Когнитивно-креативное назначение режима «диалога» в работе с художественным текстом на уроках русского языка как иностранного (на примере рассказа А. П. Чехова «Тоска»)

In this paper, a stochastic model predictive control (SMPC) approach to integrated energy (load and generation) management is proposed for a microgrid with the penetration of renewable energy sources (RES). The considered microgrid consists of RES, controllable generators (CGs), energy storages and various loads (e.g., curtailable loads, shiftable loads). Firstly, the forecasting uncertainties of load demand, wind and photovoltaic generation in the microgrid as well as the electricity prices are represented by typical scenarios reduced from a large number of primary scenarios via a two-stage scenario reduction technique. Secondly, a finite horizon stochastic mixed integer quadratic programming model is developed to minimize the microgrid operation cost and to reduce the spinning reserve based on the selected typical scenarios. Finally, A SMPC based control framework is proposed to take into account newly updated information to reduce the negative impacts introduced by forecast uncertainties. Through a comprehensive comparison study, simulation results show that our proposed SMPC method outperforms other state of the art approaches that it could achieve the lowest operation cost

An Integration Mechanism between Demand and Supply Side Management of Electricity Markets

One of the main challenges in the emerging smart grid is to jointly consider the demand and supply, which is also reflected in the wholesale market (supply side) and the retail market (demand side). When integrating the demand and supply side into one framework, the mechanism for determining the market clearing price has been changed. This is due to the demand variations in the demand side in response to the market clearing price and the change of generation costs in the supply side from the demand variation. In order to find the best balance between the supply and demand under the demand response management scheme, this paper proposes a new integrated supply and demand coordination mechanism for the electricity market and smart pricing methods for generator and retailers. Another important contribution of this paper is to develop an efficient algorithm to find the match equilibrium between the demand and supply sides in the new proposed mechanism. Experimental results demonstrate that the new mechanism can effectively handle unpredictable demand under dynamic retail pricing and support the ISO to dispatch the generation economically. It can also help in achieving the goals of dynamic pricing such as maximizing the profits for retailers

Process Knowledge-guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems

Various real-world problems can be attributed to constrained multi-objective optimization problems. Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for constrained multi-objective optimization problems. Given this, a process knowledge-guided constrained multi-objective autonomous evolutionary optimization method is proposed. Firstly, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving constrained multi-objective optimization problems.The National Key R&D Program of China, the National Natural Science Foundation of China, Shandong Provincial Natural Science Foundation, Fundamental Research Funds for the Central Universities and the Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235hj2023Electrical, Electronic and Computer Engineerin

Optimal dynamic pricing for smart grid having mixed customers with and without smart meters

This paper investigates an optimal day-ahead dynamic pricing problem in an electricity market with one electricity retailer and multiple customers. The main objective of this paper is to support the retailer to make the best day-ahead dynamic pricing decision, which maximizes its profit under the realistic assumption that mixed types of customers coexist in the electricity market where some customers have installed smart meters with the embedded home energy management system in their home whereas other customers have not installed smart meters. To this end, we propose a hybrid demand modelling framework which firstly uses an optimal energy management algorithm with bill minimization to model the behavior of customers with smart meters and secondly use a data-driven demand modelling method to model the behavior of customers without smart meters. Such a hybrid demand model can not only schedule usages of home appliances to the interests of customers with smart meters but also be used to understand electricity usage behaviors of customers without smart meters. Based on the established hybrid demand model for all customers, a profit maximization algorithm is developed to achieve optimal prices for the retailer under relevant market constraints. Under the condition of no growth of the revenue (i.e. no increase of total bill from all customers), simulation results indicate our optimization algorithm can improve the profit for around 5% on average

Multiple Dynamic Pricing for Demand Response with Adaptive Clustering-based Customer Segmentation in Smart Grids

In this paper, we propose a realistic multiple dynamic pricing approach to demand response in the retail market. First, an adaptive clustering-based customer segmentation framework is proposed to categorize customers into different groups to enable the effective identification of usage patterns. Second, customized demand models with important market constraints which capture the price-demand relationship explicitly, are developed for each group of customers to improve the model accuracy and enable meaningful pricing. Third, the multiple pricing based demand response is formulated as a profit maximization problem subject to realistic market constraints. The overall aim of the proposed scalable and practical method aims to achieve 'right' prices for 'right' customers so as to benefit various stakeholders in the system such as grid operators, customers and retailers. The proposed multiple pricing framework is evaluated via simulations based on real-world datasets

Dynamic Clustering Analysis for Driving Styles Identification

For intelligent driving systems, the ability to recognize different driving styles of surrounding vehicles is crucial in determining the safest, yet more efficient driving decisions especially in the context of the mixed driving environment. Knowing for instance if the vehicle in the adjacent lane is aggressive or cautious can greatly assist in the decision making of ego vehicle in terms of whether and when it is appropriate to make particular manoeuvres (e.g. lane change). In addition, vehicles behave differently under different surrounding environments, making the driving styles identification highly challenging. To this end, in this paper we propose a dynamic clustering based driving styles identification and profiling approach where clusters vary in response to the changing surrounding environment. To better capture dynamic driving patterns and understand the driving style switch behaviours and more complicated driving patterns, a position-dependent dynamic clustering structure is developed where a driver is assigned to a cluster sequence rather than a single cluster. To the best of our knowledge, this is the first research paper of its kind on the dynamic clustering of driving styles. The usefulness of the proposed method is demonstrated on a real-world vehicle trajectory dataset where results show that driving style switches and more complex driving behaviours can be better captured. The potential applications in intelligent driving systems are also discussed

Mechanical reliability and oxygen permeationof Ce0.8Gd0.2O2-δ-FeCo2O4 dual phase membranes

Dual phase oxygen transport membranes, consisting of ionic and electronicconducting phases, exhibit great potential in high-purity oxygen generation due totheir high stability under harsh application atmospheres. Oxygen-ion conductivefluorite oxides (e.g. Ce0.8Gd0.2O2-δ) and electron conductive spinel phases (e.g.FeCo2O4) are promising material candidates for such a dual phase oxygen transportmembrane. Mechanical properties (e.g. elastic modulus, hardness, strength andsubcritical crack growth behaviour) and oxygen permeation of the membrane areimportant parameters regarding reliability for future applications. These parametershave close relationships with composition and microstructural characteristics, likegrain size, phase distribution and defects (e.g. microcracks). However, theserelationships are currently not fully understood. Therefore, in this thesis, theinfluence of composition, grain size and microstructural defects on mechanicalproperties are investigated for Ce0.8Gd0.2O2-δ-FeCo2O4 membranes. Millingprocedures during powder fabrication and ceramic sintering profiles are optimizedto overcome the formation of unfavorable microstructural defects ..

Mechanical reliability and oxygen permeation of Ce0.8Gd0.2O2-δ-FeCo2O4 dual phase membranes

Dual phase oxygen transport membranes, consisting of ionic and electronic conducting phases, exhibit great potential in high-purity oxygen generation due to their high stability under harsh application atmospheres. Oxygen-ion conductive fluorite oxides (e.g. Ce0.8Gd0.2O2-δ) and electron conductive spinel phases (e.g. FeCo2O4) are promising material candidates for such a dual phase oxygen transport membrane. Mechanical properties (e.g. elastic modulus, hardness, strength and subcritical crack growth behaviour) and oxygen permeation of the membrane are important parameters regarding reliability for future applications. These parameters have close relationships with composition and microstructural characteristics, like grain size, phase distribution and defects (e.g. microcracks). However, these relationships are currently not fully understood. Therefore, in this thesis, the influence of composition, grain size and microstructural defects on mechanical properties are investigated for Ce0.8Gd0.2O2-δ-FeCo2O4 membranes. Milling procedures during powder fabrication and ceramic sintering profiles are optimized to overcome the formation of unfavorable microstructural defects. Furthermore, the effects of grain size and phase distribution on oxygen permeation are discussed for a 85 wt% Ce0.8Gd0.2O2-δ-15 wt% FeCo2O4 membrane

Appliance level demand modeling and pricing optimization for demand response management in smart grid

In this paper, we propose a distributed optimization algorithm for the demand response management with a comprehensive customer demand modeling framework in the smart grid. Different from the existing literature, the considered demand modeling framework considers not only the energy management modeling but also the appliance-level usage pattern learning models, both for time-shiftable loads. More specific, a bill minimization based demand optimization model is firstly proposed for the customers choosing to use a home energy management software. Secondly, an appliance level probability behaviour model via calculating the probability distribution of different electricity consumption patterns in response to the dynamic prices is proposed for the customers choosing to manage their energy usages by themselves. Based on the optimization and learning results, we further propose a multi-population genetic algorithm based pricing optimization model for demand response management with the aim to maximize the retailer's profit and maximize customers' benefits. Numerical results indicate the applicability and effectiveness of the proposed models and its benefits

A bilevel optimization approach to demand response management for the smart grid

This paper proposes a hybrid approach to optimal day-ahead pricing for demand response management. At the customer-side, a comprehensive energy management system, which includes most commonly used appliances and an effective waiting time cost model is proposed to manage the energy usages in households (lower level problem). At the retailer-side, the best retail prices are determined to maximize the retailer's profit (upper level problem). The interactions between the electricity retailer and its customers can be cast as a bilevel optimization problem. To overcome the infeasibility of conventional Karush-Kuhn-Tucker (KKT) approach for this particular type of bilevel problem, a hybrid pricing optimization approach, which adopts the multi-population genetic algorithms for the upper level problem and distributed individual optimization algorithms for the lower level problem, is proposed. Numerical results show the applicability and effectiveness of the proposed approach and its benefit to the retailer and its customers by improving the retailer's profit and reducing the customers' bills