Optimal participation of low voltage renewable micro-grids in energy and spinning reserve markets under price uncertainties

Fazlalipour, Pary; Ehsan, Mehdi; Mohammadi‐Ivatloo, Behnam

doi:10.1016/j.ijepes.2018.04.010

Cited by 29 publications

(11 citation statements)

References 36 publications

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“…PV measurements are clustered, while synthetical driving profiles are generated to account for randomness in the electric vehicle availability. A different approach is employed by Fazlalipour et al [10] who investigate the optimal participation of a renewable microgrid in the market, with a non-linear program. A lognormal probability distribution is assumed to model price uncertainties.…”

Section: Review Of the State Of The Art Justification And Objectivesmentioning

confidence: 99%

A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization

et al. 2020

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As renewable energy installation costs decrease and environmentally-friendly policies are progressively applied in many countries, distributed generation has emerged as the new archetype of energy generation and distribution. The design and economic feasibility of distributed generation systems is constrained by the operation of the microgrid, which has to consider the uncertainty of renewable energy sources, consumption habits and electricity market prices. In this paper, a mathematical model intended to optimize the design and economic feasibility of a microgrid is proposed. After a search in the state-of-the-art, weaknesses and strengths of existing models have been identified and taken into account for building the present model. The present model should be seen as a basis on which other models can be built upon, hence a complete definition of the different sub-models is stated: uncertainty modelling, optimization technique, physical constraints and regulatory framework. One of the main features presented is the generation of synthetic data in uncertainty modelling, employed to enhance the reliability of the model by taking into account a longer time horizon and a shorter time step. Results show significant details about energy management and prove the suitability of using a stochastic approach rather than deterministic or intuitive ones to perform the optimization.

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Section: Review Of the State Of The Art Justification And Objectivesmentioning

confidence: 99%

A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization

et al. 2020

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“…Authors of Reference 32 have developed a stochastic optimal bidding framework of a renewable‐based MG to contribute to the energy and reserve markets. In Reference 33, stochastic‐based framework has been introduced for optimal energy scheduling of a renewable‐based MG with a parking lot and the DR programs to take part in both energy and reserve markets.…”

Section: Introductionmentioning

confidence: 99%

Robust PSO ‐based framework for concurrent active/reactive and reserve management in microgrids in the presence of energy storage systems

2021

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Summary Nowadays, concurrent participation of distributed energy resources in providing electrical energy and ancillary services brings many benefits. Also, energy storage systems (ESSs) along with renewable generations have a growing penetration rate in modern power systems aimed at declining environmental issues. Due to the uncertainties of intermittent nonpredictable power resources in microgrids (MGs), high integration of these products leads to an increase in ancillary services requirements and it necessitates the coordinated management of these technologies with the ESSs. In this paper, for the first time, a robust model based on particle swarm optimization (PSO) metaheuristic is developed to cope with the uncertainty of renewables in concurrent active/reactive and reserve management problem in the MG with ESS. The robust framework provides a medium priority in comparison with deterministic and stochastic techniques. The objective function of the robust concurrent active/reactive and reserve scheduling problem in MGs is expressed as maximizing social welfare (SW). The proposed model is carried out using a max‐min optimization scheme. The robust design will be attained in such a way that the maximizer at the outer level seeks an optimal solution against the worst‐case objective function achieved through the minimizer at the inner level taking into account the uncertainty neighborhood. The performance of the presented approach has been evaluated on a typical MG. Simulation results verify that the suggested robust‐PSO technique will aid MG operators to decrease daily operational costs and to yield a higher SW.

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“…The authors of [30] have proposed an energy management model based on the virtual power plant (VPP) scheme to examine the capability of the WTs and synchronous distributed generations in the coupled energy and reserve market. Authors of [31] have developed a stochastic optimal bidding framework of a renewable-based MG to contribute in the energy and reserve markets. In [32], stochastic-based programming has been introduced for optimal energy management of the renewable-based MG with a parking lot and DR programs to take part in both energy and reserve markets.…”

Section: Introductionmentioning

confidence: 99%

Robust Particle Swarm Optimization for Active/Reactive and Reserve Scheduling in a Grid-connected Microgrid With Energy Storage Systems

Rasouli

Bigdeli

Samimi

2021

Preprint

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Background: In recent years, simultaneous participation in electrical energy and ancillary services markets has been very profitable for distributed energy resources (DERs). Moreover, the presence of renewable generations along with energy storage systems (ESS) is bringing a significant contribution to modern distribution systems. High penetration of non-predictable power sources in microgrids (MGs), due to the uncertainties of these products, increases the need for ancillary services and the management and coordination of these technologies combined with the ESSs. Results: For the first time, this paper develops a robust particle swarm optimization model to handle the uncertain renewable power production involved in the joint active/reactive and reserve scheduling of a smart MG. The robust optimization approach has a medium priority compared to deterministic and stochastic ones. The objective function utilized for the optimal joint active/reactive and reserve scheduling of an MG is defined as maximizing social welfare, which is accomplished based on a max-min optimization model. The robust optimal solution can be achieved in such a way that the maximizer at the outer level makes an optimal decision against the worst-case objective function, which is acquired based on the minimizer at the inner level considering the uncertainty neighborhood. Conclusions: The effectiveness of the proposed method is examined on a 33-bus MG test system. Simulation results prove that the proposed RPSO model can help MG operators to reduce scheduling costs to obtain a higher social welfare. The consideration of more uncertainty in renewable energy resources production leads to higher operation costs, especially reserve costs. Integration of robustness against uncertainty in the joint active/reactive and reserve management in the smart MGs leads to a more robust operation at the expense of higher costs.

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Optimal participation of low voltage renewable micro-grids in energy and spinning reserve markets under price uncertainties

Cited by 29 publications

References 36 publications

A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization

A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization

Robust PSO ‐based framework for concurrent active/reactive and reserve management in microgrids in the presence of energy storage systems

Robust Particle Swarm Optimization for Active/Reactive and Reserve Scheduling in a Grid-connected Microgrid With Energy Storage Systems

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