Controlling Microgrids Without External Data: A Benchmark of Stochastic Programming Methods

Microgrids are local energy systems that integrate energy production, demand, and storage units. They are generally connected to the regional grid to import electricity when local production and storage do not meet the demand. In this context, Energy Management Systems (EMS) are used to ensure the balance between supply and demand, while minimizing the electricity bill, or an environmental criterion. The main implementation challenges for an EMS come from the uncertainties in the consumption, the local renewable energy production, and in the price and the carbon intensity of electricity. Model Predictive Control (MPC) is widely used to implement EMS but is particularly sensitive to the forecast quality, and often requires a subscription to expensive third-party forecast services. We introduce four Multistage Stochastic Control Algorithms relying only on historical data obtained from on-site measurements. We formulate them under the shared framework of Multistage Stochastic Programming and benchmark them against two baselines in 61 different microgrid setups using the EMSx dataset. Our most effective algorithm produces notable cost reductions compared to an MPC that utilizes the same uncertainty model to generate predictions, and it demonstrates similar performance levels to an ideal MPC that relies on perfect forecasts

Usage de la simulation pour valider des algorithmes d'optimisation de maintenance

International audienceIndustrial end users are currently facing an increasing need to reduce the risk of unexpected equipment failures and to optimize their maintenance. At Schneider Electric, we suggest using physical models for ageing anticipation, complemented by machine learning models for short term anomaly detection. Physical models are elaborated by decomposing systems into their constitutive components, associating them to degradation modes and finally to appropriate kinetic laws. Simulating these models allows to compare different maintenance strategies and optimize them. Machine learning models are estimated incrementally – from a “normal” data set – in order to predict the expected behavior and detect statistically significant deviation. A human operator confirms the detected faults. Optimal parameters for this entire procedure can be found via simulation.Les industries ont un besoin croissant de réduire le risque de défaillances imprévues d'équipements et d'optimiser leurs opérations de maintenance. Chez Schneider Electric, nous proposons d'utiliser des modèles physiques pour l'anticipation du vieillissement à long terme, complétés par des modèles d'apprentissage automatique pour la détection de défauts à court terme. Les modèles physiques sont élaborés en décomposant les systèmes en leurs éléments constitutifs, puis en associant ceux-ci à des modes de dégradation et enfin aux lois cinétiques appropriées. La simulation de ces modèles permet de comparer différentes stratégies de maintenance et d'optimiser celle-ci. Les modèles d'apprentissage automatique sont entraînés de manière incrémentale, à partir d'un échantillon dit « normal », afin de prédire la valeur attendue et de détecter des écarts statistiquement significatifs. Un opérateur humain valide les fautes détectées. La simulation permet de trouver les paramètres optimaux de l'ensemble de cette procédure

Controlling Microgrids Without External Data: A Benchmark of Stochastic Programming Methods

Microgrids are local energy systems that integrate energy production, demand, and storage units. They are generally connected to the regional grid to import electricity when local production and storage do not meet the demand. In this context, Energy Management Systems (EMS) are used to ensure the balance between supply and demand, while minimizing the electricity bill, or an environmental criterion. The main implementation challenges for an EMS come from the uncertainties in the consumption, the local renewable energy production, and in the price and the carbon intensity of electricity. Model Predictive Control (MPC) is widely used to implement EMS but is particularly sensitive to the forecast quality, and often requires a subscription to expensive third-party forecast services. We introduce four Multistage Stochastic Control Algorithms relying only on historical data obtained from on-site measurements. We formulate them under the shared framework of Multistage Stochastic Programming and benchmark them against two baselines in 61 different microgrid setups using the EMSx dataset. Our most effective algorithm produces notable cost reductions compared to an MPC that utilizes the same uncertainty model to generate predictions, and it demonstrates similar performance levels to an ideal MPC that relies on perfect forecasts

Controlling Microgrids Without External Data: A Benchmark of Stochastic Programming Methods

Microgrids are local energy systems that integrate energy production, demand, and storage units. They are generally connected to the regional grid to import electricity when local production and storage do not meet the demand. In this context, Energy Management Systems (EMS) are used to ensure the balance between supply and demand, while minimizing the electricity bill, or an environmental criterion. The main implementation challenges for an EMS come from the uncertainties in the consumption, the local renewable energy production, and in the price and the carbon intensity of electricity. Model Predictive Control (MPC) is widely used to implement EMS but is particularly sensitive to the forecast quality, and often requires a subscription to expensive third-party forecast services. We introduce four Multistage Stochastic Control Algorithms relying only on historical data obtained from on-site measurements. We formulate them under the shared framework of Multistage Stochastic Programming and benchmark them against two baselines in 61 different microgrid setups using the EMSx dataset. Our most effective algorithm produces notable cost reductions compared to an MPC that utilizes the same uncertainty model to generate predictions, and it demonstrates similar performance levels to an ideal MPC that relies on perfect forecasts