A new hybrid AI optimal management method for renewable energy communities

Conte, Francesco; D’Antoni, Federico; Natrella, Gianluca; Merone, Mario

doi:10.1016/j.egyai.2022.100197

Cited by 25 publications

(7 citation statements)

References 15 publications

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“…The PV power is used as one of the inputs, which is predicted by a Time Delay Neural Network. A main contribution is the analysis of the relation between forecasting accuracy and economic income [58]. As a case study, they address a public dataset.…”

Section: Neural Network-based Forecasting Methodsmentioning

confidence: 99%

“…Nonlinear models such as neural networks presented in several works in the literature [27,34,44,52,58,59] are data-driven approaches that can perform nonlinear mappings in data patterns. They are popular due to the fact that they are known as universal function approximators and could fit well to data obtained in different applications and scenarios.…”

Section: Strengths and Weaknesses Of Forecasting Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review

de Oliveira,

de Mattos Neto,

Siqueira

et al. 2023

Energies

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The worldwide appeal has increased for the development of new technologies that allow the use of green energy. In this category, photovoltaic energy (PV) stands out, especially with regard to the presentation of forecasting methods of solar irradiance or solar power from photovoltaic generators. The development of battery energy storage systems (BESSs) has been investigated to overcome difficulties in electric grid operation, such as using energy in the peaks of load or economic dispatch. These technologies are often applied in the sense that solar irradiance is used to charge the battery. We present a review of solar forecasting methods used together with a PV-BESS. Despite the hundreds of papers investigating solar irradiation forecasting, only a few present discussions on its use on the PV-BESS set. Therefore, we evaluated 49 papers from scientific databases published over the last six years. We performed a quantitative analysis and reported important aspects found in the papers, such as the error metrics addressed, granularity, and where the data are obtained from. We also describe applications of the BESS, present a critical analysis of the current perspectives, and point out promising future research directions on forecasting approaches in conjunction with PV-BESS.

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Section: Neural Network-based Forecasting Methodsmentioning

confidence: 99%

Section: Strengths and Weaknesses Of Forecasting Modelsmentioning

confidence: 99%

Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review

de Oliveira,

de Mattos Neto,

Siqueira

et al. 2023

Energies

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show abstract

“…In [13]- [15] the SE is maximized according to the Italian law definition. In these works, MPC is applied to some case studies where a common PV plant is coupled with an energy storage system so as to maximize the SE, leaving the members free to consume electrical energy at their convenience.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, extending and improving the approach outlined in [13]- [15], a different case study is considered. The REC is composed by a set of Smart Homes (SHs) that share a unique PV plant.…”

Section: Introductionmentioning

confidence: 99%

Optimal Coordination and Discount Allocation in Residential Renewable Energy Communities with Smart Home Appliances

Conte¹,

Silvestro²,

Vinci³

et al. 2023

Preprint

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This paper proposes an optimal management strategy for a Renewable Energy Community defined according to the Italian legislation. The specific case study is composed by a set of houses equipped with smart appliances, that share a PV plant. The objective is to minimize the cost of electrical energy use for each member of the community, taking into account the discount achievable from government incentives with proper shaping of the community daily consumption. Such incentives are indeed proportional to the shared energy, i.e. the portion of the renewable energy consumed at each hour by community members. The management algorithm allows an optimal coordination of houses power demands, according to the degree of flexibility granted by users. Moreover, a policy to fairly distribute the obtained discount is introduced. Simulation results show the potentialities of the approach.

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“…According to the results, using a linear profile for the contribution factor and load shift led to daily costs for energy and degradation of $68.27 and $ 0.81, respectively. When compared to the conventional energy management system, which ignores grid power and load shifting when using battery storage, these costs reflect a 25.66% reduction in energy expenditures and a 91.72% reduction in battery degradation costs (Conte et al, 2022). This approach makes predictions about the future values of community energy attributes using a time-delayed neural network.…”

Section: Introductionmentioning

confidence: 99%

Energy management strategies of hybrid renewable energy systems: A review

Ibrahim

2023

Wind Engineering

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Burning fossil fuels results in more emissions than generating electricity from renewable sources. The transition to renewable energy from fossil fuels, which currently produce the majority of emissions, is essential to preventing the climatic disaster. Hybrid energy generation systems are still in their infancy. It is envisaged that future technology developments would lead to greater application and more economical goods. There will be more standardised designs, which will make it easier to select a system that is suitable for a certain application. The components will communicate more with one another. As a result, control, monitoring, and diagnosis will be made simpler. The hybrid energy system (HES), also known as hybrid power, is expected to be the long-term power solution for microgrid (MG) systems. This study compares and contrasts several theories and conventional approaches to controlling HRES’s control and energy consumption. A successful energy management strategy has been created using a variety of methods and procedures. The effectiveness of an EMS is determined by its control architecture and the solution approach used; common topologies include hierarchical, decentralised and centralised EMS. Supply side management and demand side management, two EMS components, will be discussed later. The three EMS control architectures are examined in this section. In order to determine the most practical and dependable solution with the lowest Net present cost (NPC), COE and realistic environmental consequences, various hybridisation cases of a PV panel, wind turbine, battery storage and diesel generator are designed, analysed and compared using DSM. The results of taking into account DSM indicated a reduction in CO2 emissions of 25%, NPC emissions of 14.8%, COE emissions of 14% and an increase in RF emissions of 8.5%. Two fundamental metrics – the DSM Quality Index for technical benefits and the DSM Appreciation Index for economic advantages – are used to assess the technical and economic benefits of DSM.

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A new hybrid AI optimal management method for renewable energy communities

Cited by 25 publications

References 15 publications

Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review

Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review

Optimal Coordination and Discount Allocation in Residential Renewable Energy Communities with Smart Home Appliances

Energy management strategies of hybrid renewable energy systems: A review

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