Probabilistic Optimal Power Dispatch in Multi-Carrier Networked Microgrids under Uncertainties

Amir, Vahid; Jadid, Shahram; Ehsan, Mehdi

doi:10.3390/en10111770

Cited by 18 publications

(15 citation statements)

References 46 publications

(59 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2. It can be seen that every microgrid not only connects to the main grid, but also to its two neighbors on both sides [4], [16], [24], [31], [36], [64]. With this topology, the IMMGs can form a line or a ring, and each MG can exchange energy and information directly with its adjacent MGs and the main grid [2], [13], [18], [65].…”

Section: B Model 2: Daisy-chain Topologymentioning

confidence: 99%

“…The centralized controller in IMMGs gathers detailed global information from every individual MG as well as the market information, and then makes decisions by executing an optimization algorithms [13], [32], [64], [71]. In this method, coordination can be satisfied, but it implies higher communication and computation requirements.…”

Section: Scheduling Optimization Structure 1) Centralized Energy Mmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Energy Management in Interconnected Multi-Microgrids

et al. 2019

View full text Add to dashboard Cite

An interconnected multi-microgrids (IMMGs) system takes advantage of various complementary power sources and effectively coordinates the energy sharing/trading among the MGs and the main grid to improve the stability, reliability, and energy efficiency of the system. The core of this structure is to achieve the optimal distribution of energy sharing through proper strategies. However, the volatility and intermittent characteristics of renewable resources, time-varying loads in the MGs, their correlated power generations, and the coupled energy among the MGs during energy trading, all bring about new challenges to achieving a stable operation and optimal scheduling in the power system. Many solutions have been proposed to solve these problems. In this paper, we provide an overview of the current energy management systems (EMS) in IMMGs, focusing on the IMMG structure, EMS objectives, timescales, and scheduling optimization structure. We then provide a review of the distributed optimization algorithms in IMMGs. We conclude this survey with a discussion of future directions. INDEX TERMS Smart grid, microgrid (MG), interconnected multi-microgrids (IMMG), energy sharing, energy management.

show abstract

Section: B Model 2: Daisy-chain Topologymentioning

confidence: 99%

Section: Scheduling Optimization Structure 1) Centralized Energy Mmentioning

confidence: 99%

A Survey of Energy Management in Interconnected Multi-Microgrids

et al. 2019

View full text Add to dashboard Cite

show abstract

“…To be more specific, the proposed model correlates the FEPs of responsive loads for multiple carriers with energy market price, energy purchase and on-site generations. The final prices of previous publication (Amir et al , 2017b) are modeled in equations (13) and (14), based on Figure 4: …”

Section: System Modelmentioning

confidence: 99%

“…This paper follows the work proposed in Amir et al , (2017b); however, this paper develops the economic models of price-responsive loads for both TBDRPs and IBDRPs so that a more comprehensive DSM model is proposed and its impact on load factor and system costs are analyzed. Moreover, the thermal energy wastage after connecting the heat network of MCMGs and comparison of simultaneous and individual operation of multiple carriers on the total cost of NMCMG are analyzed thoroughly.…”

Section: Introductionmentioning

confidence: 99%

Operation of networked multi-carrier microgrid considering demand response

Amir

Jadid

Ehsan

2019

COMPEL

Self Cite

View full text Add to dashboard Cite

Purpose Microgrids are inclined to use renewable energy resources within the availability limits. In conventional studies, energy interchange among microgrids was not considered because of one-directional power flows. Hence, this paper aims to study the optimal day-ahead energy scheduling of a centralized networked multi-carrier microgrid (NMCMG). The energy scheduling faces new challenges by inclusion of responsive loads, integration of renewable sources (wind and solar) and interaction of multi-carrier microgrids (MCMGs). Design/methodology/approach The optimization model is formulated as a mixed integer nonlinear programing and is solved using GAMS software. Numerical simulations are performed on a system with three MCMGs, including combined heat and power, photovoltaic arrays, wind turbines and energy storages to fulfill the required electrical and thermal load demands. In the proposed system, the MCMGs are in grid-connected mode to exchange power when required. Findings The proposed model is capable of minimizing the system costs by using a novel demand side management model and integrating the multiple-energy infrastructure, as well as handling the energy management of the network. Furthermore, the novel demand side management model gives more accurate optimal results. The operational performance and total cost of the NMCMG in simultaneous operation of multiple carriers has been effectively improved. Originality/value Introduction and modeling of the multiple energy demands within the MCMG. A novel time- and incentive-based demand side management, characterized by shifting techniques, is applied to reshape the load curve, as well as for preventing the excessive use of energy in peak hours. This paper analyzes the need to study how inclusion of multiple energy infrastructure integration and responsive load can impact the future distribution network costs.

show abstract

“…Based on the results, the integration of the RER into the MGs will be easily implemented from the power flow point of view. Amir et al focus on the probabilistic analysis of optimal power dispatch in a networked MCMG considering economic aspects. Furthermore, a novel demand response (DR) model is proposed.…”

Section: Introductionmentioning

confidence: 99%

Reliability‐constrained optimal design of multicarrier microgrid

Amir

Azimian

Razavizadeh

2019

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

Summary The unprecedented penetration of distributed generation in distribution energy networks provides utilities with a unique opportunity to manage portions of networks as microgrids (MGs). The implementation of an MG may offer many benefits, such as capital investments deferral, reduction of greenhouse gas emissions, improvement in reliability, and reduction in network losses. Future energy networks will contain various forms of energy which are acquired by mixing several sources and energy storages in the concept called multicarrier microgrid (MCMG). In order to draw the most effective performance from MCMG systems, appropriate design and operation are essential. This paper represents a compound co‐optimization strategy to find the best type and size of components and the associated optimum dispatch in a grid‐tied community MCMG implementing reliability criteria. Here, the required level of reliability is handled within the optimization process to fulfill multiple demands. The mixed‐integer nonlinear programming (MINLP) technique of general algebraic modeling system (GAMS) and the genetic algorithm of MATLAB software are utilized to solve the co‐optimization problem. Additionally, a contemporary time‐based demand response program is modeled to reshape the load curve, as well as prevent the undue use of energy in peak hours. Eventually, the proposed strategy is applied to a test case to select the best components while respecting reliability restrictions. Numerical simulations prove the effectiveness of the proposed expansion planning.

show abstract

Probabilistic Optimal Power Dispatch in Multi-Carrier Networked Microgrids under Uncertainties

Cited by 18 publications

References 46 publications

A Survey of Energy Management in Interconnected Multi-Microgrids

A Survey of Energy Management in Interconnected Multi-Microgrids

Operation of networked multi-carrier microgrid considering demand response

Reliability‐constrained optimal design of multicarrier microgrid

Contact Info

Product

Resources

About