This paper presents an optimization model for Home Energy Management Systems from an aggregator’s standpoint. The aggregator manages a set of resources such as PV, electrochemical batteries and thermal energy storage by means of electric water heaters. Resources are managed in order to participate in the day-ahead energy and local flexibility markets, also considering grid constraint support at the Point of Common Coupling. The resulting model is a Mixed-Integer Linear Programming problem in which the objective is to minimize day-ahead operation costs for the aggregator while complying with energy commitments in the day-ahead market and local flexibility requests. Three sources of uncertainty are considered: energy prices, PV production and load. Adjustable Robust Optimization is used to find a robust counterpart of the problem for including uncertainty. The results obtained show that using robust optimization allows strategic bidding to capture uncertainties while complying with obligations in the wholesale and local market. Data from a real-life energy community with 25 households is used to validate the proposed robust bidding methodology.

Deriving decisions from data typically involves a sequential process with two components, forecasting and optimization. Forecasting models learn by minimizing a loss function that stands as a proxy for task-specific costs without considering the downstream optimization, which hinders performance and obscures the impact of data on decisions. This work proposes a single data-driven module that leverages the structure of the optimization component and learns a policy conditioned on explanatory data. Specifically, we describe an algorithm to train decision trees by minimizing task-specific costs and prescribe decisions via weighted Sample Average Approximation. We then develop a generic framework to assess the impact of explanatory data on prescriptive performance. To illustrate the efficacy of the proposed approach, we consider two case studies related to trading renewable energy. First, we examine trading in a day-ahead market and propose strategies that balance trading decisions and predictive accuracy. Next, we append a storage device and co-optimize the day-ahead offers and the operational policy, based on a tractable approximation using the linear decision rule approach. The empirical results demonstrate improved prescriptive performance compared to solutions derived under the standard modeling framework. Further, we provide insights on how explanatory data impact optimization performance under different market designs.

This paper analyzes the benefits of dynamic line rating (DLR) in the system with high penetration of wind generation. A probabilistic forecasting model for the line ratings is incorporated into a two-stage stochastic optimization model. The scheduling model, for the first time, considers the uncertainty associated with wind generation, line ratings, and line outages to co-optimize the energy production and reserve holding levels in the scheduling stage as well as the redispatch actions in the real-time operation stage. Therefore, the benefits of higher utilization of line capacity can be explicitly balanced against the costs of increased holding and utilization of reserve services due to the forecasting error. The computational burden driven by the modeling of multiple sources of uncertainty is tackled by applying an efficient filtering approach. The case studies demonstrate the benefits of the DLR in supporting cost-effective integration of high penetration of wind generation into the existing network. We also highlight the importance of simultaneously considering the multiple sources of uncertainty in understanding the benefits of DLR. Furthermore, this paper analyzes the impact of different operational strategies, the coordination among multiple flexible technologies, and the installed capacity of wind generation on the benefits of DLR.

This paper presents a new forecast approach for ancillary services delivered by aggregated renewable power plants. The increasing penetration of distributed weather-dependent renewable generation challenges grid operators. Wind and photovoltaic power plants are technically able to provide ancillary services, but their stochastic behavior hinders currently their integration into reserve mechanisms. A methodology is developed to forecast the flexibility that a wind and photovoltaics based Virtual Power Plant can provide. A Quantile Regression Forest model is used to forecast the aggregated production. We propose a methodology to derive an optimal quantile of the combined production forecasts that can be used as basis to provide a reliable ancillary service to the System Operator. A copula-based approach integrates the dependence between prices and renewable production. The proposed methodology is evaluated using historical prices for energy and automatic Frequency Restoration Reserve along with measured production from several renewable power plants.