of the AI paradigm in industry, there is a distinct lack of case studies that showcase its application from a practical point of view. In this paper, we discuss the application of data analytics in predicting first tier supply chain disruptions using historical data available to an Original Equipment Manufacturer (OEM). Our methodology includes three phases: First, an exploratory phase is conducted to select and engineer potential features that can act as useful predictors of disruptions. This is followed by the development of a performance metric in alignment with the specific goals of the case study to rate successful methods. Third, an experimental design is created to systematically analyse the success rate of different algorithms, algorithmic parameters, on the selected feature space. Our results indicate that adding engineered features in the data, namely agility, outperforms other experiments leading to the final algorithm that can predict late orders with 80% accuracy. An additional contribution is the novel application of machine learning in predicting supply disruptions. Through the discussion and the development of the case study we hope to shed light on the development and application of data analytics techniques in the analysis of supply chain data. We conclude by highlighting the importance of domain knowledge for successfully engineering features.     

Demand response for home energy management system

The optimization of energy consumption, with consequent cost reduction, is one of the main challenges for the present and future smart grid. Demand response (DR) program is expected to be vital in home energy management system (HEMS) which aims to schedule the operation of appliances to save energy costs by considering customer convenience as well as characteristics of electric appliances. The DR program is a challenging optimization problem especially when the formulations are non-convex or NP-hard problems. In order to solve this challenging optimization problem efficiently, an effective heuristic approach is proposed to achieve a near optimal solution with low computational costs. Different from previously proposed methods in literatures which are not suitable to be run in embedded devices such as a smart meter. The proposed algorithm can be implemented in an embedded device which has severe limitations on memory size and computational power, and can get an optimal value in real-time. Numerical studies were carried out with the data simulating practical scenarios are provided to demonstrate the effectiveness of the proposed method.