Day-Ahead Smart Grid Cooperative Distributed Energy Scheduling With Renewable and Storage Integration

Zhang, Yuan; Rahbari-Asr, Navid; Duan, Jie; Chow, Mo-Yuen

doi:10.1109/tste.2016.2581167

Cited by 83 publications

(57 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Renewable energy generation Stochastic optimization Handling date uncertainties of renewable energy [10][11][12] Robust optimization [14][15][16][17] Wind power forecasting Linear methods Increasing the accuracy of prediction model [19,20] Nonlinear methods [24][25][26][27] Microgrid management Ordinary decision theory Optimizing energy-scheduling strategies [28][29][30] Noncooperative games [33][34][35][36] Cooperative games [37][38][39][40] and robust optimization [9]. On the one hand, stochastic optimization provides an effective framework to optimize statistical objective functions while the uncertain numerical data are assumed to follow a proverbial probability distribution.…”

Section: Application Scenarios Solution Methods Optimization Goals LImentioning

confidence: 99%

“…The authors developed a cooperative distributed energy-scheduling algorithm to optimize the energy dispatch problem while considering the integration of renewable generation and energy storage in Ref. [39]. In Ref.…”

Section: Application Scenarios Solution Methods Optimization Goals LImentioning

confidence: 99%

“…It can be treated by the grid as a controllable load or generator and can operate in either islanded or grid-connected mode [5]. However, due to the intermittent and fluctuating characteristics of renewable energy sources and limited generation capacity, the large penetration of uncontrolled and uncoordinated renewable generators into the microgrid especially distribution network will cause a high level of volatility and system disturbances.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy Management in Microgrids: A Combination of Game Theory and Big Data‐Based Wind Power Forecasting

Zhou¹,

Xiong²,

Xu³

et al. 2017

Development and Integration of Microgrids

View full text Add to dashboard Cite

Energy internet provides an open framework for integrating every piece of equipment involved in energy generation, transmission, transformation, distribution, and consumption with novel information and communication technologies. In this chapter, the authors adopt a combination of game theory and big data to address the coordinated management of renewable and traditional energy, which is a typical issue on energy interconnections. The authors formulate the energy management problem as a three-stage Stackelberg game and employ the backward induction method to derive the closed-form expressions of the optimal strategies. Next, we study the big data-based power generation forecasting techniques and introduce a scheme of the wind power forecasting, which can assist the microgrid to make strategies. Simulation results show that more accurate prediction results of wind power are conducive to better energy management.

show abstract

Section: Application Scenarios Solution Methods Optimization Goals LImentioning

confidence: 99%

Section: Application Scenarios Solution Methods Optimization Goals LImentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Management in Microgrids: A Combination of Game Theory and Big Data‐Based Wind Power Forecasting

Zhou¹,

Xiong²,

Xu³

et al. 2017

Development and Integration of Microgrids

View full text Add to dashboard Cite

show abstract

“…The lower control level concentrates on power system modelling and aims to control BESS to track the power reference. An optimal distributed control scheme should be flexible, robust and address the problem with a costeffective way (Kumar Nunna & Srinivasan, 2016;Morstyn, Hredzak, & Agelidis, 2014;Morstyn, Hredzak, Member, & Vassilios, 2016;Xu, Zhang, Hug, Kar, & Li, 2015;Zhang, Rahbari-asr, Duan, & Chow, 2016;Zhao & Ding, 2018).…”

Section: Control Strategy For Distributed Mas-based Microgridmentioning

confidence: 99%

“…In order to protect the privacy of each participant, Zhao & Ding (2018) presented another cooperative optimal solution for multiple battery energy storage system in a microgrid to maintain the supply-demand mismatch under the wind power generation uncertainties. Similarly, Zhang et al (2016) presented an offline cooperative distributed energy scheduling algorithm, which allowed limited information for agent communication and facilitated reconfiguration of the distributed system. Likewise, in (Kumar Nunna & Srinivasan, 2016), a dynamically updated energy management schedule was presented by altering the operation of storage devices and controllable demand response load to overcome the day-ahead forecasting error and system uncertainty.…”

Section: Control Strategy For Distributed Mas-based Microgridmentioning

confidence: 99%

A review of forecasting algorithms and energy management strategies for microgrids

2018

Systems Science & Control Engineering

View full text Add to dashboard Cite

As an autonomous subsystem integrating with the utility, a microgrid system consists of distributed energy sources, power conversion circuits, storage units and adjustable loads. With the high penetration of distributed generators, it is challenging to provide a reliable, consistent power supply for local customers, because of the time-varying weather conditions and intermittent energy outputs in nature. Likewise, the electricity consumption changes due to the season effect and human behaviour in response to the changes in electricity tariff. Therefore, studies on accurate forecasting power generation and load demand are worthwhile in order to solve unit commitment and schedule the operation of energy storage devices. The paper firstly gives a brief introduction about microgrid and reviews forecasting algorithms for power supply side and load demand. Then, the mainstream energy management approaches applied to the microgrid, including centralized control, decentralized control and distributed control schemes are presented. A number of the optimal energy management algorithms are highlighted for centralized controllers based on short-term forecasting information and a generalized centralized control scheme is thus summarized. Consensus protocol is discussed in this paper to solve the cooperative problem under the multi-agent system-based distributed energy system. Finally, the future of energy forecasting approaches and energy management strategies are discussed. ARTICLE HISTORY

show abstract

Hybrid renewable/nonrenewable/storage resources in electrical grid considering active‐reactive losses and depth of discharge

Hemmati

Mahdavi

2021

Int J Energy Res

View full text Add to dashboard Cite

This paper presents a linear model for optimal operation of distributed generations (DGs) including renewable and nonrenewable DGs in the electrical grids.The accurate model of active-reactive losses is formed by two quadratic terms that are linearized by piecewise linear approximation of the quadratic curves.The DGs' technical/economic formulations are also presented based on the linear model. The proposed linear models are combined to implement linear optimal power flow (OPF) in the electrical grid integrated with hybrid diesel/wind/ solar/battery. The model is associated with uncertainties and expressed as stochastic mixed integer linear programming. Both the active and reactive powers of the grid and resources are incorporated. The model optimizes the following design variables: sitting and sizing of energy storage systems and DGs, depth of discharge, energy of battery, operation pattern of battery, operation pattern of non-renewables units, and investment/operational costs. The proposed model is tested on 69-bus distribution grid. The simulation results show that the depth of discharge is optimized on 30% to 95%. Active and reactive losses achieved by the proposed model are 72% and 47% more accurate compared to the other existing models. The voltage profile is improved by about 8%. The network losses are reduced by 24%. The peak shaving is performed at hours 18 to 24.

show abstract

Day-Ahead Smart Grid Cooperative Distributed Energy Scheduling With Renewable and Storage Integration

Cited by 83 publications

References 30 publications

Energy Management in Microgrids: A Combination of Game Theory and Big Data‐Based Wind Power Forecasting

Energy Management in Microgrids: A Combination of Game Theory and Big Data‐Based Wind Power Forecasting

A review of forecasting algorithms and energy management strategies for microgrids

Hybrid renewable/nonrenewable/storage resources in electrical grid considering active‐reactive losses and depth of discharge

Contact Info

Product

Resources

About