Advances in Green Energy Systems and Smart Grid

Load forecasting is a basic work of power system dispatching. With the rapid development of smart grid technology, the accuracy of load forecasting is put forward by increasing demand. Fusion load, weather and other multi-sourced data, a refined load forecasting method of support vector machine (SVM) based on data mining is proposed. Firstly, the history load data is clustered and the operation days are divided into six categories. Then, the load data and weather data such as humidity and temperature are fused together, a refined load forecasting model based on data mining is proposed. And the parameters of the model are optimized globally. Forecasting the load of a prefecture-level city in Zhejiang Province in 2013, the load prediction error of sampling point and daily average load forecasting rate are used as indexes to evaluate the prediction accuracy, the prediction results show that the prediction accuracy of the support vector machine (SVM) refined load forecasting method based on historical data and real-time influencing factors proposed in this paper is significantly higher than that of the traditional load forecasting method.

The steam generator is one of the most important equipment in nuclear power plants. The water level control of steam generators is a challenging problem due to its complicated characteristics. This paper studies a novel intelligent virtual reference feedback tuning method based on human learning optimization (IVRFTH) and applies it to the water level control, in which the optimal controller can be directly designed without knowing the mathematical model of the controlled object. The simulation results show that the developed IVRFTH surpasses the standard IVRFT method with the introduction of human learning optimization (HLO). As IVRFTH is easy to design the optimal controller without the model information, it is very promising for the engineering application.

Aiming at the problem of variable speed constant frequency (VSCF) in wind power system, a design scheme of power split type compound hydraulic mechanical continually variable transmission (HMCVT) used in wind power system is proposed. With variable speed input and constant speed output as the premise, the design scheme combines the characteristics of hydraulic high power density, stepless speed change and mechanical high efficiency by means of power split. Its static speed and efficiency properties are theoretical analyzed. Combined with the actual working conditions, the Matlab/Simulink software is used for modeling the HMCVT wind power system, dynamic simulation and analysis. Finally, the transmission test platform had been built, and the experimental data and simulation results are basically the same. The results show that the HMCVT wind power system can realize VSCF. Its dynamic characteristics are good, and efficiency can be stabilized at around 87%.

This paper is concerned with wind power accommodation strategy by considering user satisfaction and demand response dispatch economic costs. Firstly, price-based demand response dispatch economic costs model and user satisfaction model are established, which are incorporated into a multi-objective optimization model. Then, multi-objective function is transformed into single objective function by the normalized method, which is solved by the sequential quadratic programming method. Finally, simulation is operated on the modified IEEE-30 nodes distribution network, and simulation results show that the proposed strategy can successfully eliminate the wind fluctuations in a certain range.

The digital pressure control device of Pressurizer, which is equipped with the input Module of selecting median from four inputs, was used as the research object, then a fault tree including static part and dynamic part of the device is established. Next, the Method of Markov was used to analyze the dynamic part. The overall fault tree was analyzed according to the overall logic. And then the Bayesian method is used to obtain the current reliability index of the equipment based on the DCS operating data as input value of the fault tree. Finally own capability and the effect on entire device of input module of selecting median from four inputs is evaluated from the value of reliability and importance, the result shows that the input module is relatively highly optimized.

In order to suppress the torsional vibration caused by electric vehicle low-damping characteristic, a driveline simulation model was constructed in Matlab/Simulink based on permanent magnet synchronous motor(PMSM) using vector control strategy. A fuzzy self-tuning PID controller was designed to improve speed feedback and robustness of motor controller. The results show that the dynamic characteristics of electric vehicle driveline can be revealed effectively by electro-mechanical coupling method. The application of fuzzy logic method to tune PID controller can contribute a lot to solve torque ripple problem caused by mutation load and mutation control signal.

The purpose of this paper is to explore the sustainable route of China’s new energy vehicles with sustainable business model. It is based upon a case study of the new energy vehicles leader: BYD. In order to compare the different periods of business model of the example, business model canvas and triple layered business model canvas are proposed. Through the use of case studies drawn from real-world situations, BYD improve its technology to capture the market, innovate its business model to break the bottleneck. Other companies should learn from the merits of BYD’s new energy vehicles development. The originality of the paper is that we compare the Business model canvas and Triple layered business model canvas and present a sustainable business approach, triple layered business model canvas, for new energy vehicle firms.

Accurate knowledge of the state-of-charge (SOC) parameter estimated for electric vehicle (EV) batteries is of particular importance when calculating the EV energy demand. Many factors and methods have been proposed for SOC estimation. However, often methods focus on the battery itself rather than customer usage and related factors. This paper proposes a EV energy demand estimation method based on SOC, where onboard power electronics and weather conditions are considered. A practical mathematical model is proposed for large-scale EV provision in which a correction factor is included for calculation of actual maximum range. Typical values from real EVs and statistics are used to exemplify the model in a case study. The results presented in the paper indicate that with inclusion of weather factors and use of onboard technology, electricity consumption is greater and consequently the actual range of an EV is shortened and the EV energy demand is quantitatively increased.

This paper is concerned with bi-level joint planning model of distributed generation (DG) and electric vehicle charging station (EVCS) siting and sizing under active management (AM) mode. Firstly, the joint planning model including DG and EVCS is established by taking the lowest annual comprehensive cost as the upper level objective. Then, the lower level model optimizes DG output curtailment by taking the uncertainty of load, EVCS charging load, and the intermittent DG output of wind farm and photovoltaic generator into account. According to the bidirectional interaction of upper and lower levels, the bi-level planning model is optimized by biogeography-based optimization (BBO) and primal-dual interior point method (PDIPM) respectively. Finally, simulation is operated on the revised IEEE-33 nodes distribution network, and simulation results show that the joint planning of DG and EVCS can obtain better planning scheme.

The time-varying characteristics of electric vehicle (EV) controllable energy and the rationality of frequency regulation (FR) task allocation have significant influences on participating in system FR. Considering the various types of EV, the controllable quantities and the real-time controllable energy of EVs are simulated and calculated. On the basis of the real-time changes of EV’s controllable energy, the real-time allocating scheme of system FR coefficient is put forward. The simulation results show that the real-time adjustment of load frequency control (LFC) model based on dynamic controllable energy of EV can effectively suppress the system frequency deviation; under the same total battery energy, the electric private car participates in the system FR for the longest time, the real-time controllable energy of which is the largest; the real-time allocating ratio of system FR has a wide range of fluctuation, and the FR effect under real-time allocation mode is better than that under the fixed allocation mode.

In the research of Vehicle-to-grid (V2G), the large-scale EVs needs to be aggregated to participate in the charging/discharging strategy. Aiming at the problem of electric vehicles (EVs) in aggregator participating in dispatching plan, this paper proposed an orderly scheduling strategy of the EV in the aggregator (Vehicle-to-Aggregator, V2A). Under the condition of real-time price, an index evaluation system of the EVs has been established, which consider the randomness and credit of EV users. This paper first analyzed the impact of the declaration information on the schedule plan. With the consideration of declared scheduling capacity, EV user’s credit, battery loss and the degree of participation as the evaluation index, the evaluation index system of the EV aggregator is proposed. Then, the weight of each index is determined, which uses the method of combination weighting based on the Accelerated Genetic Algorithm. Then the scheduling priority of EVs in the aggregator can be obtained. Finally, combining with the dispatching plan of power grid, the actual scheduling capacity of aggregators at different nodes in each period is determined. The simulation results show that the strategy proposed in this paper can consider the influence of various indexes of EVs on schedule, and effectively realize the dispatching plan for aggregators.

As the key subject of the green manufacturing system, the construction of eco–factory has become an important content in order to achieve the sustainable development of enterprises. In this paper, a technological updating decision-making model is established based on dynamic programming (DP) for eco–factory. Firstly, the evaluation index system for eco–factory is established. Secondly, the local weight and global weight of each index are calculated based on analytic network process (ANP) and Delphi method. Finally, the decision–making model of eco-factory is established to find the optimal investment plan by using the method of logarithmic fitting and DP. The ANP and Delphi method present a great potential in solving complex and ambiguous problems and the decision-making based on DP can obtain the better ecological benefits through an example of a foundry factory, therefore the feasibility of the proposed method is proved.

The environmental impacts of manufacturing systems can be improved by reducing the energy consumption of machine tools. To meet the requirements for reducing energy consumption of machine tools, it is critical to monitor and analyze energy usage of machine tools. However, machine tools are composed of multiple energy consumers and the requirements towards monitoring energy of machine tools are different. This paper proposes a configurable on-line monitoring system to obtain energy data for the specific energy consumption components of machine tools, which can be used to analyze energy characteristics considering different energy monitoring requirements and different machine tools. The case study of the system implemented in lathe and machining center shows that the system can be used to monitor energy consumption of different types of machine tools and satisfy different monitoring requirements of energy consumption. The results can provide a better understanding of energy and support refined energy management in the manufacturing system.

Based on the consideration of the system primary energy utilization and renewable energy consumption, the energy efficiency evaluation indices of multi-energy system of an industrial park is proposed. The weight of each index is determined by the information entropy principle, and the comprehensive energy efficiency evaluation index and evaluation method of the multi-energy system of industrial park are also established. Considering the change of energy demand in typical days of summer, winter and transition season, the two operating strategies of “Electric Load Following” and “Thermal Load Following” are used to simulate multiple system operating scenarios. Based on the established index system, the comprehensive energy efficiency of each scenario is calculated and its comprehensive energy efficiency is evaluated for an industrial park in Beijing. The case study shows that the comprehensive evaluation model and evaluation method of energy efficiency can fully reflect the characteristics of multi-energy complementary operation and cascading energy utilization in the park, which has practical significance for the design and operation of multi-energy system.

So far, the connotation and system of Green supply chain Management (GSCM) have already had quite a lot of content, and its theoretical research has also achieved fruitful results. However, at present, the connotation and system of Green supply chain Management still lack the support of information foundation, so it is difficult to monitor, analyze and evaluate the implementation of Green supply chain Management. Based on the connotation and system of green supply chain management, this thesis discusses how to build the information base of Green supply chain Management, and puts forward a new information integration framework and operation mode of green supply chain. It lays a foundation for realizing the connotation and system of Green supply chain Management and the integration of enterprise management information system, and provides a way to solve the problem that the Green supply chain Management system is difficult to monitor, analyze and evaluate.

In order to ensure the high voltage electrical equipment working efficiently and safely, a wireless temperature monitoring system with low power consumption was designed based on C8051F930 and Si4438. The system includes the host and the slave,both of which are composed of main control module, wireless communication module, power module and so on. The slave is installed in electric equipment, and it sends temperature data to the host through wireless communication. When the temperature exceeds the preset value, the system sends warning information. The system has the advantages of low power consumption, high precision, long distance and low cost, which enables users to remote temperature monitoring of high-voltage electrical equipment in time.

The reliable state of charge (SOC) estimation is indispensable for flow batteries to maintain the safe and reliable operation. The widely adopted Extended Kalman filter (EKF) algorithm is a model-based method, however, the uncertainties in battery model will cause large errors in SOC estimation. An accurate battery model is the essence to capture the behaviors of batteries. In this paper, a novel framework for the SOC estimation of Zinc-nickel flow batteries is proposed based on the fast recursive algorithm (FRA) and extended Kalman filter (EKF). The FRA is firstly used to determine the model structure and identify the model parameters. Due to merits of FRA, a compact and accurate model of flow battery is built. Then, the SOC is estimated using the EKF based on the proposed linear-in-the-parameter model. Experimental studies and resultant simulations manifest the modelling accuracy of the proposed SOC estimation framework.

Based on the fact that the penetration of renewable energies is increasing dramatically, almost all the energy markets have changed and taken action to present the strategy of real-time pricing over the last decade. However, the research on how these renewables, which is going to become the vital part in the integrated energy system, coordinate with other energy sources under the real-time pricing. Moreover, with the development of energy storage system, issues about how to evaluate the participation of them in the integrated energy system and how to provide an optimal capability for them in a given settings should be given more consideration. This paper will focus on introducing an approach to coordinate the participation of all the energy resources in the integrated energy systems within and without energy storage system.

The Lithium-ion battery is one of the most common batteries used in Electric Vehicles (EVs) due to the specific features of high energy density, power density, long life span and environment friendly. With the development of lithium-ion battery technology, different materials have been adopted in the design of the cathodes and anodes in order to gain a better performance. \(LiMn_{2}O_{4}\), \(LiNiMnCoO_{2}\), \(LiNiCoAlO_{2}\), \(LiFePO_{4}\) and \(Li_{4}Ti_{5}O_{12}\) are five common lithium-ion batteries adopted in commercial EVs nowadays. The characteristics of these five lithium-ion batteries are reviewed and compared in the aspects of electrochemical performance and their practical applications.

High performance Phase Change Materials (PCMs) play a vital role in Thermal Energy Storage (TES) technologies. A cost-effective and easy-controllable fabrication process by mix-sintering method is an effective approach to produce composite PCMs at a large scale. In this work, a series of form stable composite PCMs with different phase change temperatures were prepared by mix-sintering method. These composite PCMs could be applied in a cascading manner in thermal storage system. DSC measurements and analyses show that the cascading system has an energy storage density of 1068.96 J/g within a working temperature range from 50 °C to 550 °C. Besides, an SEM study shows homogenous microstructure of the prepared composite PCMs.

According to the characteristics of the smart grid, in order to solve the problem of subjective hierarchical boundary cloud model and the problem of real-time data collection, this paper proposes an objective cloud matter element analysis based on cluster analysis. According to the development and planning, this paper puts forward an index system of the smart grid comprehensive development level. Because of non-national-standard data, this paper classifies the data into several levels according to cluster analysis, and then gets each hierarchical boundary cloud model of each index by calculating, then the correlation degree between the sample data and the hierarchical boundary cloud model is calculated; By combining ANP and principal component analysis method, the integrated weight vector is obtained and the weighted evaluation result is obtained. By multiple operations, the evaluation is ordered by using the standard deviation. The model illustrates the validity and superiority of the method.

With the rapid increase of the installed capacity and generation capacity of renewable generation, the topology and structure of modern power systems get more complicated, which promotes higher requirements for power system secure and stable operation. However, power system large-scale blackouts often occur. This paper firstly reviewed 51 major blackouts in history worldwide. The main causes of blackouts were summarized afterwards. Finally, distributed accommodation for distributed energy in energy internet was proposed as a key research direction to avoid large-scale blackouts.

The existing out-of-step splitting device may be worse in adaptability under multi-frequency out-step oscillation scenario. Aiming at this problem, this paper presents an out-of-step splitting strategy based on compound information to adapt to the migration of oscillating center. In detail, the proposed strategy can obtain bus voltage amplitude/phase information provided by WAMS and local Ucosφ criterion to determine position of out-of-step oscillation center and out-of-step period. The proposed strategy can greatly simplify the configuration and parameter setting of out-of-step splitting devices. Simulation results show that the correctness of the proposed strategy under multi-frequency out-step oscillation scenario.

With the popularization of intelligent household appliances, the interactions between electric market and customers have become more frequent. Because of Photovoltaic(PV) power intermittency, the customers are encouraged to participate in PV consumption. This paper proposes an optimal smart power utilization model using a non-cooperative game for residential community with distributed PV energy. Firstly, a benefit maximization model for distributed PV energy is established to determine an optimal PV output. Secondly, according to the consumption habits and load curves of residential customers, a power utilization model of community customers is built by clustering analysis. Finally, a non-cooperative game model between PV power supplier and customers in the community is built, and a Nash Equilibrium point is obtained based on the balance between maximum benefit of PV power and minimum electricity bills for customers, which is useful to encourage the consumers to consume PV energy in local areas. The proposed model can not only achieve the maximum benefit of PV power, but also reduce customers’ electricity payments, which is indicated in case studies.

A bidirectional filter with low ripple and fast response is designed to solve the problem of low frequency fluctuating in DC distribution network of DC side. Firstly, the matched filter design method is used to optimize the filter order and passband frequency. After that, the voltage transfer function of DC side filter network is derived and its voltage transfer characteristic is analyzed. For the resonance problem of the filter network, the parameter selection method of suppressing the peak value of the resonance point is given, and the effect of actual parameter deviation is further analyzed. The simulation model of double-ended and single-ended radiation DC distribution network was built in MATLAB/Simulink, and the experimental platform of single-ended DC distribution network was established.

In this paper, analysis for the influence of electric vehicle (EV) chargers on grid harmonics is conducted. The simulation model of the power distribution network with vehicle-mounted chargers is established, and the harmonic feature of the charger is analyzed. Considering different State of Charge (SOC) of the chargers and the relationship between harmonic current and initial SOC values of the EVs is established. To reduce the harmonic current caused by EV chargers, an optimization model of coordinated charging has been developed and the optimal solution is obtained by using the PSO algorithm. The validity of the proposed optimal charging schedule is verified through the simulation results.

Base-load cycling capacity (BLCC) shortage problem may seriously affect the integration scale of renewable energy. The ability to improve the BLCC only by operational dispatch of conventional plants is very limited. Therefore, it is critical to guarantee adequate BLCC at the capacity planning level. However, the BLCC have been ignored currently at yearly planning stage. In this paper, a yearly generation capacity planning model considering the BLCC constraints is proposed based on the screening curves method. With this model, an optimal mix of generation capacity can be obtained. Through dispatching conventional plants of the optimal mix, the BLCC constraint of each day in the planning year can be satisfied. Then, the impacts of cost parameters and renewable energy integration scale on the optimal mix are theoretically analyzed. Numerical simulations are presented to verify the reasonableness and effectiveness of the theoretical analysis.

Nowadays, energy consumption problem and air contamination affecting the quality of our lives horribly is becoming more and more serious. Industrial emission issues have attracted much attention specially. As a crucial index, the denitration efficiency of Selective Catalytic Reduction (SCR) system, which is the primary way to control emission of NO_x in coal-fired power plant, is normally required above 80%. In this paper, Fractional Order Proportional Integral Differential based on Internal Model Control (IMC-FOPID) controller is introduced to control the emission of NO_x by implementing multiple model strategy. Compared with traditional PID controllers, the proposed control method in this paper is more effective.

A novel communication network planning method is proposed by considering the relationship between optical fiber communication network and power grid and the topological stability of communication networks. Firstly, the minimum spanning tree is used to generate the shortest power grid topology in term of the minimum expense of initial power grid. Then, for dual network (i.e., power grid and communication network) coupling characteristics, considering the constraints of network looping rate and invulnerability, an economic planning model is established and further solved by using the particle swarm optimization (PSO) algorithm. Finally, a 29-node system is employed to confirm the effectiveness and feasibility of the proposed method.