Conditional scenario-based energy management algorithm with uncertain correlated forecasts
[EN] This paper introduces the application of the novel approach known as Conditional Scenario-Based Model Predictive Control (CSB-MPC) into energy management in a low voltage distribution system (LVDS). The LVDS comprises renewable energy sources (RES) and an energy storage system, with correlated...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/213850 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/213850 |
| Access Level: | acceso abierto |
| Palabra clave: | Conditional scenario Energy storage system (ESS) Low voltage distribution system Microgrid Model predictive control (MPC) Renewable energy sources (RES) Scenario-based MPC Stochastic MPC INGENIERIA DE SISTEMAS Y AUTOMATICA |
| Sumario: | [EN] This paper introduces the application of the novel approach known as Conditional Scenario-Based Model Predictive Control (CSB-MPC) into energy management in a low voltage distribution system (LVDS). The LVDS comprises renewable energy sources (RES) and an energy storage system, with correlated power generation and demand under uncertainty. This correlation is leveraged to derive a reduced set of conditional scenarios and their associated probabilities. This reduced set retains the essential characteristics of a larger set of equiprobable scenarios generated based on information about uncertainties in power generation and demand forecasts. Instead of computationally demanding methods using the larger set, a feasible Scenario-Based Mixed-Integer Linear Program (MILP) is solved by employing the reduced set. This optimisation minimises costs associated with energy consumption from the main grid and the potential waste of generated power. In the objective function, the scenarios are weighted by their probabilities to mitigate the impact of less likely forecasts, leading to efficient LVDS component utilisation and reduced operating costs. Simulation results comparing the performance of the LVDS with CSB-MPC against other MPC approaches, including classic scenario-based, chance-constrained, and deterministic methods, reveal that CSB-MPC consistently exhibits higher probabilities of operational constraint satisfaction, with differences of up to 10% or even 30%. Notably, CSB-MPC achieves these results with similar solution times to classic scenario-based MPC but with lower LVDS operating costs, between 6.62% to 11.31% less. |
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