Multi-period flexibility forecast for low voltage prosumers

Pinto, Rui; Bessa, Ricardo J.; Matos, Manuel

doi:10.1016/j.energy.2017.11.142

Cited by 34 publications

(30 citation statements)

References 51 publications

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“…where R 2 is the square of the radius being calculated, x i and x j are support vectors, k is the kernel function. For this problem, it was found that sigmoid kernel is the most suitable type [26]. To be classified as feasible, a trajectory must present a radius lower or equal to the sphere's radius.…”

Section: Machine Learning Modelmentioning

confidence: 99%

See 1 more Smart Citation

Data Economy for Prosumers in a Smart Grid Ecosystem

Bessa

Rua

Abreu

et al. 2018

Proceedings of the Ninth International Conference on Future Energy Systems

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Smart grids technologies are enablers of new business models for domestic consumers with local flexibility (generation, loads, storage) and where access to data is a key requirement in the value stream. However, legislation on personal data privacy and protection imposes the need to develop local models for flexibility modeling and forecasting and exchange models instead of personal data. This paper describes the functional architecture of an home energy management system (HEMS) and its optimization functions. A set of data-driven models, embedded in the HEMS, are discussed for improving renewable energy forecasting skill and modeling multi-period flexibility of distributed energy resources.

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Section: Machine Learning Modelmentioning

confidence: 99%

“…To be classified as feasible, a trajectory must present a radius lower or equal to the sphere's radius. The results for the test case described in [26] are presented in Table 4.…”

Section: Machine Learning Modelmentioning

confidence: 99%

Data Economy for Prosumers in a Smart Grid Ecosystem

Bessa

Rua

Abreu

et al. 2018

Proceedings of the Ninth International Conference on Future Energy Systems

Self Cite

View full text Add to dashboard Cite

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“…Several studies have been presented in the literature on optimal energy management of household PV‐battery integrated systems 5‐15 . From residents' perspective, although application environments of the integrated systems vary depending on specific system configurations (DC‐ or AC‐coupled), 5 rate policies, 6 and metering topologies, 7 the goal of optimization is usually to minimize the electricity cost of users directly 8 or to reduce the grid power fluctuation in the grid‐connected home microgrid 9 .…”

Section: Introductionmentioning

confidence: 99%

“…However, the aging of batteries and the incorporation of new operating scenarios (such as smart load control, multiobjective optimization) complicate battery energy management. The resulting operational constraints and targets often lead to the nonlinear characteristics of the problem 11‐14 . In this case, the evolutionary algorithms are the practical options for solving the relevant complicated optimization.…”

Section: Introductionmentioning

confidence: 99%

Optimal energy management of residential battery storage under uncertainty

Zhou

Feng

et al. 2021

Int Trans Electr Energ Syst

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Summary Backgroud The uncertainty associated with renewable generation and loads imposes various challenges for the economic operation of microgrids. On the residential side, energy storage systems are implemented with high expectations to enhance photovoltaic power consumption and electricity price arbitrage. Aims This paper proposes an expectation‐oriented stochastic model for optimal energy management of household batteries to cope with the uncertain electricity generation and consumption. Materials & Methods The exact cost expectation of daily operation is formulated as an alternative to the conventional scenario‐based average approximation. A predictive control‐based battery scheduling mechanism is developed further to apply the proposed model to intraday energy storage management. Results Simulation results validate the convexity of the proposed model. Discussion Although nonlinear in nature, the resultant optimization problem is proven to be convex, which ensures computable global optimal solutions for the proposed model. Conclusion Simulation results demonstrate the effectiveness of the proposed scheduling strategy compared with existing methods.

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“…Such a VPP can integrate other agents like flexible consumers or ESS from static storages and electric mobility [7], [8], [9]. Following the ongoing development of short-term markets for AS [10], methodologies for the optimal offer of energy and reserve have been proposed for wind farms [11], [12], microgrids [13], and aggregated flexible loads [14] [15]. The optimization models of these methodologies rely on probabilistic forecasts or trajectories of RES production.…”

Section: Introductionmentioning

confidence: 99%

Scenario generation of aggregated Wind, Photovoltaics and small Hydro production for power systems applications

et al. 2019

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This paper proposes a methodology for an efficient generation of correlated scenarios of Wind, Photovoltaics (PV) and small Hydro production considering the power system application at hand. The merits of scenarios obtained from a direct probabilistic forecast of the aggregated production are compared with those of scenarios arising from separate production forecasts for each energy source, the correlations of which are modeled in a later stage with a multivariate copula. It is found that scenarios generated from separate forecasts reproduce globally better the variability of a multi-source aggregated production. Aggregating renewable power plants can potentially mitigate their uncertainty and improve their reliability when they offer regulation services. In this context, the first application of scenarios consists in devising an optimal day-ahead reserve bid made by a Wind-PV-Hydro Virtual Power Plant (VPP). Scenarios are fed into a two-stage stochastic optimization model, with chance-constraints to minimize the probability of failing to deploy reserve in real-time. Results of a case study show that scenarios generated by separately forecasting the production of each energy source leads to a higher Conditional Value at Risk than scenarios from direct aggregated forecasting. The alternative forecasting methods can also significantly affect the scheduling of future power systems with high penetration of weather-dependent renewable plants. The generated scenarios have a second application here as the inputs of a two-stage stochastic unit commitment model. The case study demonstrates that the direct forecast of aggregated production can effectively reduce the system operational cost, mainly through better covering the extreme cases. The comprehensive application-based assessment of scenario generation methodologies in this paper informs the decision-makers on the optimal way to generate short-term scenarios of aggregated RES production according to their risk aversion and to the contribution of each source in the aggregation.

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Multi-period flexibility forecast for low voltage prosumers

Cited by 34 publications

References 51 publications

Data Economy for Prosumers in a Smart Grid Ecosystem

Data Economy for Prosumers in a Smart Grid Ecosystem

Optimal energy management of residential battery storage under uncertainty

Scenario generation of aggregated Wind, Photovoltaics and small Hydro production for power systems applications

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