Optimal day-ahead scheduling of microgrid with hybrid electric vehicles using MSFLA algorithm considering control strategies

For optimal microgrid (MG) operation, one challenge is the supply of cooling and electricity energy is a coupled co-optimization issue when considering the combined cooling, heating and power (CCHP) units and ice-storage air-conditioners. Another challenge is the inherent randomness of renewable energy within the MG should be accommodated by MG itself. In Part I of this two-part paper, the partial load performance of CCHPs and the performance of ice-storage air-conditioners are modeled, and the cooling and electricity coordinated MG day-ahead scheduling and real-time dispatching models are established. In day-ahead scheduling model, the uncertainty of wind and solar power is represented by multi-scenarios and the objective is to achieve the minimal expected MG operation cost. In real-time dispatching model, the different time-scale dispatch schemes are respectively applied for cooling and electricity to smooth out the fluctuations of renewable energy supply and to follow the variations of cooling and electricity demands by the fine dispatching of the components within MG such that the impact of MG to the connected main grid is minimal. The proposed MG multi time-scale cooling and electricity coordinated schedule achieves an integrated optimization for multi energy-type supply, and makes the MG be controllable as seen from the main grid.

Techno-economic energy management of micro-grid in the presence of distributed generation sources based on demand response programs

The main aim of this paper is to study the potential impacts in hybrid and full electrical vehicles performance by utilising continuously variable transmissions. This is achieved by two stages. First, for Electrical Vehicles (EVs), modelling and analysing the powertrain of a generic electric vehicle is developed using Matlab/Simulink-QSS Toolkit, with and without a transmission system of varying levels of complexity. Predicted results are compared for a typical electrical vehicle in three cases: without a gearbox, with a Continuously Variable Transmission (CVT), and with a conventional stepped gearbox. Second, for Hybrid Electrical Vehicles (HEVs), a twin epicyclic power split transmission model is used. Computer programmes for the analysis of epicyclic transmission based on a matrix method are developed and used. Two vehicle models are built-up; namely: traditional ICE vehicle, and HEV with a twin epicyclic gearbox. Predictions for both stages are made over the New European Driving Cycle (NEDC).The simulations show that the twin epicyclic offers substantial improvements of reduction in energy consumption in HEVs. The results also show that it is possible to improve overall performance and energy consumption levels using a continuously variable ratio gearbox in EVs.

Over the past few years Hybrid Electric and Electric propulsion systems have found significant attention as the most plausible substitute to fossil fuel based engines. Hybrid Electric Vehicles (HEV) have been around for more than a decade and extensive research has been taken out to make these vehicles more efficient. With advances in technology, manufacturers such as Tesla and Chevrolet have successfully launched a number of Electric Vehicles (EV) in the past 5 years. In despite of all this success, HEVs and EVs currently face challenges in energy storage systems (ESS) with regard to a variety of parameters and to overcome these issues research has been done on different types of ESS systems to extend the range of such vehicles. Flywheel Energy Storage Systems (FESS) have regained interest in the last decade and the application of kinetic energy recovery system (KERS) in the Formula 1 has reinforced the case of using FESS in HEV and EV. In this study, the integration of an FESS system within a hybrid electric propulsion and an electric propulsion system is considered and with the help of Bond-Graphs as a multidisciplinary modelling tool the impact of this integration is analyzed and compared with each other.

Regarding the advances in technology and anxieties around high and growing prices of fossil fuels, government incentives increase to produce cleaner and sustainable energy through distributed generations. This makes trends in the using microgrids which consist of electric demands and different distributed generations and energy storage systems. The optimum operation of microgrids with considering demand-side management increases efficiency and reliability and maximize the advantages of using distributed generations. In this paper, the optimal operation scheduling and unit commitment of generation units installed in a microgrid are investigated. The microgrid consists of technologies based on natural gas that are microturbine and phosphoric acid fuel cell and technologies based on renewable energy, including wind turbine and photovoltaic unit along with battery energy storage system and plug-in electric vehicle commercial parking lot. The goal of the paper is to solve a multi-objective problem of maximizing revenues of microgrid operator and minimizing emissions. This paper uses an augmented epsilon constraint method for solving the multi-objective problem in a stochastic framework and also implements a fuzzy-based decision-maker for choosing the suitable optimal solution amid Pareto front solutions. This new model implements the three type of the price-based and incentive-based demand response program. It also considers the generation reserve in order to enhance the flexibility of operations. The presented model is tested on a microgrid and the results demonstrate the efficacy of the proposed model economically and environmentally compared to other methods.

Multi-objective energy management using a smart charging technique of a microgrid with the charging impact of plug-in hybrid electric vehicles

As the scale of electric vehicles (EVs) rapidly expands and the interconnection of multimicrogrid (MMG) advances swiftly, the coordinated and optimal scheduling of EVs and MMGs has become a key technology to enhance the efficiency of regional energy use. Herein, this study proposes a two‐layer optimal scheduling strategy for MMG containing EVs, separated into the load layer and the microgrid (MG) layer. In the load layer, EVs are guided to charge in an orderly manner through time‐of‐use pricing, to alleviate the harm caused to the grid due to disordered charging and reduce the load fluctuation. In the MG layer, firstly, the MMG total operation cost minimization model is established through the cooperative game model and addressed by employing the alternating direction method of multipliers; Secondly, a Nash bargaining model for benefit distribution is developed based on the electric–carbon integrated contribution bargaining mechanism, and the benefits of the cooperation are distributed fairly. The findings indicate that the proposed model can remarkably decrease the charging cost of users and reasonably distribute the cooperative benefits. Meanwhile, this model experiences an 18.34% reduction in total operating cost and a 32.95% reduction in carbon emissions compared with the separate operation.

Bald eagle search optimizer-based energy management strategy for microgrid with renewable sources and electric vehicles

<div class="htmlview paragraph">Hybrid electric vehicles (HEV) and other advanced propulsion technologies offer greater fuel economy and lower pollutant and tailpipe emissions. Some experts regard the HEV technology as a practical, attractive solution to social concerns about fuel economy (and related fossil fuel conservation) and vehicle emissions. [<span class="xref">Maples 98</span>] The technology is practical; however, an HEV is more expensive to produce than a conventional internal combustion engine (ICE). In a world of limited resources and many petroleum users and emissions sources, the policy question is whether the best use of resources is to build HEV, to improve the fuel economy and lower emissions from other sources, or to devote the resources to other environmental projects.</div> <div class="htmlview paragraph">We compare the second generation (designed for the United States) Toyota Prius, to the conventional internal combustion engine Toyota Corolla. We examine both private (vehicle purchase price, maintenance, and fuel) and social (the pollutant [non-methane organic gases, carbon monoxide, nitrogen oxides] and carbon dioxide emissions) costs. For each of the vehicles, we evaluate lifetime vehicle exhaust emissions as well as upstream emissions from producing the fuel.</div> <div class="htmlview paragraph">We find that the second generation Prius is still not cost-effective in improving fuel-economy and lowering emissions. For the Prius to be attractive to U.S. consumers, the price of gasoline would have to be more than three times greater than the present level. To be attractive to regulators, the social value of abating tailpipe emissions would have to be 14 times greater than conventional values. Alternatively, the value of abating greenhouse gas emissions would have to be at least $220 per ton. We judge that any HEV would have a difficult time competing with the Corolla because of the Corolla's already high fuel economy and low pollutant emissions.</div> <div class="htmlview paragraph">We also examine the costs and benefits of making a GM Silverado pickup truck into a gasoline hybrid. Like the analysis of the Prius, we again find that the price of gasoline or social value of abating carbon dioxide would have to be higher than the base case values in order to payback the cost of converting the vehicle to an HEV. Finally, we find that at current gasoline prices and carbon dioxide valuation, a conventional vehicle would need to have a fuel economy of 13 mpg or less in order for a $4,000 cost of converting it to an HEV to get 30% better fuel economy would pay off. We conclude that HEV will not have significant sales unless fuel prices rise several-fold or unless regulators mandate them.</div>

Electric vehicles (EVs) play a vital role in the reduction of emission of the greenhouse gases by reducing the consumption of fossil fuel. This paper presents a fully developed integration of the EVs with a security-constrained unified active and reactive power dynamic economic dispatch of microgrids (MGs) to minimize the total operating cost or maximizes the profit. The formulation of the overall optimization problem considers the reactive power production cost and relevant constraints, the environmental costs, and the battery degradation cost. A comprehensive set of constraints including active and reactive security constraints, limitation of the greenhouse gases constraints, and constraints relevant to the integration of the EVs with the MG are considered as well. The bi-directional penetration of the EVs with the MG is modelled and incorporated with unit commitment (UC) optimization problem. The results show that the proposed approach of the integration of the EVs with the MG reduces the total operating cost and increases the profit.

The purpose of this paper is to analyse Tesla’s strategy for ensuring own sustainable future growth, while acting as a catalyst of change in the automotive industry. The paper utilises the document analysis as a main method, with the presentation of the results in a case study format. It was realised that Tesla has managed to create a product that has a rather unique positioning in a highly competitive industry. However, Tesla was unable to capitalise on the potential demand regardless of superiority of its products, as the current market leaders have a considerably lower price and are competing with a different class of vehicles altogether. The implications for industry development are discussed in connection with competition in Hybrid Electric Vehicle and Battery Electric Vehicle categories.

This paper summarizes the trend of novel technologies used for electric vehicles (EV) and hybrid electric vehicles (HEV) based on the published papers and reports in order to predict the needs technologies for expanding EV and HEV market. At first, the relationship between energy demand situation and expanding trends of electric propulsion vehicles will be mentioned. Regarding traction motors, required performances and miniaturizing technologies by novel coil winding structures and increasing rotational speed, iron loss reduction technologies by the contrived core shapes, have been extracted and classified into the trends for each EV and HEV. In conclusion, miniaturizing technology was found to be one of the key issue of traction motor for electric propulsion vehicles.

Full Electrical Vehicles (FEVs) and Hybrid Electrical Vehicles (HEVs) are vehicles with many electric components compared to conventional ones. In fact the power train consists of electrical machines, power electronics and electric energy storage system (battery, super capacitors) connected to mechanical components (transmissions, gear boxes and wheels) and, for HEV, to an Internal Combustion Engine (ICE). The approach for a new vehicle design has to be multidisciplinary in order to take into account the dynamic interaction among all the components of the vehicle and the power train itself. The vehicle designers in order to find the correct sizing of components, the best energy control strategy and to minimize the vehicle energy consumption need modeling and simulation since prototyping and testing are expensive and complex operations. Developing a simulation model with a sufficient level of accuracy for all the different components based on different physic domains (electric, mechanical, thermal, power electronic, electrochemical and control) is a challenge. Different commercial simulation tools have been proposed in literature and they are used by the automotive designer [1]. They have different level of detail and are based on different mathematical approaches. In paragraph 2 a general overview on different modeling approaches will be presented. In the following paragraphs the author approach, focused on the modeling of each component constituting a FEV or HEV will be detailed. The authors approach is general and is not based on vehicle oriented simulation tools. It represents a good compromise among model simplicity, flexibility, computational load and components detail representation. The chapter is organized as follows:

The production of energy for traction in classical vehicles is performed by petrol or oil combustion. The consequence is green gas production. Paper deals with different power trains of electric hybrid and electric vehicles. Electric hybrid vehicles diminish the fuel consumption by giving to the internal combustion engine (ICE) better working conditions with respect to ICE revolutions and demanded power. The kinetic braking energy recovery is an important feature too. Electric vehicles are usually called “green vehicles”. It is true that they do not produce green gas on places where they are driving but it is also true that their traction energy has to be produced with another energy technology used in electric plants. The efficiency of the vehicle powertrain is important. The energy for transportation by vehicles in the world is great. Therefore it is very important what energy production technology for this traction purposes will be used.

For a Microgrid (MG) with cooling, heating, electricity coordinated supply to satisfy its end customers' multi energy-type demands, a day-ahead coordinated scheduling model is developed. In the model, the objective for achieving the economy of MG owner, and the constraints for the multi-energy supply and demand balances and energy devices characteristics are considered. The fluctuations of solar and wind power generation and electricity load are described by uncertainty intervals. For the nonlinear performance of energy devices, the piecewise linear regression is implemented to make it approximately represented by linear functions, and then the interval linear programming is applied to derive the interval values of the worst- and the best-case optimal solution. Several case studies are given to demonstrate the effectiveness of the proposed method.