arXiv subjects

David Pozo

Publications and source records attributed to David Pozo.

At least 19 recordsLinked to original sources

arXiv2023

Assessing the value of energy storage systems for distribution grid applications

We analyze the potential benefits that energy storage systems (ESS) can bring to distribution networks in terms of cost, stability and flexibility. We propose an optimization model for the optimal sizing, siting, and operation of storage systems in distribution grids. A DistFlow formulation is used for modeling the AC power flow. The ESS model is based on a generic formulation that captures the charging and discharging modes' complementarity. The resulting optimization model is stated as a mixed-integer quadratically constrained program (MIQCP) problem. The optimization model is assessed on the modified 33-bus IEEE network, which includes renewable energy resources and ESS. The obtained results show that ESS can offer various important benefits such as overall cost reduction, energy arbitrage, voltage regulation, and congestion management in distribution grids. These findings highlight the significance of utilizing ESS technologies to provide aggregated value through various grid services, extending beyond energy arbitrage alone.

eess.SY↗

arXiv2022

Construction of Multi-period TSO-DSO Flexibility Regions

Active distribution networks (ADN) have grown considerably in recent years. Distributed energy resources present in ADNs can provide flexibility to the power system through TSO/DSO coordination, i.e., at the interface node (feeder) between the transmission and distribution network. This paper addresses the issue of calculating multi-period flexibility regions of the ADNs. Flexibility regions are tightly dependent between periods and conditioned on the actual deployment of such flexibilities in real-time. The existing state-of-the-art has not provided a robust methodology for building multi-period flexible regions. We present a new mathematical framework based on a non-iterative formulation that considers the multi-period flexibility boundary points in a single optimization problem. The proposed methodology is evaluated on IEEE standard test networks and compared with the most widely used methods in the literature.

eess.SY↗

arXiv2022

A review on recent advances in scenario aggregation methods for power system analysis

Worldwide commitments to net zero greenhouse emissions have accelerated investments in renewable energy resources. The requirements for operating and planning power systems are becoming stringent because of the need to take into account the uncertainty associated with renewable generation. Several modeling frameworks that consider the inherent uncertainty in the operation and planning of the power system have been extensively studied. Stochastic optimization has been the most popular approach among these frameworks due to its intuitive representation, especially when formulated using discrete probabilistic scenarios to represent the random variables. Although many scenarios representing all possible uncertain operating conditions would be needed to accurate evaluate stochastic operation and planning models, the size of the scenario set impacts computational complexity, posing a significant tradeoff between uncertainty detail representation and computational tractability. During the last decade, a large body of research has focused on developing new scenario aggregation methods to derive reduced scenario sets that show properties similar to the original scenario set while decreasing computational burden. This review provides an up-to-date, comprehensive classification and analysis of the literature related to scenario aggregation methods for addressing power system optimization problems. First, we present a general framework and the aggregation methodologies. Then, the main studies related to temporal and spatial scenario aggregation are described, followed by a bibliometric analysis of the main publication sources, authors, and application problems. Finally, we provide a numerical analysis and discuss 16 aggregation methods for the transmission expansion planning problem. Finally, recommendations, opportunities, and conclusions are discussed.

math.OC↗

arXiv2022

Linear Battery Models for Power Systems Analysis

Mathematical models are just models. The desire to describe battery energy storage system (BESS) operation using computationally tractable model formulations has motivated a long-standing discussion in both the scientific and industrial communities. Linear BESS models are the most widely used so far. However, finding suitable linear BESS models has been controversial. This paper focuses on the description of linear BESS models. Four linear BESS formulations are presented, among the most popularly used. A new formulation is also proposed. The 5 BESS models are tested in 100 random BESS and 1.450 random samples of daily profiles of renewable generation. Two classical problems of power systems, namely, the set-point tracking problem and the transmission expansion planning problem, are selected for numerical analysis. Five thousand simulations are used to draw a better interpretation of each linear formulation presented and showcase specific challenges of BESS models. Practical recommendations are provided based on the findings.

eess.SY↗

arXiv2021

Sub-millisecond Video Synchronization of Multiple Android Smartphones

This paper addresses the problem of building an affordable easy-to-setup synchronized multi-view camera system, which is in demand for many Computer Vision and Robotics applications in high-dynamic environments. In our work, we propose a solution for this problem -- a publicly-available Android application for synchronized video recording on multiple smartphones with sub-millisecond accuracy. We present a generalized mathematical model of timestamping for Android smartphones and prove its applicability on 47 different physical devices. Also, we estimate the time drift parameter for those smartphones, which is less than 1.2 msec per minute for most of the considered devices, that makes smartphones' camera system a worthy analog for professional multi-view systems. Finally, we demonstrate Android-app performance on the camera system built from Android smartphones quantitatively on setup with lights and qualitatively -- on panorama stitching task.

cs.CV↗

arXiv2021

QuickFlex: a Fast Algorithm for Flexible Region Construction for the TSO-DSO Coordination

Most of the new technological changes in power systems are expected to take place in distribution grids. The enormous potential for distribution flexibility could meet the transmission system's needs, changing the paradigm of generator-centric energy and ancillary services provided to a demand-centric one, by placing more importance on smaller resources, such as flexible demands and electric vehicles. For unlocking such capabilities, it is essential to understand the aggregated flexibility that can be harvested from the large population of new technologies located in distribution grids. Distribution grids, therefore, could provide aggregated flexibility at the transmission level. To date, most computational methods for estimating the aggregated flexibility at the interface between distribution grids and transmission grids have the drawback of requiring significant computational time, which hinders their applicability. This paper presents a new algorithm, coined as QuickFlex} for constructing the flexibility domain of distribution grids. Contrary to previous methods, a priory flexibility domain accuracy can be selected. Our method requires few iterations for constructing the flexibility region. The number of iterations needed is mainly independent of the distribution grid's input size and flexible elements. Numerical experiments are performed in four grids ranging from 5 nodes to 123 nodes. It is shown that QuickFlex outperforms existing proposals in the literature in both speed and accuracy.

eess.SY↗

arXiv2021

Optimal Transmission Switching: Improving Solver Performance Using Heuristics

The optimal transmission switching problem (OTSP) is an established problem of changing a power grid's topology to obtain an improved operation by controlling the switching status of transmission lines. This problem was proven to be NP-hard. Proposed solution techniques based on mixed-integer formulations can guarantee globally optimal solutions but are potentially intractable in realistic power grids. Heuristics methods cannot guarantee global optimality but can provide tractable solution approaches. This paper proposes solving the OTSP using exact formulations alongside parallel heuristics that generate good candidate solutions to speed up conventional branch-and-bound algorithms. The innovative aspect of this work is a new asynchronous parallel algorithmic architecture. A solver instance solving the full OTSP formulation is run in parallel to another process that asynchronously generates solutions to be injected into the full OTSP solution procedure during run time. Our method is tested on 14 instances of the pglib-opf library: The largest problem consisting of 13659 buses and 20467 branches. Our results show a good performance for large problem instances, with consistent improvements over off-the-shelf solver performance. We find that the method scales well with an increase in parallel processors.

eess.SY↗

arXiv2021

Electricity and Reserve Pricing in Chance-Constrained Electricity Markets with Asymmetric Balancing Reserve Policies

Recently, chance-constrained stochastic electricity market designs have been proposed to address the shortcomings of scenario-based stochastic market designs. In particular, the use of chance-constrained market-clearing avoids trading off in-expectation and per-scenario characteristics and yields unique energy and reserves prices. However, current formulations rely on symmetric control policies based on the aggregated system imbalance, which restricts balancing reserve providers in their energy and reserve commitments. This paper extends existing chance-constrained market-clearing formulations by leveraging node-to-node and asymmetric balancing reserve policies and deriving the resulting energy and reserve prices. The proposed node-to-node policy allows for relating the remuneration of balancing reserve providers and payment of uncertain resources using a marginal cost-based approach. Further, we introduce asymmetric balancing reserve policies into the chance-constrained electricity market design and show how this additional degree of freedom affects market outcomes.

math.OC↗

arXiv2021

Impact of Electric Vehicle Routing with Stochastic Demand on Grid Operation

Given the rise of electric vehicle (EV) adoption, supported by government policies and dropping technology prices, new challenges arise in the modeling and operation of electric transportation. In this paper, we present a model for solving the EV routing problem while accounting for real-life stochastic demand behavior. We present a mathematical formulation that minimizes travel time and energy costs of an EV fleet. The EV is represented by a battery energy consumption model. To adapt our formulation to real-life scenarios, customer pick-ups and drop-offs were modeled as stochastic parameters. A chance-constrained optimization model is proposed for addressing pick-ups and drop-offs uncertainties. Computational validation of the model is provided based on representative transportation scenarios. Results obtained showed a quick convergence of our model with verifiable solutions. Finally, the impact of electric vehicles charging is validated in Downtown Manhattan, New York by assessing the effect on the distribution grid.

math.OC↗

arXiv2021

The Value of Operational Coordination for EV Fleet Aggregators

The integration of energy systems such as electricity and gas grids and power and thermal grids can bring significant benefits in terms of system security, reliability, and reduced emissions. Another alternative coupling of sectors with large potential benefits is the power and transportation networks. This is primarily due to the increasing use of electric vehicles (EV) and their demand on the power grid. Besides, the production and operating costs of EVs and battery technologies are steadily decreasing, while tax credits for EV purchase and usage are being offered to users in developed countries. The power grid is also undergoing major upgrades and changes with the aim of ensuring environmentally sustainable grids. These factors influence our work. We present a new operating model for an integrated EV-grid system that incorporates a set of aggregators (owning a fleet of EVs) with partial access to the distribution grid. Then, the Cooperative Game Theory is used to model the behavior of the system. The Core is used to describe the stability of the interaction between these aggregators, and the Shapley value is used to assign costs to them. The results obtained show the benefit of cooperation, which could lead to an overall reduction in energy consumption, reduced operating costs for electric vehicles and the distribution grid, and, in some cases, the additional monetary budget available to reinforce the transmission and grid infrastructures.

math.OC↗

arXiv2021

Priority Lists for Power System Investments: Locating Phasor Measurement Units

Power systems incrementally and continuously upgrade their components, such as transmission lines, reactive capacitors, or generating units. Decision-making tools often support the selection of the best set of components to upgrade. Optimization models are often used to support decision making at a given point in time. After certain time intervals, re-optimization is performed to find new components to add. In this paper, we propose a decision-making framework for incrementally updating power system components. This is an alternative approach to the classical sequential re-optimization decision making for an investment problem with modeled budget constraints. Our approach provides a priority list as a solution with a list of new components to upgrade. We show that i) our framework is consistent with the evolution of power system upgrades, and ii) in particular circumstances, both frameworks provide the same solution if the problem satisfies submodularity property. We have selected the problem of phasor measurement unit localization and compared the solution with the classical sequential re-optimization framework. For this particular problem, we show that the two approaches provide close results, while only our proposed algorithm is applicable in practice. The cases of 14 and 118 IEEE buses are used to illustrate the proposed methodology.

eess.SY↗

arXiv2021

Empirical Analysis of Capacity Investment Solution in Distribution Grids

This paper presents an analysis of the stability and quality of the distributed generation planning problem's investment solution. The entry of distributed generators power based on non-conventional energy sources has been extensively promoted in distribution grids. In this paper, a two-stage stochastic programming model is used to find the optimal distributed generators' installed capacities. We emphasize the design of scenarios to represent the stochasticity of power production on renewable sources. In the scenario generation, a method is proposed based on the clustering of real measurements of meteorological variables. We measure the quality and stability of the investment solution as a function of the number of scenarios. The results show that a reduced selection of scenarios can give an inadequate solution to distributed generators' investment strategy.

eess.SY↗

arXiv2020

Pricing in Integrated Heat and Power Markets

There is a growing interest in the integration of energy infrastructures to increase systems' flexibility and reduce operational costs. The most studied case is the synergy between electric and heating networks. Even though integrated heat and power markets can be described by a convex optimization problem, prices derived from dual values do not guarantee cost recovery. In this work, a two-step approach is presented for the calculation of the optimal energy dispatch and prices. The proposed methodology guarantees cost-recovery for each of the energy vectors and revenue-adequacy for the integrated market.

econ.GN↗

arXiv2020

Segmentation and Defect Classification of the Power Line Insulators: A Deep Learning-based Approach

Power transmission networks physically connect the power generators to the electric consumers. Such systems extend over hundreds of kilometers. There are many components in the transmission infrastructure that require a proper inspection to guarantee flawless performance and reliable delivery, which, if done manually, can be very costly and time consuming. One essential component is the insulator. Its failure can cause an interruption of the entire transmission line or a widespread power failure. Automated fault detection could significantly decrease inspection time and related costs. Recently, several works have been proposed based on convolutional neural networks, which address the issue mentioned above. However, existing studies focus on a specific type of insulator faults. Thus, in this study, we introduce a two-stage model that segments insulators from their background to then classify their states based on four different categories, namely: healthy, broken, burned/corroded and missing cap. The test results show that the proposed approach can realize the effective segmentation of insulators and achieve high accuracy in detecting several types of faults.

cs.CV↗

arXiv2020

Electric End-User Consumer Profit Maximization: An Online Approach

The fast growth of communication technology within the concept of smart grids can provide data and control signals from/to all consumers in an online fashion. This could foster more participation for end-user customers. These types of customers do not necessarily have powerful prediction tools or capability of storing a large amount of historical data. Besides, the relevant information is not always known a priori, while decisions need to be made fast within a very limited time. These limitations and also the novel structure of decision making, which comes from the necessities to make the decision very fast with a limited amount of information, implies a requirement for investigating a novel framework: online decision-making. In this study, we propose an online constrained convex optimization framework for operating responsive end-user electrical customers in real-time. Within this online-decision-making framework, algorithms are proposed for two cases: no prediction data is available at the moment of decision-making, and a limited number of forward time periods predictions of uncertain parameters are available. The simulation results exhibit the capability of the model to achieve considerable profits in an easy-to-implement procedure. Comprehensive numerical test cases are performed for comparison with existent alternative models.

eess.SY↗

arXiv2020

Coordination Between TSOs and DSOs: Flexibility Domain Identification

The enormous technological potential accumulated over the past two decades would make it possible to change the operating principles of power systems entirely. The consequent technological evolution is not only affecting the structure of the electricity markets, but also the interactions between Transmission System Operators (TSOs) and Distribution System Operators (DSOs). New practical solutions are needed to improve the coordination between the grid operators at the national, TSOs, and local level, DSOs. In this paper, we define the flexibility range of coordination between TSOs and DSOs. By doing so, we propose an algorithm based on epsilon-constrained methods by means of mathematical programming and power systems principles. We evaluate and compare different classical optimal power flow formulations (AC-OPF, DistFlow, DistFlow-SOCP, and LinDistFlow) for building the flexible TSO-DSO flexible domain. The presented approaches in this paper are analyzed in an IEEE 33-bus test radial distribution system. We show that for this particular problem, the DistFlow-SOCP has the worst accuracy, despite the popularity among the academic community of convex relaxation approaches.

eess.SY↗

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

Non-ideal Linear Operation Model for a Li-ion Battery

Currently, the characterization of electric energy storage units used for power system operation and planning models relies on two major assumptions: charge and discharge efficiencies, and power limits are constant and independent of the electric energy storage state of charge. This approach can misestimate the available storage flexibility. This work proposes a detailed model for the characterization of steady-state operation of Li-ion batteries in optimization problems. The model characterizes the battery performance, including non-linear charge and discharge power limits and efficiencies, as a function of the state of charge and requested power. We then derive a linear reformulation of the model without introducing binary variables, which achieves high computational efficiency, while providing high approximation accuracy. The proposed model characterizes more accurately the performance and technical operational limits associated with Li-ion batteries than those present in classical ideal models. The developed battery model has been compared with three modelling approaches: the complete non-convex formulation; an ideal model typically used in the power system community; and a mixed integer linear reformulation approach. The models have been tested on a network-constrained economic dispatch for a 24-bus system. Based on the simulations, we observed approximately 12% of energy mismatches between schedules that use an ideal model and those that use the model proposed in this study.

eess.SY↗