Optimal Cost Management of Distributed Generation Units and Microgrids for Virtual Power Plant Scheduling

Taheri, Seyed Iman; Salles, Maurício B. C.; Costa, Éder Silva

doi:10.1109/access.2020.3039169

Cited by 32 publications

(15 citation statements)

References 45 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [11], a joint day-ahead and real-time market for VPP with DR model has been constructed using a twolevel robust optimization model to minimize penalty in realtime market considering an uncertainty margin. Authors in [12] have presented a hybrid optimization model including grid search algorithm and a proprietary derivative-free algorithm for DGs, ESSs, and MGs in VPP. A DAM bidding strategy for a VPP as a price maker to minimize RTM penalty has been studied through uncertain resources modeling using interval interpretation [13].…”

Section: Figure 1 Vpp System Composition Diagrammentioning

confidence: 99%

See 1 more Smart Citation

Optimal Operation Strategy of Virtual Power Plant Considering Real-Time Dispatch Uncertainty of Distributed Energy Resource Aggregation

Lee

Won

2021

IEEE Access

View full text Add to dashboard Cite

Virtual power plant (VPP) technologies continue to develop to embrace various types of distributed energy resources (DERs) that have inherent real-time uncertainty. To prevent side effects on the power system owing to the uncertainty, the VPP should manage its internal resources' uncertainty as a whole. This paper proposes an optimal operation strategy for a VPP participating in day-ahead and real-time energy market so that a distributed energy resource aggregation (DERA) can cope with real-time fluctuation due to uncertainties while achieving its maximum profit. The proposed approach has bidding models of the DERAs including microgrid, electric vehicle aggregation, and demand response aggregation, as well as the VPP. The VPP determines internal prices applied to the DERA by evaluating its real-time responses to the day-ahead schedule and updating proposed pricing function parameters, and the DERA adjusts its energy reserves. By repeating this coordination process, the VPP can establish an optimal operation strategy to manage real-time uncertainty on the DERA's own. The effectiveness of the proposed strategy is verified by identifying a capability of the DERA to cope with real-time fluctuation through scenario-based simulations. The result shows that the VPP can reduce 1.6% of cost while the internal price applied to the DERA is close to the maximum.

show abstract

Section: Figure 1 Vpp System Composition Diagrammentioning

confidence: 99%

“…In most of the literature, only individual resources have been addressed as participating in a VPP, and participation of aggregation resources has rarely been considered except for [10]- [12]. As mentioned in [6], a distributed energy resource aggregation (DERA) such as MGs can also participate in VPP.…”

Section: Figure 1 Vpp System Composition Diagrammentioning

confidence: 99%

Optimal Operation Strategy of Virtual Power Plant Considering Real-Time Dispatch Uncertainty of Distributed Energy Resource Aggregation

Lee

Won

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Equation ( 6) models the area upper the line BC. Also, the area above the line CD is represented by (7). Equations ( 8) and ( 9) are stated to satisfy the operation of the CHP unit in its permitted range.…”

Section: Chp Unitsmentioning

confidence: 99%

“…Given the VPP concept, achieving coordinated optimization control of distributed generations is much easier than before, and the efficiency and reliability of the power system are increased, with less investment in infrastructure. 6,7 Deployment of multi-energy systems (MESs) into VPPs has considerably increased due to environmental and economic issues in recent years. 8 As one of the efficient MESs, combined heat and power (CHP) units could be deployed into VPPs to introduce a developed structure of VPP termed as a virtual multi-energy plant (VMEP).…”

Section: Introductionmentioning

confidence: 99%

Robust self‐scheduling of a virtual multi‐energy plant in thermal and electricity markets in the presence of multi‐energy flexible technologies

Pezhmani

Mirzaei

Zare

et al. 2021

Intl J of Energy Research

View full text Add to dashboard Cite

Virtual power plant (VPP) aggregates a diverse set of renewable energy sources, energy storages, and flexible loads to operate them as a single power plant in the energy markets. With the advent of multi-energy systems, the operation and mechanism of traditional VPPs are enhanced for acting in multiple markets and providing more flexibility for energy management systems.In this work, a robust optimization method is proposed for optimal selfscheduling of a new VPP termed as a virtual multi-energy plant (VMEP) in electricity and thermal markets in the presence of different energy storages.The proposed VMEP consists of wind turbines, heat-only units, combined heat and power units, photovoltaic panels, electrical energy storages, and thermal storages. Also, thermal and electrical price-based demand response (DR) programs are employed to shift the multi-carrier energy demand of the VMEP's consumers into low price periods. In addition, a robust decisionmaking model is implemented to deal with the uncertainty of electricity market prices in the optimal self-scheduling of the VMEP without the need for a probability distribution function. The simulation results demonstrate that the implementation of multi-energy DR programs can increase the VMEP's daily profit by about 8.5%. Additionally, the economic performance of the VMEP in multi-energy markets is enhanced up to 10.5% by utilizing multi-carrier energy storages. K E Y W O R D Scombined heat and power systems, multi-carrier energy storage, multi-energy demand response, robust optimization, virtual power plant

show abstract

“…This anarchic penetration may lead to a degradation in operations and voltage stability. To support the growth of DERs adoption, applying developing methods to the distribution system is imperative 1,2 . The developing approaches for the distribution system contain placement and sizing automatic voltage regulators (AVRs), placement of controlled/fixed capacitors, etc.…”

Section: Introductionmentioning

confidence: 99%

Supporting distributed energy resources with optimal placement and sizing of voltage regulators on the distribution system by an improved teaching‐learning‐based optimization algorithm

Taheri

Salles

Khan

2021

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

Summary The continuous increase of the distributed energy resources (DERs) penetration levels leads to voltage stability problems in the distribution system. One of the approaches for the mentioned emerging challenge is the proper placement of automatic voltage regulators (AVRs). This paper investigates the optimal placement and sizing of AVRs in a distribution network by presenting a new modification of the teaching‐learning‐based optimization (TLBO) algorithm. The objective functions consist of minimizing the distribution system voltage deviation, energy generation cost, and electrical losses. The modification improves the convergence velocity and accuracy of the TLBO algorithm using the combination of mutation technique and quasi‐opposition‐based‐learning concept. This paper compares the performance of the proposed algorithm with other famous evolutionary algorithms. The test distribution system contains installed DERs that work more efficiently after the placement of AVRs based on the mentioned objective functions by the proposed optimization algorithm. The simulation results display the best optimization algorithms for AVRs placement with a significant level of less than 0.10 (ie, probability‐value). The proposed multiobjective optimization algorithm's considerable merit is the accuracy and convergence velocity in solving this specific optimization problem.

show abstract

Optimal Cost Management of Distributed Generation Units and Microgrids for Virtual Power Plant Scheduling

Cited by 32 publications

References 45 publications

Optimal Operation Strategy of Virtual Power Plant Considering Real-Time Dispatch Uncertainty of Distributed Energy Resource Aggregation

Optimal Operation Strategy of Virtual Power Plant Considering Real-Time Dispatch Uncertainty of Distributed Energy Resource Aggregation

Robust self‐scheduling of a virtual multi‐energy plant in thermal and electricity markets in the presence of multi‐energy flexible technologies

Supporting distributed energy resources with optimal placement and sizing of voltage regulators on the distribution system by an improved teaching‐learning‐based optimization algorithm

Contact Info

Product

Resources

About