Energy Management in Smart Distribution Systems With Vehicle-to-Grid Integrated Microgrids

Nunna, H. S. V. S. Kumar; Battula, Swathi; Doolla, Suryanarayana; Srinivasan, Dipti

doi:10.1109/tsg.2016.2646779

Cited by 117 publications

(48 citation statements)

References 19 publications

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“…PEV charging stations in the market with control capabilities provide opt-out options to drivers in the event of smart charging control or DR. Kamankesh et al 117 demonstrated that smart charging is best among the three charging strategies, namely, uncontrolled, controlled, and smart charging schemes, to overcome issues of grid constraints violation and to achieve economical operation. Energy management of PEV for V2G and G2V mode is discussed in Nunna et al 118 In the case where accurate forecast data to the microgrid controller is not available, the charging-discharging schedule is decided by real-time generation and demand (5 to 15 minutes prior) known as decentralized control. Whereas when accurate forecast data are available on a day ahead or half-day ahead basis, Abbreviations: GHG, greenhouse gas; G2V, grid-to-vehicle; HVAC, heating, ventilation, and air conditioning; PEV, plug-in electric vehicle; V2B, vehicle-tobuilding; V2G, vehicle-to-grid; V2H, vehicle-to-home.…”

Section: Pev Behavior and Ev-emsmentioning

confidence: 99%

Energy management system for smart grid: An overview and key issues

2020

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Summary Energy crisis and the global impetus to “go green” have encouraged the integration of renewable energy resources, plug‐in electric vehicles, and energy storage systems to the grid. The presence of more than one energy source in the grid necessitates the need for an efficient energy management system to guide the flow of energy. Moreover, the variability and volatile nature of renewable energy sources, uncertainties associated with plug‐in electric vehicles, the electricity price, and the time‐varying load bring new challenges to the power engineers to achieve demand‐supply balance for stable operation of the power system. The energy management system can effectively coordinate the energy sharing/trading among all available energy resources, and supply loads economically in all the conditions for the reliable, secure, and efficient operation of the power system. This paper reviews the framework, objectives, architecture, benefits, and challenges of the energy management system with a comprehensive analysis of different stakeholders and participants involved in it. The review paper gives a critical analysis of the distributed energy resources behavior and different programs such as demand response, demand‐side management, and power quality management implemented in the energy management system. Different uncertainty quantification methods are also summarized. This review paper also presents a comparative and critical analysis of the main optimization techniques used to achieve different energy management system objectives while satisfying multiple constraints. Thus, the review offers numerous recommendations for research and development of the cutting‐edge optimized energy management system applicable for homes, buildings, industries, electric vehicles, and the whole community.

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Section: Pev Behavior and Ev-emsmentioning

confidence: 99%

Energy management system for smart grid: An overview and key issues

2020

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“…The connection of buses has not been known before DFR, consequently, (16) is substituted by two inequality constraints (19) and (20) as follows:…”

Section: Load Flow and Radiality Constraintsmentioning

confidence: 99%

A new optimal operation framework for balanced microgrids considering reconfiguration and generation scheduling simultaneously

Sedighizadeh

Shaghaghi‐shahr

Aghamohammadi

et al. 2020

Int Trans Electr Energ Syst

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Summary In this article, the optimal distribution feeder reconfiguration (DFR) and optimal generation scheduling (OGS) are simultaneously taken into account to enhance operational flexibility and efficiency in microgrids (MGs). The proposed model considers different features of MGs, including distributed generation (DG) units, plug in hybrid electric vehicles (PHEVs), and demand response (DR) programs. Three DR programs are modeled including, load shifting, load curtailing, and time of use (TOU) programs. Uncertainties of renewable generations and loads are modeled using a computationally competitive approach. Both controlled and uncontrolled charging/discharging patterns of PHEVs are investigated. To enhance tractability, the proposed model is formulated as a mixed‐integer second‐order cone programing (MISOCP) problem to minimize operational cost subject to technical and financial constraints. To evaluate the performance of the proposed model, it is examined on the balanced 33‐bus test systems in different case studies of DFR and OGS. The optimization problem is solved using the GAMS software package. It is found that PHEVs and DR not only enhances MG operation flexibility, but they also reduce the total operation cost and smooth load profile.

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“…An OS in [116] is similar to the last mentioned, but this OS specializes in the MG operation optimization. An OS in [117] maximizes the profit of owners of EVs which are joined to the V2G system. The OS forecasts energy supply and demand in the PS and watches market prices.…”

Section: Ps Oss Regulating the Charging/discharging Of Electric Vehiclesmentioning

confidence: 99%

Review of Trends and Targets of Complex Systems for Power System Optimization

Vysocky

Mišák

2020

Energies

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Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.

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Energy Management in Smart Distribution Systems With Vehicle-to-Grid Integrated Microgrids

Cited by 117 publications

References 19 publications

Energy management system for smart grid: An overview and key issues

Energy management system for smart grid: An overview and key issues

A new optimal operation framework for balanced microgrids considering reconfiguration and generation scheduling simultaneously

Review of Trends and Targets of Complex Systems for Power System Optimization

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