Optimal sizing of distributed energy resources in smart microgrids: A mixed integer linear programming formulation

Scalfati, Andrea; Iannuzzi, Diego; Fantauzzi, M.; Roscia, Mariacristina

doi:10.1109/icrera.2017.8191125

Cited by 18 publications

(11 citation statements)

References 22 publications

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“…Also in automotive applications, EV EMSs show same problem to know the vehicle's route and traffic information. Therefore, as also suggested in [28], the direct application of optimization algorithms in EMS can be tricky and is more suitable for benchmark tests [19,29], EMS reference solution evaluation [14,30] and the dimensioning of energy systems [31].…”

Section: Q Imentioning

confidence: 99%

Optimization strategies for Microgrid energy management systems by Genetic Algorithms

Leonori

Paschero

Mascioli

et al. 2020

Applied Soft Computing

142

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Grid-connected Microgrids (MGs) have a key role for bottom-up modernization of the electric distribution network forward next generation Smart Grids, allowing the application of Demand Response (DR) services, as well as the active participation of prosumers into the energy market. To this aim, MGs must be equipped with suitable Energy Management Systems (EMSs) in charge to efficiently manage in real time internal energy flows and the connection with the grid. Several decision making EMSs are proposed in literature mainly based on soft computing techniques and stochastic models. The adoption of Fuzzy Inference Systems (FISs) has proved to be very successful due to their ease of implementation, low computational run time cost, and the high level of interpretability with respect to more conventional models. In this work we investigate different strategies for the synthesis of a FIS (i.e. rule based) EMS by means of a hierarchical Genetic Algorithm (GA) with the aim to maximize the profit generated by the energy exchange with the grid, assuming a Time Of Use (TOU) energy price policy, and at the same time to reduce the EMS rule base system complexity. Results show that the performances are just 10% below to the ideal (optimal) reference solution, even when the rule base system is reduced to less than 30 rules.

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Section: Q Imentioning

confidence: 99%

Optimization strategies for Microgrid energy management systems by Genetic Algorithms

Leonori

Paschero

Mascioli

et al. 2020

Applied Soft Computing

142

View full text Add to dashboard Cite

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“…The direct reciprocity of peer-to-peer (P2P) energy trading by the association of prosumers contributes to a dramatic decrease in transmission losses and the minimization of the operational costs in smart residential communities. However, most studies regarding the productive management of Microgrids have been dedicated merely to economic incentives for financial sustainability and energy interchanges with the Smart Grids [17,18].…”

Section: Motivation Of Prosumer Scheduling For Microgridsmentioning

confidence: 99%

Approximation Algorithm-Based Prosumer Scheduling for Microgrids

Shin

2020

Energies

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Since the inherent intermittency and uncertainty of renewable energy resources complicates efficient Microgrid operations, a Demand Response (DR) scheme is implemented for customers in the grid to alter their power-usage patterns. However, for a real-time pricing model at the current DR, the automated decision on the energy price is not trustworthy because of artificial interferences to the power generation. As opposed to energy price, an operational cost-based prosumer scheduling approach would be able to protect the integrity of the power grid operations from deceptive market transactions and assist in robust energy management. To investigate the operational challenges associated with the costs and prosumers in the Microgrid, we focus on formulating the problem mathematically and designing approximation algorithms to solve the problem of how to optimally identify suppliers to minimize the total operational costs associated with providing electricity. We prove the hardness of the scheduling as one of the NP-Hard problems and propose polynomial time algorithms for approximating optimal solutions. With a proper resilience level for reliable power services, the scheduling algorithms include ways to construct not only robust supplier networks, but also group energy communities in terms of black start while minimizing the operational costs. A series of theoretical performances and experimental evaluations also demonstrates the practical effectiveness of this scheduling model for the operations.

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“…Many studies can be found in literature analyzing microgrids' financial feasibility and the correct sizing of their respective Distributed Energy Resources (DERs) [3,4]. In most of the published works the main objective of the microgrid energy management system is to achieve the highest possible economic benefit by minimizing its operational costs [5,6].…”

Section: Introductionmentioning

confidence: 99%

Advantages of Minimizing Energy Exchange Instead of Energy Cost in Prosumer Microgrids

et al. 2019

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Microgrids of prosumers are a trendy approach within the smart grid concept, as a way to increase distributed renewable energy penetration within power systems in an efficient and sustainable way. Single prosumer individual management has been previously discussed in literature, usually based on economic profit optimization. In this paper, two novel approaches are proposed: firstly, a different objective function, relative to the mismatch between generated and demanded power, is tested and compared to classical objective function based on energy price, by means of a genetic algorithm method; secondly, this optimization procedure is applied to batteries’ coordinated scheduling of all the prosumers composing a community, instead of single one, which better matches the microgrid concept. These approaches are tested on a microgrid with two household prosumers, in the context of Spanish regulation for self-consumption. Results show noticeably better performance of mismatch objective function and coordinated schedule, in terms of self-consumption and self-sufficiency rates, power and energy interchanges with the main grid, battery degradation and even economic benefits.

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Optimal sizing of distributed energy resources in smart microgrids: A mixed integer linear programming formulation

Cited by 18 publications

References 22 publications

Optimization strategies for Microgrid energy management systems by Genetic Algorithms

Optimization strategies for Microgrid energy management systems by Genetic Algorithms

Approximation Algorithm-Based Prosumer Scheduling for Microgrids

Advantages of Minimizing Energy Exchange Instead of Energy Cost in Prosumer Microgrids

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