Multi-stage stochastic framework for energy management of virtual power plants considering electric vehicles and demand response programs

Sheidaei, F.; Ahmarinejad, Amir

doi:10.1016/j.ijepes.2020.106047

Cited by 91 publications

(34 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vast amounts of research have been dedicated to finding good usage of EV in the micro grid demand response beside the main purpose of being an ecofriendly transport vehicle [28][29][30][31][32]. EV has been considered to overcome the power generation fluctuations [33], and smoothed the wind power [34] and was part of the stochastic mixed-integer linear programming (MILP) model [35]. The research in this paper considers the use of EVs depending on their presence at home.…”

Section: Ev In H-mgsmentioning

confidence: 99%

Home-Microgrid Energy Management Strategy Considering EV’s Participation in DR

et al. 2021

View full text Add to dashboard Cite

Electric vehicles (EVs) have a lot of potential to play an essential role in the smart power grid. EVs not only can reduce the amount of emission yielded from fossil fuels but also can be considered as an energy storage system (ES) and a backup system. EVs could support the demand response (DR) strategy that is considered as utmost importance to shift electricity demand in peak hours. This article aims to assess the impact of the presence of EV on DR strategy in a home-microgrid (H-MG). In order to reach the optimal set point, our energy management system (EMS) has been merged with differential evolution (DE) method. The results were auspicious and showed that the proposed method could decrease market clearing price (MCP) by 26% and increase the performance of DR by 17%.

show abstract

Section: Ev In H-mgsmentioning

confidence: 99%

Home-Microgrid Energy Management Strategy Considering EV’s Participation in DR

et al. 2021

View full text Add to dashboard Cite

show abstract

“…When s ≠ t, E st is called cross-elasticity, which implies a positive value, according to a previous study. 12 The load fluctuation generated by the PBDR is expressed as follows:…”

Section: Day-ahead Dispatching Modelmentioning

confidence: 99%

“…When s = t , E st is referred to as self‐elasticity, which implies a negative value. When s ≠ t , E st is called cross‐elasticity, which implies a positive value, according to a previous study 12 . The load fluctuation generated by the PBDR is expressed as follows:

normalΔ L_{italicPB, t} = L_{t} \times ({}, E_{tt} \times \frac{[P_{t} - P_{t}^{0}]}{P_{t}^{0}} + \sum_{\begin{array}{l} s = 1 \\ s \neq t \end{array}}^{24} E_{st} \times \frac{[P_{s} - P_{s}^{0}]}{P_{s}^{0}}) .

…”

Section: Three‐level Energy Interactive Management Modelmentioning

confidence: 99%

“…After self‐demand is satisfied, VPPs can transmit surplus electricity to the upper power grid (UPG) by balance transactions in the power market. Sheidaei and Ahmarinejad 12 developed a hierarchical model for simultaneous modeling of a VPP energy management problem to cover fluctuations at minimum cost. Yi et al 13 formulated a bi‐level optimization model for VPP bidding in multiple retail markets (including active power, reactive power, and spinning reserve market).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Three‐level interactive energy management strategy for optimal operation of multiple virtual power plants considering different time scales

Tan

Lin

et al. 2020

Int J Energy Res

View full text Add to dashboard Cite

Virtual power plants (VPPs) integrate distributed energy effectively and overcome geographical restrictions. This could improve the broad benefits of distributed energy generation. Owing to the intermittency of distributed wind and photovoltaic power at different time scales, when multiple VPPs exist, the interactive energy management strategy should be addressed in terms of two dimensions, namely multi-operator and multi-timescale (day-ahead (24 hours), intraday (4 hours), and real-time (1 hour)). In this study, an interactive energy management framework for multiple VPPs was designed. Furthermore, a three-level energy-coordinated management model was employed for multi-VPP optimal operation including day-ahead cooperative scheduling, intraday noncooperative bidding, and real-time cooperative reserve. To solve this model, a chaotic search algorithm was applied to improve the ant-colony optimization algorithm. The resulting improved chaotic ant-colony optimization algorithm is proposed to increase the optimization velocity and solution efficiency. Finally, a demonstration project, namely the Sanliduo's VPP in Guangxi Province in China, was taken as an example. The following points can be stressed according to the obtained results: (a) the proposed model implements dispatching-balance-reserve interactive optimization such that the output of wind and photovoltaic power increased by 3.42% and 1.85%, respectively, and the revenue increased by 2.53%; (b) the proposed solution algorithm develops the optimal global strategy: compared with the ant-colony algorithm, the average convergence time increased by 44 seconds, and the supply cost decreased by 1.2%; (c) the robust coefficient provides effective risk decisionmaking tools for different types of decision-makers considering the operational characteristics of VPPs; (d) the openness of power markets is positively correlated to the balance revenue, and higher levels are beneficial for VPPs.

show abstract

“…Abdul Latif et al [22,23] proposed using demand response to analyze a microgrid integrated with photovoltaics, wind turbines, fuel cells, diesel generators, and energy storage systems and established an optimal energy distribution strategy, but did not consider how demand response affected the scheduling of various parts of the microgrid. To compensate for the output fluctuation of renewable power at the lowest costs, F. Sheidaei [24] added two demand response methods, including real-time pricing and key peak pricing, to the microgrid to compensate for the WPP and PV's fluctuations with minimal cost. Sahbasadat [25] analyzed three simulation results and studied the impact of DR on the cost of microgrids.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization Model considering Demand Response and Uncertainty of Generation Side of Microgrid

Wang

Zhou

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

To make better use of renewable power generation, a microgrid system is constructed, containing a multitype power supply and a multienergy storage structure. A multiobjective scheduling optimization model is proposed considering MS differential cost, power abandonment cost, and operation income, and demand response models are introduced to improve the ability of storage and users’ participating in peak shaving. To solve the uncertainty of WPP and PV’s output, the scenario reduction method is adopted, and a single target conversion of multiobjective functions is made through a desion attribute table. The results show that scenario reduction can put a brake on the impact of WPP and PV’s output uncertainty on system dispatch; demand response helps solve the problem of poor matching between the source and load. ESS’s co-operative effect can improve the system’s stability.

show abstract

Multi-stage stochastic framework for energy management of virtual power plants considering electric vehicles and demand response programs

Cited by 91 publications

References 33 publications

Home-Microgrid Energy Management Strategy Considering EV’s Participation in DR

Home-Microgrid Energy Management Strategy Considering EV’s Participation in DR

Three‐level interactive energy management strategy for optimal operation of multiple virtual power plants considering different time scales

Multiobjective Optimization Model considering Demand Response and Uncertainty of Generation Side of Microgrid

Contact Info

Product

Resources

About