Vehicle-to-grid management for multi-time scale grid power balancing

The mitigation of peak-valley difference and transient power fluctuation are both of great significance to the economy and stability of the power grid. This paper proposes a novel vehicle-to-grid behavior management method that can provide peak-shaving and fast power balancing service to the grid simultaneously. Firstly, a multi-time scale vehicle-to-grid behavior management framework is designed to enable large-scale optimization and real-time control at the same time in vehicle-to-grid scheduling. Then, the grid peak-shaving requirement is modeled as an optimization problem in a centralized V2G state coordinator, where the charging behavior of all grid-connected electric vehicles can be synergistically scheduled. The optimization variable is designed as a group of vehicle-to-grid state control signals that can respond to grid peak-shaving requirements. Further, a V2G power controller is designed to manage the vehicle charging power in real time based on the sampled grid frequency state and discrete state control signals. In the developed scheduling method, the charging power of grid-connected electric vehicles is scheduled by the cooperation between the V2G state coordinator and the power controller. The effectiveness of the proposed methodologies is verified on a microgrid system, and results indicate that the V2G scheduling can achieve multi-time scale grid power balancing. This work can bring dual benefits, enabling system operators to use cheap solutions to manage energy networks and allowing vehicle owners to gain profits from providing V2G services to the grid.</p

Energy Storage Systems for Energy Management of Renewables in Distributed Generation Systems

Distributed generation (DG) systems are the key for implementation of micro/smart grids of today, and energy storages are becoming an integral part of such systems. Advancement in technology now ensures power storage and delivery from few seconds to days/months. But an effective management of the distributed energy resources and its storage systems is essential to ensure efficient operation and long service life. This chapter presents the issues faced in integrating renewables in DG and the growing necessity of energy storages. Types of energy storage systems (ESSs) and their applications have also been detailed. A brief literature study on energy management of ESSs in distributed microgrids has also been included. This is followed by a simple case study to illustrate the need and effect of management of ESSs in distributed systems

Residential Demand Side Management model, optimization and future perspective: A review

The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints

Integration of Massive Plug-in Hybrid Electric Vehicles into Power Distribution Systems: Modeling, Optimization, and Impact Analysis

With the development of vehicle-to-grid (V2G) technology, it is highly promising to use plug-in hybrid electric vehicles (PHEVs) as a new form of distributed energy resources. However, the uncertainties in the power market and the conflicts among different stakeholders make the integration of PHEVs a highly challenging task. Moreover, the integration of PHEVs may lead to negative effects on the power grid performance if the PHEV fleets are not properly managed. This dissertation studies various aspects of the integration of PHEVs into power distribution systems, including the PHEV load demand modeling, smart charging algorithms, frequency regulation, reliability-differentiated service, charging navigation, and adequacy assessment of power distribution systems. This dissertation presents a comprehensive methodology for modeling the load demand of PHEVs. Based on this stochastic model of PHEV, a two-layer evolution strategy particle swarm optimization (ESPSO) algorithm is proposed to integrate PHEVs into a residential distribution grid. This dissertation also develops an innovative load frequency control system, and proposes a hierarchical game framework for PHEVs to optimize their charging process and participate in frequency regulation simultaneously. The potential of using PHEVs to enable reliability-differentiated service in residential distribution grids has been investigated in this dissertation. Further, an integrated electric vehicle (EV) charging navigation framework has been proposed in this dissertation which takes into consideration the impacts from both the power system and transportation system. Finally, this dissertation proposes a comprehensive framework for adequacy evaluation of power distribution networks with PHEVs penetration. This dissertation provides innovative, viable business models for enabling the integration of massive PHEVs into the power grid. It helps evolve the current power grid into a more reliable and efficient system

Online Coordinated Charging of Plug-In Electric Vehicles in Smart Grid to Minimize Cost of Generating Energy and Improve Voltage Profile

This Ph.D. research highlights the negative impacts of random vehicle charging on power grid and proposes four practical PEV coordinated charging strategies that reduce network and generation costs by integrating renewable energy resources and real-time pricing while considering utility constraints and consumer concerns

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

Modeling and Integration of Demand Response and Demand Side Resources for Smart Grid Application in Distribution Systems

Today\u27s electric grid is undergoing drastic changes to evolve into a smart grid. Deregulation of the integrated and monopolistic power system into genco, transco and disco has led to tremendous competition among these players. These entities are in the process of developing innovative smart grid strategies that can improve their reliability and profit. In this thesis work, some of the smart grid initiatives by discos have been explored.;This thesis work is driven by two major objectives. The primary objective is to explore Demand Response (DR), develop its comprehensive model and to analyze various effects and implications of DR on distribution networks. The second major objective of the thesis is to integrate the developed demand response model into a microgrid market optimization. A microgrid network is a real world demonstration of smart grid that integrates and coordinates various demand side resources into its operation. For this reason, a microgrid has been chosen in this work so that it offers a broader scope where in addition to DR models, Battery Energy Storage System (BESS) and Distributed Energy Resources (DER) or Distributed Generation (DG) can also be modeled and integrated.;This thesis develops a model for DR by utilizing consumer behavior modeling considering different scenarios and levels of consumer rationality. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices for different types of consumers. These Price Elasticity Matrices (PEMs) are utilized to calculate the level of demand response for a given consumer. DR thus obtained is applied to a real world distribution network considering a day-ahead real time pricing scenario to study the effects of demand reduction and redistribution on system voltage and losses. Results show considerable boost in system voltage that paves way for further demand curtailment through demand side management techniques like Volt/Var Control (VVC).;Following this, the thesis develops a market optimization model for an islanded microgrid that includes Smart Grid elements namely DR, DERs and BESS. Comprehensive models for DR and BESS have been developed and integrated into the optimization program. Demand Side Bidding (DSB) by DR Aggregators is introduced into the proposed double sided microgrid energy market by utilizing the DR models developed. The optimization program uses Linear Programming (LP) technique to determine the dispatch schedule of DERs, BESS and the level of DR to minimize the operating cost of the microgrid market. A time series simulation of a large microgrid test system is performed to show the feasibility of the proposed market optimization

System modeling and dispatch schedule optimization of combined PV battery system using linear optimization

Master's thesis in Renewable energy (ENE500)Energy storage plays a vital role in paving the way for more renewable penetration. The technology is costly, but intelligent solutions regarding dispatch strategies and system design can help reduce the total cost over the projected lifetime of a system. For this thesis, a customizable linear programming algorithm is created within Python to optimize the battery energy scheduling based on generated PV power, electricity cost and load demand. The commercial system optimization tool HOMER is used to verify the code by running simulations based on historic data collected from Nord Pool and UiAs own photovoltaic system. One benefit of the custom made code is its ability to do day-ahead optimization utilizing data from APIs. To obtain forecasted irradiation and temperature data the Solcast API was used as the only paid service for the Python script to ensure market-leading accuracy

Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems:A Review

Renewable energy sources (RESs) are the replacement of fast depleting, environment polluting, costly, and unsustainable fossil fuels. RESs themselves have various issues such as variable supply towards the load during different periods, and mostly they are available at distant locations from load centers. This paper inspects forecasting techniques, employed to predict the RESs availability during different periods and considers the dispatch mechanisms for the supply, extracted from these resources. Firstly, we analyze the application of stochastic distributions especially the Weibull distribution (WD), for forecasting both wind and PV power potential, with and without incorporating neural networks (NN). Secondly, a review of the optimal economic dispatch (OED) of RES using particle swarm optimization (PSO) is presented. The reviewed techniques will be of great significance for system operators that require to gauge and pre-plan flexibility competence for their power systems to ensure practical and economical operation under high penetration of RESs

Intelligent control of PV co-located storage for feeder capacity optimization

Battery energy storage is identified as a strong enabler and a core element of the next generation grid. However, at present the widespread deployment of storage is constrained by the concerns that surround the techno-economic viability. This thesis addresses this issue through optimal integration of storage to improve the efficiency of the electricity grid. A holistic approach to optimal integration includes the development of methodologies for optimal siting, sizing and dispatch coordination of storage