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Javad Mohammadi

Publications and source records attributed to Javad Mohammadi.

At least 37 records · Page 2Linked to original sources
arXiv2023

Equitable Coordination in Multi-agent Power Systems: Impacts of Computation Granularity

The growing integration of distributed energy resources drives the centralized power system towards a decentralized multi-agent network. Operating multi-agent networks significantly relies on inter-agent communications. Computation granularity in this context refers to the number of nodes overseen by an agent. The impact of granularity on equitable power coordination, particularly among marginalized customers with limited communication bandwidth (e.g., intermittent internet connectivity) is not well studied. This work explores different levels of computation granularity for agent-based energy dispatch and studies their impact on equitable coordination. We will leverage and utilize the Consensus + Innovations approach to model the equitable coordination of a multi-agent power system.

eess.SY↗
arXiv2023

Toward Rapid, Optimal, and Feasible Power Dispatch through Generalized Neural Mapping

The evolution towards a more distributed and interconnected grid necessitates large-scale decision-making within strict temporal constraints. Machine learning (ML) paradigms have demonstrated significant potential in improving the efficacy of optimization processes. However, the feasibility of solutions derived from ML models continues to pose challenges. It's imperative that ML models produce solutions that are attainable and realistic within the given system constraints of power systems. To address the feasibility issue and expedite the solution search process, we proposed LOOP-LC 2.0(Learning to Optimize the Optimization Process with Linear Constraints version 2.0) as a learning-based approach for solving the power dispatch problem. A notable advantage of the LOOP-LC 2.0 framework is its ability to ensure near-optimality and strict feasibility of solutions without depending on computationally intensive post-processing procedures, thus eliminating the need for iterative processes. At the heart of the LOOP-LC 2.0 model lies the newly proposed generalized gauge map method, capable of mapping any infeasible solution to a feasible point within the linearly-constrained domain. The proposed generalized gauge map method improves the traditional gauge map by exhibiting reduced sensitivity to input variances while increasing search speeds significantly. Utilizing the IEEE-200 test case as a benchmark, we demonstrate the effectiveness of the LOOP-LC 2.0 methodology, confirming its superior performance in terms of training speed, computational time, optimality, and solution feasibility compared to existing methodologies.

eess.SY↗
arXiv2023

Electric Vehicle Aggregation Review: Benefits and Vulnerabilities of Managing a Growing EV Fleet

Electric vehicles (EVs) are becoming more popular within the United States, making up an increasingly large portion of the US's electricity consumption. Hence, there is much attention has been directed on how to manage EVs within the power sector. A well-investigated strategy for managing the increase in electricity demand from EV charging is aggregation, which allows for an intermediary to manage electricity flow between EV owners and their utilities. When implemented effectively, EV aggregation provides key benefits to power grids by relieving electrical loads.. These benefits are aggregation's ability to shift EV loads to peak shave, which often leads to lower emissions, electricity generation prices, and consumer costs depending on the penetration levels of non-dispatchable electricity sources. This review seeks to appropriately highlight the broad vulnerabilities of EV aggregation alongside its benefits, namely those regarding battery degradation, rebound peaks, and cybersecurity. The holistic overview of EV aggregation provides comparisons that balance expectations with realistic performance.

eess.SY↗
arXiv2022

EVs and ERCOT: Future Adoption Scenarios and Grid Implications

Electric vehicles (EVs) are becoming more commonplace in Texas, mainly due to their increasing attractiveness to consumers and pushes from the state's governing bodies to incentivize further adoption. Meanwhile, service from Texas's electric grid, ERCOT, has been seeing increases in power demand due to a growing population, increased air conditioning use, and pushes for electrification across other industries. The electrification of vehicles will only add to this demand increase. This paper focuses on evaluating different EV adoption, charging management, and policy scenarios, and how they will be expected to impact ERCOT, particularly with respect to peak demand increases. A strong increase in the peak demand can lead to challenges to keep the electrical grid's reliability, making it an important consideration for electrification in any sector. The anticipated impacts of EV adoption on peak demand are quantified using ERCOT's data on past generation and planned installations, the approximated effectiveness of EV incentives, EV charging profiles, and travel patterns. The results showcase the fact that the achievement of ambitious EV market share goals will be manageable on a statewide level regarding electricity supply into 2030, but will eventually necessitate ambitious charging management strategies in order to limit the EV fleet's potentially heavy impact on peak demand looking forward into 2050 and beyond.

eess.SY↗
arXiv2022

Learning to Solve Optimization Problems with Hard Linear Constraints

Constrained optimization problems appear in a wide variety of challenging real-world problems, where constraints often capture the physics of the underlying system. Classic methods for solving these problems rely on iterative algorithms that explore the feasible domain in the search for the best solution. These iterative methods are often the computational bottleneck in decision-making and adversely impact time-sensitive applications. Recently, neural approximators have shown promise as a replacement for the iterative solvers that can output the optimal solution in a single feed-forward providing rapid solutions to optimization problems. However, enforcing constraints through neural networks remains an open challenge. This paper develops a neural approximator that maps the inputs to an optimization problem with hard linear constraints to a feasible solution that is nearly optimal. Our proposed approach consists of four main steps: 1) reducing the original problem to optimization on a set of independent variables, 2) finding a gauge function that maps the infty-norm unit ball to the feasible set of the reduced problem, 3)learning a neural approximator that maps the optimization's inputs to an optimal point in the infty-norm unit ball, and 4) find the values of the dependent variables from the independent variable and recover the solution to the original problem. We can guarantee hard feasibility through this sequence of steps. Unlike the current learning-assisted solutions, our method is free of parameter-tuning and removes iterations altogether. We demonstrate the performance of our proposed method in quadratic programming in the context of the optimal power dispatch (critical to the resiliency of our electric grid) and a constrained non-convex optimization in the context of image registration problems.

eess.SY↗
arXiv2022

Mapping Disruption Sources in the Power Grid and Implications for Resilience

Developing models and metrics that can address resilience against disruptions is vital to ensure power grid reliability and that adequate recovery and adaptation mechanisms are in place. In this paper, we propose a novel disruption mapping approach and apply it to the publicly available U.S. Department of Energy DOE-417 Electric Emergency and Disturbance Report to holistically analyze the origin of anomalous events and their propagation through the cyber, physical and human domains. We show that capturing the disruption process onset has implications for quantifying, mitigating, and reporting power grid resilience.

eess.SY↗
arXiv2022

Teaching Networks to Solve Optimization Problems

Leveraging machine learning to facilitate the optimization process is an emerging field that holds the promise to bypass the fundamental computational bottleneck caused by classic iterative solvers in critical applications requiring near-real-time optimization. The majority of existing approaches focus on learning data-driven optimizers that lead to fewer iterations in solving an optimization. In this paper, we take a different approach and propose to replace the iterative solvers altogether with a trainable parametric set function, that outputs the optimal arguments/parameters of an optimization problem in a single feed forward. We denote our method as Learning to Optimize the Optimization Process (LOOP). We show the feasibility of learning such parametric (set) functions to solve various classic optimization problems including linear/nonlinear regression, principal component analysis, transport-based coreset, and quadratic programming in supply management applications. In addition, we propose two alternative approaches for learning such parametric functions, with and without a solver in the LOOP. Finally, through various numerical experiments, we show that the trained solvers could be orders of magnitude faster than the classic iterative solvers while providing near optimal solutions.

cs.LG↗
arXiv2022

Numerical Comparisons of Linear Power Flow Approximations: Optimality, Feasibility, and Computation Time

Linear approximations of the AC power flow equations are of great significance for the computational efficiency of large-scale optimal power flow (OPF) problems. Put differently, the feasibility of the obtained solution is essential for practical use cases of OPF. However, most studies focus on approximation error and come short of comprehensively studying the AC feasibility of different linear approximations of power flow. This paper discusses the merits of widely-used linear approximations of active power in OPF problems. The advantages and disadvantages of the linearized models are discussed with respect to four criteria; accuracy of the linear approximation, optimality, feasibility, and computation time. Each method is tested on five different systems.

eess.SY↗
arXiv2022

A Fully Decentralized Tuning-free Inexact Projection Method for P2P Energy Trading

Agent-based solutions lend themselves well to address privacy concerns and the computational scalability needs of future distributed electric grids and end-use energy exchanges. Decentralized decision-making methods are the key to enabling peer-to-peer energy trading between electricity prosumers. However, the performance of existing decentralized decision-making algorithms highly depends on the algorithmic design and hyperparameter tunings, limiting applicability. This paper aims to address this gap by proposing a decentralized inexact projection method that does not rely on parameter tuning or central coordination to achieve the optimal solution for Peer-to-Peer (P2P) energy trading problems. The proposed algorithm does not require parameter readjustments, and once tuned, it converges for a wide range of P2P setups. Moreover, each prosumer only needs to share limited information (i.e., updated coupled variable) with neighboring prosumers. The IEEE 13 bus test system is used to showcase our proposed method's robustness and privacy advantages.

eess.SY↗
arXiv2020

Sharing Mobile and Stationary Energy Storage Resources in Transactive Energy Communities

Most power systems are increasingly based on distributed energy resources, leading to a strong impact on the electrical grid management. In the case of Portugal, the contribution of renewable energy sources to the electricity generation portfolio is already high and the objective is to achieve 100% by 2050. Most of the new renewable generation capacity will be ensured by distributed photovoltaic generation installed in buildings and therefore the inherent intermittence of photovoltaic output combined with a mismatch with demand profile will challenge the operation and resiliency of the electrical grid. Addressing these issues requires management at the community level and the leveraging of spatio-temporal flexibility of controllable energy resources, such as energy storage resources. This is recognized by the regulators in Portugal and the recent renewable generation self-consumption legislation enables generation-surplus trading in communities. Implementing intra-community trading and utilizing the potentials of renewable generation requires oversight and coordination at the community level in the context of transactive energy systems. This paper focuses on addressing energy sharing through a transactive energy market in community microgrids, using stationary and mobile energy storage as flexibility resources. The proposed framework considers public and commercial buildings with on-site battery storage and numerous electric vehicle charging stations. The formulation is assessed using real data from a community of buildings on a Portuguese University campus. The results showcase that the proposed method achieves an increase in renewable self-consumption at building and community levels, as well as a reduction in electricity costs.

eess.SY↗
arXiv2020

Connecting Distributed Pockets of EnergyFlexibility through Federated Computations:Limitations and Possibilities

Electric grids are traditionally operated as multi-entity systems with each entity managing a geographical region. Interest and demand for decarbonization and energy democratization is resulting in growing penetration of controllable energy resources. In turn, this process is increasing the number of grid entities. The paradigm shift is also fueled by increased adoption of intelligent sensors and actuators equipped with advanced processing and computing capabilities. While collaboration among power grid entities (agents) reduces energy cost and increases overall reliability, achieving effective collaboration is challenging. The main challenges stem from the heterogeneity of system agents and their collected information. Furthermore, the scale of data collection is constantly increasing and many grid entities have strict privacy requirements. Another challenge is the energy industry's common practice of keeping data in silos. Federated computation is an approach well suited to addressing these issues that are increasingly important for multi-agent energy systems. Through federated computation, agents collaboratively solve learning and optimization problems while respecting each agent's privacy and overcoming barriers of cross-device and cross-organization data isolation. In this paper, we first establish the need for federated computations to achieve energy optimization goals of the future power grid. We discuss practical challenges of performing multi-agent data processing in general. Then we address challenges that arise specifically for orchestrating operation of connected distributed energy resources in the Internet of Things. We conclude this paper by presenting a novel federated computation framework that addresses some of these issues, and we share examples of two initial field test setups in research demonstrations and commercial building applications with Grid Fruit LLC.

cs.DC↗
arXiv2020

Power System Dispatch with Marginal Degradation Cost of Battery Storage

Battery storage is essential for the future smart grid. The inevitable cell degradation renders the battery lifetime volatile and highly dependent on battery dispatch, and thus incurs opportunity cost. This paper rigorously derives the marginal degradation cost of battery for power system dispatch. The derived optimal marginal degradation cost is time-variant to reflect the time value of money and the functionality fade of battery and takes the form of a constant value divided by a discount factor plus a term related to battery state of health. In case studies, we demonstrate the evolution of the optimal marginal costs of degradation that corresponds to the optimal long-term dispatch outcome. We also show that the optimal marginal cost of degradation depends on the marginal cost of generation in the grid.

math.OC↗
arXiv2020

Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Time

The Optimal power flow (OPF) problem contains many constraints. However, equality constraints along with a limited set of active inequality constraints encompass sufficient information to determine the feasible space of the problem. In this paper, a hybrid supervised regression and classification learning based algorithm is proposed to identify active and inactive sets of inequality constraints of AC OPF solely based on nodal power demand information. The proposed algorithm is structured using several classifiers and regression learners. The combination of classifiers with regression learners enhances the accuracy of active / inactive constraints identification procedure. The proposed algorithm modifies the OPF feasible space rather than a direct mapping of OPF results from demand. Inactive constraints are removed from the design space to construct a truncated AC OPF. This truncated optimization problem can be solved faster than the original problem with less computational resources. Numerical results on several test systems show the effectiveness of the proposed algorithm for predicting active and inactive constraints and constructing a truncated AC OPF. We have posted our code for all simulations on arxiv and have uploaded the data used in numerical studies to IEEE DataPort as an open access dataset.

eess.SY↗
arXiv2020

Computationally Efficient Solutions for Large-Scale Security-Constrained Optimal Power Flow

In this paper, we discuss our approach and algorithmic framework for solving large-scale security constrained optimal power flow (SCOPF) problems. SCOPF is a mixed integer non-convex optimization problem that aims to obtain the minimum dispatch cost while maintaining the system N-1 secure. Finding a feasible solution for this problem over large networks is challenging and this paper presents contingency selection, approximation methods, and decomposition techniques to address this challenge in a short period of time. The performance of the proposed methods are verified through large-scale synthetic and actual power networks in the Grid Optimization (GO) competition organized by the U.S. Advanced Research Projects Agency-Energy (ARPA-E). As many prior works focus on small-scale systems and are not benchmarked using validated, publicly available datasets, we aim to present a practical solution to SCOPF that has been proven to achieve good performance on realistically sized (30,000 buses) networks.

math.OC↗
arXiv2020

Management of Electric Vehicles as Flexibility Resource for Optimized Integration of Renewable Energy with Large Buildings

The increasing penetration of renewable generation requires new tools to achieve a high matching between demand and renewable generation at building and community levels. Large commercial and public buildings with parking lots have a considerable potential to provide energy flexibility by controlling the charging of Electric Vehicles (EVs) and injecting part of the stored energy into the building, using Building-to-Vehicle (B2V) and Vehicle-to-Building (V2B) systems. However, EVs and buildings do not often belong to the same entity and in Portugal the existing regulation does not allow financial transactions between buildings and EVs as separate entities. Addressing this regulation hurdle requires innovative optimization methods for the implementation of B2V/V2B systems. Moreover, the new legislation regarding the self-consumption of renewable generation in Portugal enables the trade of renewable generation surplus between buildings and the establishment of renewable energy communities. This paper intends to address this issue and proposes a formulation to aggregate and manage the sharing of generation surplus between buildings, using EVs as a flexibility resource. The simulation results showcase the achieved increase in renewable self-consumption at building and community levels, as well as the reduction in electricity costs.

eess.SY↗
arXiv2020

CarnegiePLUG: Prosumer-in-the-Loop simUlation Grid

This paper introduces Carnegie Mellon campus-wide CarnegiePLUG test-bed. This test-bed in a hardware-in-the-loop simulator that enables large-scale sensing, computation and actuation over a network of heterogeneous energy producers and consumers (prosuming) assets. CarnegiePLUG is a multi-agent framework that facilitates plug-and-play integration of a wide range of virtual and physical sensors and controllers through a multi-layer architecture. Virtual functionalities are accessed in software environments that interface with communication layer of our test-bed. This paper discusses CarnegiePLUG layers, provides implementation details and showcases potential applications of this test-bed through preliminary results.

cs.DC↗
arXiv2020

Linking Parking and Electricity Values to Unlock Potentials of Electric Vehicles in Portuguese Buildings

Large parking lots in public and commercial buildings are increasingly installing the required infrastructure for serving Electric Vehicles (EVs). Utilizing charging and discharging flexibility of parked EVs has the potential to significantly increase the self-consumption of on-site renewable generation and reduce building energy costs. Optimal charging and discharge management of electric vehicles can fill the gap between self-generation and building electric demand while accounting for electricity tariffs. Optimal interactions between EVs and buildings will play a key role in the operation of power networks with a high penetration of distributed energy resources, such as the Portuguese electric grid. However, in Portugal, the existing regulation does not allow financial transactions between buildings and EVs in exchange for charging and discharging. This renders most proposed charging and discharging management strategies impractical. This paper introduces a novel and practical framework to connect electricity and parking values at commercial and public buildings. This framework will manage interactions between building and vehicle in the context of parking time duration and added value services for the charging and discharging periods. The proposed formulation is ready to adopt since it is compatible with the current regulations and relies on existing technologies. The simulation results showcase the cost reduction and self-consumption benefits of the proposed solution for building owners.

physics.soc-ph↗