Electrical Vehicles Charging Coordination by Fuzzy Logical System

The interdependency of transportation and electric power networks is becoming tighter due to the proliferation of electric vehicles (EVs), which introduces additional difficulties in the planning of the two networks. This paper presents the enhanced solution for the coordinated planning of multiple facilities in the two networks, including electric power lines, transportation roads, energy storage systems and fast charging stations. In order to calculate the optimal solution for the proposed coordinated planning problem, we introduce the applications of linear optimization theory including Karush-Kuhn-Tucker conditions, the big M method, and a linear expression of power loss to transform the nonlinear planning problem into a mixed-integer quadratically constrained programming (MIQCP) formulation, which is solved by commercial solvers. The proposed MIQCP formulation is decomposed into two corresponding subproblems by Lagrangian relaxation to represent transportation and electric power networks. The case studies validate the proposed planning model and demonstrate that the proposed solution can enhance the coordinated network planning with the proliferation of EVs.

Developing cars is a major factor that has determined the increasing of the civilization degree and the continuous stimulation of the society progress. Currently, in Europe, one in five active people and in the US, one in four, directly work in the automotive industry (research, design, manufacture, maintenance) or in related domains (fuel, trade, traffic safety, roads, environmental protection). On our planet the number of the cars increases continuously and he nearly doubled in the last 10 years. With increasing number of cars entered in circulation every year, is held and increasing fuel consumption, increased environmental pollution due to emissions from internal combustion engines (ICE), used to their propulsion. Reducing oil consumption takes into account the limited availability of petroleum reserves and reducing emissions that affect the health of population in large urban agglomerations. The car needs a propulsion source to develop a maximum torque at zero speed. This can not be achieved with the classic ICE. For ICE power conversion efficiency is weak at low speeds and it has the highest values close to the rated speed. Pollution reduction can be achieved by using electric vehicles (EV), whose number is still significant. The idea of an electrical powered vehicle (EV) has been around for almost 200 years. The first electric vehicle was built by Thomas Davenport in 1834 [Westbrook, 2005]] But over time, the batteries used for energy storage could provide the amount of electricity needed to fully electric propulsion vehicles. Electric vehicles are powered by electric batteries which are charged at stations from sources supplied by electrical network with electricity produced in power plants. Currently, a lot of researches are focused on the possibility of using fuel cells for producing energy from hydrogen. EV with fuel cell can be a competitive alternative to the standard ICE that is used in today’s cars. If performance is assessed overall thrust of the effort wheel and crude oil consumed for the two solutions: classic car with ICE and car with electric motor powered by electric batteries, the difference between their yields is not spectacular. In terms of exhaust emissions is the net advantage for electric vehicles. Pollutant emissions due to energy that is produced in power plants (plant property, located) are much easier to control than those produced by internal combustion engines of vehicles that are individual and scattered. Power plants are usually located outside urban areas, their emissions affects fewer people living in these cities. By using electric motors and controllers efficient, electric vehicles provide the means to achieve a clean and efficient urban transport system and a friendly environment. Electric vehicles are zero emission vehicles, called ZEV type vehicles (Zero-Emissions Vehicles).

This paper took the check of rupturing capacity circuit breaker in petrochemical electric power network for example. It introduced the selection rule of largest short-circuit current which flow through circuit breaker, the principle of short circuit calculation, the equivalent model of external system, the modeling of mathematical model of industrial electric owned power network, and the procedures of short circuit calculations. At same time, the different short-circuit current was calculated which flow through the circuit breaker in both original petrochemical electric power network and the rebuilding one. At last, the simulation example proved that the margin of short-circuit current is less and less or even exceeding with the growth in the loads, increasing in self-power supply and external system in industrial electric power network. The industrial should pay sufficient attention to it.

The reliable operation of electricity transport and distribution networks plays a crucial role in modern societies. However, too often, when a fault occurs in electricity networks, such as a transmission line drop, loss of generation, or any other important failure, voltages start to decay, potentially leading to complete blackouts with dramatic consequences. Thus, techniques are required that improve the power grid operation in case of emergencies. In this chapter, to achieve this aim, an approach is presented that uses an adaptive predictive control scheme. Electric power transmission networks are hereby considered as large-scale interconnected dynamical systems. First, voltage instability issues are illustrated on a 9-bus benchmark system. Then, the details of the proposed approach are discussed: the power network modeling and the construction of a hybrid prediction model (i.e., including both continuous and discrete dynamics), and the formulation and the resolution of the adaptive predictive control problem. In simulation studies on the 9-bus benchmark system the performance of the proposed approach is illustrated in various emergency voltage control cases.

Traditional electrical power systems were designed with an assumption of unidirectional power flows through electrical networks. For many years power plants had been the only sources of electrical energy, and the energy had been delivered to the consumers by the means of transmission and distribution networks exclusively.With the emergence of distributed generation units, power flows have become bidirectional, which has made it impossible to estimate the voltages and currents in substation feeder nodes by measuring only the values at the beginning of the feeder. As the aforementioned estimation process is the basis of traditional voltage regulation techniques, it is obvious that under the new circumstances existing methods have to be upgraded.The article demonstrates how the open source tool PandaPower along with the Python programming language was utilized to analyze and evaluate several different scenarios of using photovoltaic distributed sources inverters for voltage regulation in low voltage electrical power networks.

Electric vehicle is a new type of mobile intelligent power equipment and energy storage terminal. Electric vehicle energy service infrastructure network is an important part of smart grid. In order to solve the automation and intelligence problems of wide area electric vehicle charging and changing business, this paper first analyzes the charging and changing business scenarios of electric vehicles and the technical support requirements of operation monitoring business. Therefore, this paper proposes the information perception application and communication networking mode of Internet of things technology in electric vehicle charging and swapping network. On this basis, a wide area electric vehicle charging and switching network operation monitoring and management platform architecture based on GIS and SOA is designed. The results show that: the total number of public charging piles in China increases with the growth of years, and the largest increase is 593000 in 2020.

Electric vehicles (EVs) are increasing popular as clean transportation. However electric vehicle (EV) users often encounter a shortage of electricity in the driving process. Especially when vehicle charging path planning system request reaches peak hour, it will increase the system calculation pressure. To promote penetration and popularity of electric vehicles, it is of critical importance to determine location and size of charging stations more scientifically and improve the computational power of path planning. In this paper, a method is developed for charge warning and path planning of insufficient energy EVs. The method utilizes the appropriate energy consumption factor to construct the electricity early warning model, analyzes the consumption of electricity in the vehicle in real time, and promptly warns the users when the vehicle has insufficient electric energy. Meanwhile, it combines the actual traffic information and map information to construct a network topology based on time weight, queuing mechanism, and charging calculation model to obtain path network model data, charging station queuing times and charging times as parameters. EV path planning problem is solved to minimize the total times of travel using the Dijkstra algorithm with the input path network model data, charging station queuing times and charging times as parameters. At the same time, the Spark computing framework is joined innovatively to improve the computing speed and solve the problem of path planning peak period. The Spark framework is utilized to parallelize the optimized Dijkstra algorithm. The experiment shows that the electric vehicle charging warning and path planning method provides an effective way for drivers of electric vehicles to charge.

Power transformers are very vital components in electrical power network. Deterioration of transformer’s insulation has significant impacts on its health condition. Hence, detailed analysis of insulation testing attributes is very important in diagnosing the transformer. It is possible with an effective decision-making system. In the present paper, a new grey relational-based fuzzy logic expert system is proposed for continuous health monitoring of power transformers. In this approach, ranking-based qualitative relational analysis is initially carried out in order to reduce large number of input attributes into three input health grades. These input grades are further integrated with a technically designed expert fuzzy rule base. Outputs of the proposed model are validated with the outputs of multi-criterion based fuzzy logic model presented in [Mharakurwa E T and Goboza R 2019 Multiparameter-based fuzzy logic health index assessment for oil-immersed power transformers Adv. Fuzzy Syst. 2019]. Testing data of 200 in-service transformers are utilised in the validation of both the models. It is observed from the comparison that similarity index of the proposed grey-fuzzy system is 92 percent. Several issues exist in regular transformer health assessment models such as the large number of rules, uncertainties, requirement of large training data, complexity issues are overcome by integrating the grey relational analysis with the fuzzy logic system. The proposed model can be easily implemented by all utilities and diagnostic experts of transformers.

Summary form only given. The smart grids are electricity networks that can intelligently integrate the behaviour and actions of all users connected to it in order to efficiently deliver sustainable, economic and secure electricity supplies. One of the reasons why smart grid is generating so much interest right now is its ability to enable the integration of renewable energy into the electric power network, leading to a broader generation portfolio and potentially beneficial carbon implications. In this presentation, a general overview of the current power electronic technology that permits the integration of renewable energies into the smart grid will be analyzed. In particular, it will be described the "Smart City" project aims to develop a demonstrator of a smart grid in Malaga city in which customers and distribution companies work together to achieve sustainable energy consumption, by increased use of renewable energy sources, bringing generation closer to customers and making a commitment to rational and efficient consumption. A network of this type is essential in order to deploy electric vehicles.

The primary objective of the Response to Demand (DR) is to manage the required demand, by allocating existing resources in a way in which to allow it to be used without the need to increase new generation capacity. The DR focused on the grid load due to the insertion of Electric Vehicles (EVs), and it promotes a great emphasis on the impact they cause on the electrical systems. Therefore, this work develops an optimal response to demand with respect to the impact generated by EVs, in addition to the different types of loads toward the electrical network with various scenarios. A heuristic model is presented that is based on the Hungarian algorithm in MatLab, that is, in this specific work it was intended to achieve the adequate allocation of energy resources for the charging of EVs. Posing several load scenarios that focus on DR among which the types of loads that will be used have been modeled and determined: (a) slow, (b) fast, and (c) ultra-fast that allowed us to evaluate the different behaviors. It also describes the different types of batteries and which EVs are using them, as well as having a response demand taking advantage of the penetration of conventional and unconventional energies and in turn also with the influence of solar radiation in winter and summer cases to the EV load will be those coming from renewable energies.KeywordsResource allocationHungarian algorithmDemand responseElectric vehiclesMatchingFirst section

Currently, hydrogen fuel cell electric vehicles (HFCEVs) are becoming more financially accessible as an alternative to petroleum-powered vehicles, while also decreasing carbon dioxide emissions. However, the coordinated scheduling of HFCEVs refueling with the operations of hydrogen refueling stations, electrical power network (PN), and transportation networks (TN) represents an integral challenge. This complex problem encompasses a combinatorial mixed-integer nonlinear optimization problem with a sizeable number of decision variables. Existing methods struggle to adequately solve this problem. In this article, deep reinforcement learning (DRL) is deployed to determine the refuelling price to guide the HFCEV refuelling in the transportation network. First, HFCEV traffic flow model based on the refuelling price in real-world TN is presented. Then, the HFCEVs hydrogen demands in the microgrid is presented. After that, an IEEE 30 nodes utility grid exporting electricity to microgrids is presented. At last, DRL (DDPG, TD3, SAC, PPO) is deployed to determine the price based on the traffic condition of the TN and the voltage condition of the PN. The simulation results demonstrate that through the DRL price agent, the total travel time of the TN and the total operation costs of the PN are all reduced, and multiagent DDPG and TD3 algorithm have the best performance.

Compared to yesteryear, non-linear loads now proliferate in homes, offices and the industrial environment. Non-linear loads now form a major component of the total loading in the typical electrical network. The Compact Fluorescent Lamp is but one example of a non-linear load that inherently produces and injects distorted currents into the electrical power network, which could create adverse consequences if appropriate and adequate harmonic mitigation techniques are not employed. CFLs are now a major component of the luminaires market around the world because consideration is being given worldwide to the phasing out of incandescent lamps. However, there are legitimate concerns over the quality of some CFLs. This paper investigates and compares the quality of some major brands of CFLs, in terms of parameters such as harmonic content, power factor, fundamental power factor, nonactive power, and apparent power. The wider implication of these results is ascertained by investigating the effects large scale installation of CFLs would have on a generalized electrical network. This work shows that CFLs with high harmonic currents increase the requirements of the source and other network components, as well as place a significant strain on them, since they draw a higher current and also consume a higher apparent power and nonactive power than is necessary for power delivery. Also, it is found that voltage harmonic limits can approach or exceed the IEEE limit of 5% as the number of CFLs deployed is increased. This situation is more pronounced if CFLs with higher current harmonic content are installed.

At present, semiconductor converters have found wide application in the electric power industry [6]. One of the most common semiconductor converters is a frequency converter, as it provides a three-phase voltage with adjustable frequency from a three-phase mains voltage. However, it, like other semiconductor converters, is the cause of the appearance of harmonics and, as a result, the non-sinusoidal shape of the voltage curve [1]. Harmonics are the cause of such negative effects in the electrical network as concomitant heating of the windings of motors, transformers, etc., false alarms in distribution networks, asymmetry in industrial networks with three-phase sources when harmonics occur in one phase, the occurrence of noise in communication networks, the influence on adjacent low-current and power cables due to the induced EMF, and also with the appearance of harmonics, there is a need to increase the cross section of neutral wires due to the summation of harmonics multiple of the 3rd in three-phase networks [3]. Therefore, the study of the harmonic components of the voltage is an urgent task.
 Subject of research: processes occurring in electrical networks with a frequency converter.
 Purpose of research: to identify the presence of harmonic voltage components in electrical networks with a frequency converter.
 Object of research: a research stand that simulates the operation of electrical networks with a frequency converter.
 Methods of research: in the process of performing research, scientific and technical generalization of literary sources on the initial prerequisites for research, methods of the theoretical foundations of electrical engineering and the theory of electrical networks, the method of analytical research, methods of system analysis were used. Experimental studies were carried out by a complex method using the Resurs-PKE device.
 Results of research he study: based on the studies carried out, it was shown that the frequency converter is a source of harmonic voltage components, while harmonics appear only in certain sections of the electrical circuit.

Electric Vehicle (EV) charging infrastructure will be deployed extensively in the near future. This will be as a consequence of the policies approved by the governments of many countries including the UK. The rise in the mass production of EVs and the associated increase in consumer demand significantly exacerbate the problems associated with the operation and management of future electric power networks. The 11/0.433kV local substations that will be used to transform voltage levels and supply the demand from consumers are the most affected, requiring reinforcement where substations are close to their firm capacities. In this paper an 11kV power distribution network is modelled using the power system analysis software Electrical Transient Analyzer Program (ETAP). The peak load values over a year are evaluated under four different Electric Vehicle (EV) penetration levels and three different charging types (slow, fast and rapid) at all twenty substations on site. Transformer loading at twenty substations is carefully observed in all simulations under different scenarios to evaluate the total number of EVs the site can accommodate. Initially ETAP 12.0.0 is employed to conduct power flow analysis and study the existing network conditions and later the voltage variations, real and reactive power changes in the system due to the uptake of EVs. The existing network data as required for the research and as presented in this paper has been obtained in collaboration with distribution network operator (DNO) Scottish and Southern Energy (SSE).

In our days, almost all of the electrical equipment has been equipped with switching supplies which can cause disturbances in the electrical power network. In order to reduce or even eliminate this disturbances, an electrical power network monitoring is required. The disturbances presents in the system must be recorded and analyzed in order to take the right decision to eliminate them.