A coordinated optimal programming scheme for an electric vehicle fleet in the residential sector

The development of intelligent strategies to manage electric vehicle charging process is the key for fostering a proper diffusion of electric vehicles at customer premises. The presence of renewable generation and the exploitation of vehicle-to-grid can enhance this process. In this paper, two procedures are proposed for optimizing electric vehicle charging strategies, for an aggregation of consumers, with the objectives of load profile levelling and total cost minimization, in the presence of possible realistic diffusion of photovoltaic systems and electric vehicles. Moreover, the best compromise between the two objectives is evaluated by determining techno-economic merit indicators. The procedures are applied to a realistic case study in the UK, considering an aggregator managing a group of residential customers in a low-voltage distribution network, where multiple tariff schemes are assessed

Demand Side Management of Electric Vehicles in Smart Grids: A survey on strategies, challenges, modeling, and optimization

The shift of transportation technology from internal combustion engine (ICE) based vehicles to electricvehicles (EVs) in recent times due to their lower emissions, fuel costs, and greater efficiency hasbrought EV technology to the forefront of the electric power distribution systems due to theirability to interact with the grid through vehicle-to-grid (V2G) infrastructure. The greater adoptionof EVs presents an ideal use-case scenario of EVs acting as power dispatch, storage, and ancillaryservice-providing units. This EV aspect can be utilized more in the current smart grid (SG) scenarioby incorporating demand-side management (DSM) through EV integration. The integration of EVswith DSM techniques is hurdled with various issues and challenges addressed throughout thisliterature review. The various research conducted on EV-DSM programs has been surveyed. This reviewarticle focuses on the issues, solutions, and challenges, with suggestions on modeling the charginginfrastructure to suit DSM applications, and optimization aspects of EV-DSM are addressed separatelyto enhance the EV-DSM operation. Gaps in current research and possible research directions have beendiscussed extensively to present a comprehensive insight into the current status of DSM programsemployed with EV integration. This extensive review of EV-DSM will facilitate all the researchersto initiate research for superior and efficient energy management and EV scheduling strategies andmitigate the issues faced by system uncertainty modeling, variations, and constraints

PV Charging and Storage for Electric Vehicles

Electric vehicles are only ‘green’ as long as the source of electricity is ‘green’ as well. At the same time, renewable power production suffers from diurnal and seasonal variations, creating the need for energy storage technology. Moreover, overloading and voltage problems are expected in the distributed network due to the high penetration of distributed generation and increased power demand from the charging of electric vehicles. The energy and mobility transition hence calls for novel technological innovations in the field of sustainable electric mobility powered from renewable energy. This Special Issue focuses on recent advances in technology for PV charging and storage for electric vehicles

A Case Study of the Use of Smart EV Charging for Peak Shaving in Local Area Grids

Electricity storage systems, whether electric vehicles or stationary battery storage systems, stabilize the electricity supply grid with their flexibility and thus drive the energy transition forward. Grid peak power demand has a high impact on the energy bill for commercial electricity consumers. Using battery storage capacities (EVs or stationary battery systems) can help to reduce these peaks, applying peak shaving. This study aims to address the potential of peak shaving using a PV plant and smart unidirectional and bidirectional charging technology for two fleets of electric vehicles and two comparable configurations of stationary battery storage systems on the university campus of Saarland University in Saarbrücken as a case study. Based on an annual measurement of the grid demand power of all consumers on the campus, a simulation study was carried out to compare the peak shaving potential of seven scenarios. For the sake of simplicity, it was assumed that the vehicles are connected to the charging station during working hours and can be charged and discharged within a user-defined range of state of charge. Furthermore, only the electricity costs were included in the profitability analysis; investment and operating costs were not taken into account. Compared to a reference system without battery storage capacities and a PV plant, the overall result is that the peak-shaving potential and the associated reduction in total electricity costs increases with the exclusive use of a PV system (3.2%) via the inclusion of the EV fleet (up to 3.0% for unidirectional smart charging and 8.1% for bidirectional charging) up to a stationary battery storage system (13.3%)

Operational Methods for Charging of Electric Vehicles

The increasing number of electric vehicles induces a new relationship between the electric vehicles, transportation network and electric network. The deployment of the charging infrastructure is a prerequisite of the widespread of electric vehicles. Furthermore, the charging process and energy management have a significant influence on the operation of both the transportation and electric networks. Therefore, we have elaborated novel operational methods that support the deployment of charging infrastructure for electric cars and buses operating in public bus service, and the energy management. Weighted sum-models were developed to assess candidate sites for public charging stations. The mathematical model of public bus services was elaborated that supports the optimization of static charging infrastructure at bus stops and terminals without schedule adjustments. The flexibility and predictability of charging sessions were identified as the main differences between charging infrastructure deployment for cars and buses. Furthermore, the flows of energy, information and value have been revealed among the components of charging with a focus on commercial locations, which is the basis of energy flow optimization on the smart grid

供給と需要側を考慮した電源システムのモデリングと評価

Modelling and optimization of sustainable power system and energy network are becoming complex engineering. Demand side resources also need to be planned considering characteristics of district energy supply scenario. This research first analyzes the feasibility of VPP based on scenario of Chongming Island. VPP focuses on expansion of renewable energy and upgrade of efficient appliances, results verify the effectiveness of the VPP concept. Then investigates the techno-economic viability of high variable renewable integration. PV-PHS dispatch scenarious are carried out with constraints, PHS effectively recovers the suppression and decreases the PV power levelized cost. Introduction PV-PHS shifts merit order curve to right, decreasing power generating cost. Thirdly, cost and environmental benefits of optimal designed decentralized energy systems were investigated. Scheduled distributed energy resources could be optimized to benefit the public grid. Performance of dynamic price is investigated based on the social demonstration project experiment. Finally, the conclusions are provided.北九州市立大

Vehicle to grid concept as part of power system and electricity market

The demand of electricity is increasing day by day with the increase in world’s population. Renewable energy sources (RES) are being integrated into the smart grid system. Renewable energy sources have fluctuating nature such as wind and solar power, and thus accurate forecasting of generation from these sources is nearly impossible. At the distribution end, load forecasting is also challenging as the load demand is not constant all the time. Hence to deal with the intermittent nature of RES and to fulfil the peak load demand, there is a need for a storage system that can be integrated with the smart grid environment. Electric vehicles (EVs) serve this purpose and replace the internal combustion engine vehicles in transportation. They are considered as mobile energy storage systems which do not only reduce the environmental pollution but also provide power to the grid in peak load time by storing energy in their batteries at off peak times when the demand is quite low. The aim of this thesis is to understand the concept of V2G. It includes the short introduction of different types of EVs, their construction, advantages and issues related to them. Charging of EVs is considered as an important phenomenon as the penetration of large fleet of EVs in the grid when behaving as a load (charging), would impact the power system and may cause overloading. Thus to avoid this situation, an intelligent charging infrastructure is needed which is explained in this thesis. There are different applications for using EVs as supplying energy back to the system, such as vehicle to home (V2H), vehicle to building (V2B), vehicle to grid (V2G) etc. EVs charge their batteries in off peak time and then discharge them by connecting to the grid when the load demand is very high. This way of supplying power back to the grid is known as V2G. The main concern of this thesis study is to analyze the different aspects of V2G. Basically, this thesis is a literature review of V2G concept and explained its mechanism, benefits, applications and challenges associated with it. Different case studies have been analyzed which explained the implementation of V2G. Moreover, this thesis demonstrates the concept of ancillary services provided by EVs in V2G mode. These ancillary services include frequency regulation and spinning reserve. Impact of V2G on power system has been studied in detail. Electricity market competition will increase with the increasing number of EVs which are supplying ancillary services to the grid

Energy Management and Smart Charging of PEVs in Isolated Microgrids

Microgrids are defined as a cluster of loads and micro-resources operating as a single controllable entity that provides both power and heat to its local area. Typically, these rely on conventional diesel generators, but with recent developments are expected to include more renewable energy sources (RESs), battery energy storage systems (BESSs), and plug-in electric vehicles (PEVs). Both RESs, such as wind and solar, and PEVs can reduce greenhouse gas (GHG) emissions significantly such as carbon dioxide (CO_2) which are released from burning fuel by generators or conventional vehicles. Energy management in isolated microgrids is an important task since these have limited generation capacity and are expected to rely on various uncontrollable resources to match and balance the demand-supply gap. Moreover, PEVs present a promising solution to GHG emissions but on the other hand, their increased penetration can impact power system operation, particularly so in isolated microgrids. Therefore, PEV load management is considered to be a crucial issue. Similarly, demand response (DR) has the potential to provide significant flexibility in operation of isolated microgrids with limited generation capacity, by altering the demand and introducing an elasticity effect. The present research work examines the impact of uncontrolled and controlled (smart) charging of PEVs using a comprehensive mathematical optimization model for short-term operation of the isolated microgrid. This model determines optimal energy management solutions combining generation from different resources such as diesel generators, wind turbines, solar panels, and BESSs, and incorporates the DR options as well. Furthermore, the thesis presents a stochastic optimization model after creating several probabilistic operational scenarios for energy management and smart charging of PEVs in short-term operation of the isolated microgrid considering fixed and optimal DR options. The proposed stochastic optimization model studies the impact of wind and solar generation output variability as well as the effect of uncertain energy consumption patterns of customers; and also the stochastic nature of the state of charge (SOC) of the PEV battery at the start of charging. Several case and scenario studies considering a modified CIGRE isolated microgrid benchmark test system, and using the proposed models are presented and evaluated, to obtain insights into the effect of smart charging vis-`a-vis uncoordinated charging accompanied by DR options in overall energy management of the isolated microgrid.4 month