arXiv subjects

David Pozo

Publications and source records attributed to David Pozo.

23 records · Page 2Linked to original sources

arXiv2019

Stochastic Time-of-use-type Constraints for Uninterruptible Services

In this paper, a mixed integer linear formulation for problems considering time-of-use-type constraints for uninterruptible services is presented. Our work is motivated by demand response problems in power systems, in which certain devices must remain online once they are switched on, along with a certain number of periods. Classically, this kind of constraints are modeled as a summation over a rolling time window. This makes it difficult to consider this time-of-use parameter as uncertain. We propose an alternative formulation in which the time of use is on the right-hand side of a constraint instead on the limit of a summation. This allows applying existing stochastic optimization methodologies easily. An illustrative model for the optimal allocation of an uninterruptible load for the demand response problem supports the proposed formulation.

math.OC↗

arXiv2019

Two-Stage Robust Unit Commitment for Co-Optimized Electricity Markets: An Adaptive Data-Driven Approach for Scenario-Based Uncertainty Sets

Two-stage robust unit commitment (RUC) models have been widely used for day-ahead energy and reserve scheduling under high renewable integration. The current state of the art relies on budget-constrained polyhedral uncertainty sets to control the conservativeness of the solutions. The associated lack of interpretability and parameter specification procedures, as well as the high computational burden exhibited by available exact solution techniques call for new approaches. In this work, we use an alternative scenario-based framework whereby uncertain renewable generation is characterized by a polyhedral uncertainty set relying on the direct specification of its vertexes. Moreover, we present a simple, yet efficient, adaptive data-driven procedure to dynamically update the uncertainty set vertexes with observed daily renewable-output profiles. Within this setting, the proposed data-driven RUC ensures protection against the convex hull of realistic scenarios empirically capturing the complex and time-varying intra-day spatial and temporal interdependences among renewable units. The resulting counterpart features advantageous properties from a computational perspective and can be effectively solved by the column-and-constraint generation algorithm until $ε$-global optimality. Out-of-sample experiments reveal that the proposed approach is capable of attaining efficient solutions in terms of cost and robustness while keeping the model tractable and scalable.

math.OC↗

arXiv2018

Economic Impact of Wind Generation Penetration in the Colombian Electricity Market

The creation of the Renewable Energy Law (Law 1715 of 2014) promotes the introduction of large-scale renewable energy generation in the Colombian electricity market. The new legislation aims to diversify the country's generation matrix, mainly composed of hydro and fuel-based generation, with a share of 66% and 34% respectively. Currently, three wind generation projects, with an aggregated capacity of 500 MW, have been commissioned in the North of the country. This study analyses the economic impact of the large-scale introduction of wind generation on both, the market spot price and conventional generation plants operation. For this purpose, the study builds a unit commitment model to mimic the current market legislation and the system's generation data. We show that the introduction of wind energy into the Colombian electricity market would impact the generation share of large hydro and gas-fired power plants. The hydro generation has an important role in balancing the generation for fluctuations on the wind resource. Meanwhile, the gas-fired plants would decrease their participation in the market, proportionally to the introduction of wind generation in the system, by as low as 20% of its current operation.

q-fin.GN↗

arXiv2018

A Guide to Solar Power Forecasting using ARMA Models

We describe a simple and succinct methodology to develop hourly auto-regressive moving average (ARMA) models to forecast power output from a photovoltaic solar generator. We illustrate how to build an ARMA model, to use statistical tests to validate it, and construct hourly samples. The resulting model inherits nice properties for embedding it into more sophisticated operation and planning models, while at the same time showing relatively good accuracy. Additionally, it represents a good forecasting tool for sample generation for stochastic energy optimization models.

stat.AP↗

arXiv2018

Cyberattack Detection in Intelligent Grids Using Non-linear Filtering

Electric power grids are evolving towards intellectualization such as Smart Grids or active-adaptive networks. Intelligent power network implies usage of sensors, smart meters, electronic devices and sophisticated communication network. This leads to a strong dependence on information and communication networking that are prone to threats of cyberattacks, which challenges power system reliability and efficiency. Thus, significant attention should be paid to the Smart Grids security. Recently, it has been proven that False Data Injection Attacks (FDIA) could corrupt results of State Estimation (SE) without noticing, therefore, leading to a possible mis-operation of the whole power system. In this paper, we introduce an algorithm for detecting cyberattacks based on non-linear filtering by using cyber-physical information from Kirchhoff laws. The proposed algorithm only needs data from adjacent nodes, therefore can be locally and distributed implemented. Also, it requires very low computational effort so that it can be run online, and it is suitable for implementation in existing or new ad-hoc low-cost devices. The proposed algorithm could be helpful to increase power system awareness against FDIA complementing the current SE implementations. The efficiency of the proposed algorithm has been proved by mathematical simulations and computer modeling in PSCAD software. Our results show that the proposed methodology can detect cyberattacks to the SE in 99.9% of the cases with very little false alarms on the identification of spoiled measurements (4.6%).

cs.CR↗