Economic assessment of a public DC charging station for electric vehicles with load shift capability

This paper presents a new concept of public DC fast charging station for Electric Vehicles (EVs) with load shift capability and simplified interface with renewable energy sources. The key element of the proposed charging system consists in an Energy Storage System (ESS) composed by reused electrochemical batteries from EVs. In the proposed system the energy storage capability is used to smooth the intermittent power demand of fast charging systems for EV batteries, present in public charging stations, and also contributes to the stability of the electrical power grid. When integrated in a Smart Grid, the proposed system may even return some of the energy stored in the EVs batteries back to the power grid, always when it is necessary, in order to improve the power grid operation. In addition to these technical advantages, the proposed topology also presents some interesting economic benefits that are analyzed along the paper.FCT – Fundação para a Ciência e Tecnologiainfo:eu-repo/semantics/publishedVersio

Monte Carlo modelling for domestic car use patterns in United Kingdom

For the purposes of quantifying the potential impact of widespread electric vehicles charging on the UK's power distribution system, it is essential to obtain relevant statistical data on domestic vehicle usage. Since electric vehicle ownership is presently very limited, these data will inevitably be for conventional internal combustion engine vehicles, and in particular privately owned vehicles. This should not be an issue since the limited journey distances that will dealt with in this work could as easily be undertaken by an electric vehicle as a conventional vehicle. Particular attention is paid to the United Kingdom 2000 Time Use Survey as it contains detailed and valuable statistical information about household car use. This database has been analyzed to obtain detailed car use statistics, such as departure and arrival time, individual journey time, etc. This statistical information is then used to build up two Monte Carlo simulation models in order to reproduce weekday car driving patterns based on these probability distributions. The Monte Carlo methodology is a well-known technique for solving uncertainty problems. In this paper, key statistics of domestic car use are presented together with two different Monte Carlo simulation approaches the simulation results that have been analyzed to verify the results being consistent with the statistics extracted from the TUS data

Impact of Consumer Profiles on a Consumer Convenience Prioritised Demand Response

Distribution network (DN) load flexibility has simultaneously created challenges and opportunities. The major challenge is to meet the demand-supply balance while maintaining a positive profit-loss ratio. Further, Government enforced climate change policies attract low carbon technology (LCT) distributed energy resources (DER), which further complicate matters. Along with DN, the domestic appliance industry has undergone drastic modernization leading to appliances with advanced control and power efficient technologies as well as automation capabilities. This paper proposes a demand response (DR) program that facilitates these advancements while micromanaging the domestic load consumption pattern so as to manage peak demand in the network. This work identifies consumer conviction towards the DR programs as the major bottle neck for the success of such load management programs. The mixed integer linear programming based DR (MILP - DR) algorithm proposed here, minimizes the consumer inconvenience while facilitating load reduction. Further, attractive consumer engagement plans promoting different levels of engagement (load reduction) are also proposed, which further enhance the choice offering for consumers. The algorithm is tested on a 74 load (domestic) urban distribution network having 8 different consumer profiles. The algorithm is capable of inducing impartiality between consumers by updating a tolerance factor correlating inconvenience of consumers with load deprivation. The results show the capability of the algorithm to distribute load reduction based on the engagement plan, while also minimizing the consumer inconvenience. The results also suggest correlations between social parameters and achievable DR

Demand Response and Consumer Inconvenience

Balancing the energy demand and generation using the latest load management technologies is considered as an immediate requirement for peak demand management and to improve the operation of electrical distribution networks. However, load management technologies depriving consumers of utilizing their personal resources could be perceived as a consumer right violation by many consumers, and thus, the success of the program is significantly dependent on consumer satisfaction. This paper probes consumer engagement plans through an algorithm to minimize the consumer inconvenience caused by the load management/demand response (DR) program. Four different consumer engagement plans are proposed for consumers with different tolerance levels, starting from most tolerant to the least. Based on the engagement plans chosen, the reduction requests are generated by the algorithm. The second stage of the algorithm will schedule devices to meet the consumer demand and demand reduction request. The mixed integer linear programming (MILP-DR) algorithm, is implemented on a distribution network model. The uniqueness of the algorithm is the consumer tolerance (comfort) levels are given due consideration, based on a fairness of participation basis in the scheme. The is weight updating factor updates the tolerance of the consumer based on their participation (load reduction and duration of reduction)

RISK MANAGEMENT AND PARTICIPATION OF ELECTRIC VEHICLE CONSIDERING TRANSMISSION LINE CONGESTION IN THE SMART GRIDS FOR DEMAND RESPONSE

Demand response (DR) could serve as an effective tool to further balance the electricity demand and supply in smart grids. It is also defined as the changes in normal electricity usage by end-use customers in response to pricing and incentive payments. Electric cars (EVs) are potentially distributed energy sources, which support the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes, and their participation in time-based (e.g., time of use) and incentive-based (e.g., regulation services) DR programs helps improve the stability and reduce the potential risks to the grid. Moreover, the smart scheduling of EV charging and discharging activities supports the high penetration of renewable energies with volatile energy generation. This article was focused on DR in the presence of EVs to assess the effects of transmission line congestion on a 33-bit grid. A random model from the standpoint of an independent system operator was used to manage the risk and participation of EVs in the DR of smart grids. The main risk factors were those caused by the uncertainties in renewable energies (e.g., wind and solar), imbalance between demand and renewable energy sources, and transmission line congestion. The effectiveness of the model in a 33-bit grid in response to various settings (e.g., penetration rate of EVs and risk level) was evaluated based on the transmission line congestion and system exploitation costs. According to the results, the use of services such as time-based DR programs was effective in the reduction of the electricity costs for independent system operators and aggregators. In addition, the results demonstrated that the participation of EVs in incentive-based DR programs with the park model was particularly effective in this regard

Application of demand response to improve voltage regulation with high DG penetration

The ability of a consumer friendly demand response based voltage control (DR-VC) program to improve the voltage regulation in a low voltage distribution network (LVDN) with high penetration of DG is investigated. The use of active and reactive power management to regulate the nodal voltage in a distribution network with simple incremental reduction algorithm, in conjunction with DR, is proposed as a solution for over voltage and undervoltage issues in the LVDN. The algorithm micromanages the load and generation in the network enabling the operator to utilize grid resources economically and efficiently while maintaining fairness between consumers with minimum inconvenience. The algorithm is tested on a representative. 74-load radial urban distribution network (Dublin, Ireland) using consumer load and DG generation profiles. The system is modelled and analysed using COM interface between OpenDSS and MATLAB. The DR is modelled through a mixed integer linear programming (MILP), implemented in CVX, such that consumer inconvenience is prioritized. The DR-VC algorithm is capable of regulating load and generation within normal operation limits during undervoltage and overvoltage scenarios

Cost/Benefit Assessment of a Smart Distribution System With Intelligent Electric Vehicle Charging

In the near future, with more distributed generators connected and new demands arising from the electrification of heat and transport in the distribution networks, infrastructure will become ever more stressed. However, building costly new circuits to accommodate generation and demand growth is time-consuming and environmentally unfriendly. Therefore, active network management (ANM) has been promoted in many countries, aiming to relieve network pressure. Previous research in ANM was focused on distribution areas with significant renewable penetration, where ANM reduced network pressure through significantly enhanced generation curtailment strategies rather than adopting traditional asset investment. This paper proposes the use of electric vehicles (EVs) as responsive demand to complement network stress relief that was purely based on generation curtailment. It is achieved by allowing EVs to absorb excessive renewable generation when they cause network pressure, and it thus can provide additional measures to generation curtailment strategies. The approach is illustrated on a practical extra-high voltage distribution system. The analyses clearly demonstrate the combined management of demand and generation is superior to previous sole generation management. The combined management strategy can achieve 7.9% improvement in utilization of renewable energy, and subsequently increase the net investment profit by £566 k

V2G Transfer of Energy to Various Applications

In today’s world, there is a need of verge of significantant transformation in Electrical Power System. The Vehicle-to-Gird (V2G) concept optimizes this transformation. The PEV typically has a higher capacity Energy Storage System (ESS). Each PEV stores approximately 5-40kWh of energy. This energy can be transferred to the Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H) and Vehicle-to-Building (V2B) as most of the time the vehicle is kept in parking as idle. This paper presents the concept of V2G technology, their classifications, battery storages and types of batteries for V2G