Smart green charging scheme of centralized electric vehicle stations

This paper presses a smart charging decision-making criterion that significantly contributes in enhancing the scheduling of the electric vehicles (EVs) during the charging process. The proposed criterion aims to optimize the charging time, select the charging methodology either DC constant current constant voltage (DC-CCCV) or DC multi-stage constant currents (DC-MSCC), maximize the charging capacity as well as minimize the queuing delay per EV, especially during peak hours. The decision-making algorithms have been developed by utilizing metaheuristic algorithms including the Genetic Algorithm (GA) and Water Cycle Optimization Algorithm (WCOA). The utility of the proposed models has been investigated while considering the Mixed Integer Linear Programming (MILP) as a benchmark. Furthermore, the proposed models are seeded using the Monte Carlo simulation technique by estimating the EVs arriving density to the EVS across the day. WCOA has shown an overall reduction of 13% and 8.5% in the total charging time while referring to MILP and GA respectively

An insight into the integration of distributed energy resources and energy storage systems with smart distribution networks using demand-side management

Demand-side management (DSM) is a significant component of the smart grid. DSM without sufficient generation capabilities cannot be realized; taking that concern into account, the integration of distributed energy resources (solar, wind, waste-to-energy, EV, or storage systems) has brought effective transformation and challenges to the smart grid. In this review article, it is noted that to overcome these issues, it is crucial to analyze demand-side management from the generation point of view in considering various operational constraints and objectives and identifying multiple factors that affect better planning, scheduling, and management. In this paper, gaps in the research and possible prospects are discussed briefly to provide a proper insight into the current implementation of DSM using distributed energy resources and storage. With the expectation of an increase in the adoption of various types of distributed generation, it is estimated that DSM operations can offer a valuable opportunity for customers and utility aggregators to become active participants in the scheduling, dispatch, and market-oriented trading of energy. This review of DSM will help develop better energy management strategies and reduce system uncertainties, variations, and constraints

Demand side management studies on distributed energy resources: A survey

The number of distributed environmentally friendly energy sources and generators necessitates new operating methods and a power network board to preserve or even increase the efficiency and quality of the power supply. Similarly, the growth of matriculates promotes the formation of new institutional systems, in which power and power exchanges become increasingly essential. Because of how an inactive entity traditionally organizes distribution systems, the DG’s connection inevitably changes the system’s qualifications to which it is connected. As a consequence of the Distributed Generation, this presumption is currently legal and non-existent. This article glides on demand side management and analysis on distributed energy resources. Investigation of DSM along with zonal wise classification has been carried out in this survey. Its merits and applications are also presented.Universidad Tecnológica de Bolíva

Energy Management of Prosumer Communities

The penetration of distributed generation, energy storages and smart loads has resulted in the emergence of prosumers: entities capable of adjusting their electricity production and consumption in order to meet environmental goals and to participate profitably in the available electricity markets. Significant untapped potential remains in the exploitation and coordination of small and medium-sized distributed energy resources. However, such resources usually have a primary purpose, which imposes constraints on the exploitation of the resource; for example, the primary purpose of an electric vehicle battery is for driving, so the battery could be used as temporary storage for excess photovoltaic energy only if the vehicle is available for driving when the owner expects it to be. The aggregation of several distributed energy resources is a solution for coping with the unavailability of one resource. Solutions are needed for managing the electricity production and consumption characteristics of diverse distributed energy resources in order to obtain prosumers with more generic capabilities and services for electricity production, storage, and consumption. This collection of articles studies such prosumers and the emergence of prosumer communities. Demand response-capable smart loads, battery storages and photovoltaic generation resources are forecasted and optimized to ensure energy-efficient and, in some cases, profitable operation of the resources

Modelling electric vehicles use: a survey on the methods

In the literature electric vehicle use is modelled using of a variety of approaches in power systems, energy and environmental analyses as well as in travel demand analysis. This paper provides a systematic review of these diverse approaches using a twofold classification of electric vehicle use representation, based on the time scale and on substantive differences in the modelling techniques. For time of day analysis of demand we identify activity-based modelling (ABM) as the most attractive because it provides a framework amenable for integrated cross-sector analyses, required for the emerging integration of the transport and electricity network. However, we find that the current examples of implementation of AMB simulation tools for EV-grid interaction analyses have substantial limitations. Amongst the most critical there is the lack of realism how charging behaviour is represented

Economic Operation of Virtual Power Plants with Electric Vehicle Charging Stations

Energy management of distributed energy resources (DERs) is challenging due to the distributed and uncertain nature of DERs. To optimally operate DERs and trade their energy as well as energy flexibility for financial benefits, energy management for virtual power plants (VPPs) and electric vehicle (EV) charging stations are investigated in this thesis. The research in this thesis can be summarized into three parts. Part I provides a VPP operation strategy in the electricity market environment. Part II proposes an EV charging station operation strategy considering EV user incentives. Part III develops a coordinated VPP and EV charging station operation framework based on the methods proposed in parts I and II. (1) Economic VPP operation In this part, an optimal VPP operation regime is proposed considering multiple electricity markets and multiple uncertainties. The proposed operation regime handles both the VPP market bidding and unit dispatching problems. To deal with uncertainties, a hybrid stochastic minimax regret optimization model is proposed. To reduce the conservativeness of the formulated optimization models, a self-adaptive algorithm is proposed. (2) Economic EV charging station operation In this part, an EV charging station operation strategy with an EV user incentive program is proposed to improve the EV charging station economic benefit. To maximize the long-term profit of the EV charging station, an optimal incentive price selection model is developed. In the solution methodology, a problem linearization method is first proposed. Then, a distributed solution methodology is developed based on the proposed adaptive alternating-direction-methodof-multipliers algorithm. (3) Economic VPP operation considering EV charging stations i In this part, a multi-stakeholder VPP-charging station system is investigated. Firstly, a coordinated operation framework is proposed for the VPP-charging station system to maximize the total benefit of the system. Then, an improved EV user incentive program is proposed for acquiring EV energy flexibility. At the cost allocation stage, a τ -value cost allocation method is developed. To alleviate the computation burden in calculating the τ -values, a τ -values estimation approach is proposed. The effectiveness of the energy management methods proposed in this thesis is verified through theoretical analysis and numerical simulations. Significant results suggest high potential for practical application in certain scenarios