arXiv subjects

Hrvoje Pandžić

Publications and source records attributed to Hrvoje Pandžić.

5 recordsLinked to original sources

arXiv2024

A New Definition of Demand Response in the Distributed Energy Resource Era

Demand response is a concept that has been around since the very first electric power systems. However, we have seen an explosion of research on demand response and demand-side technologies in the past 30 years, coinciding with the shift towards liberalized/deregulated electricity markets and efforts to decarbonize the power sector. Now we are also seeing a shift towards more distributed/decentralized electric systems; we have entered the era of "distributed energy resources," which require new grid management, operational, and control strategies. Given this paradigm shift, we argue that the concept of demand response needs to be revisited, and more carefully/consistently defined to enable us to better utilize this massive resource for economic, technical, environmental, and societal aims. In this paper, we survey existing demand response definitions, highlight their shortcomings, propose a new definition, and describe how this new definition enables us to more effectively harness the value of demand response in modern power systems. We conclude with a demand response research agenda informed by a discussion of demand response barriers and enablers.

eess.SY↗

arXiv2024

Learning a Reward Function for User-Preferred Appliance Scheduling

Accelerated development of demand response service provision by the residential sector is crucial for reducing carbon-emissions in the power sector. Along with the infrastructure advancement, encouraging the end users to participate is crucial. End users highly value their privacy and control, and want to be included in the service design and decision-making process when creating the daily appliance operation schedules. Furthermore, unless they are financially or environmentally motivated, they are generally not prepared to sacrifice their comfort to help balance the power system. In this paper, we present an inverse-reinforcement-learning-based model that helps create the end users' daily appliance schedules without asking them to explicitly state their needs and wishes. By using their past consumption data, the end consumers will implicitly participate in the creation of those decisions and will thus be motivated to continue participating in the provision of demand response services.

cs.AI↗

arXiv2022

Solving Bilevel Optimal Bidding Problems Using Deep Convolutional Neural Networks

Current state-of-the-art solution techniques for solving bilevel optimization problems either assume strong problem regularity criteria or are computationally intractable. In this paper we address power system problems of bilevel structure, commonly arising after the deregulation of the power industry. Such problems are predominantly solved by converting the lower-level problem into a set of equivalent constraints using the Karush-Kuhn-Tucker optimality conditions at an expense of binary variables. Furthermore, in case the lower-level problem is nonconvex, the strong duality does not hold rendering the single-level reduction techniques inapplicable. To overcome this, we propose an effective numerical scheme based on bypassing the lower level completely using an approximation function that replicates the relevant lower level effect on the upper level. The approximation function is constructed by training a deep convolutional neural network. The numerical procedure is run iteratively to enhance the accuracy. As a case study, the proposed method is applied to a price-maker energy storage optimal bidding problem that considers an AC power flow-based market clearing in the lower level. The results indicate that greater actual profits are achieved as compared to the less accurate DC market representation.

eess.SY↗

arXiv2022

Solving Bilevel AC OPF Problems by Smoothing the Complementary Conditions -- Part I: Model Description and the Algorithm

The existing research on market price-affecting agents, i.e. price makers, neglects or simplifies the nature of AC power flows in the power system as it predominantly relies on DC power flows. This paper proposes a novel bilevel formulation based on the smoothing technique, where any price-affecting strategic player can be modelled in the upper level, while the market clearing problem in the lower level uses convex quadratic transmission AC optimal power flow (AC OPF), with the goal of achieving accuracy close to the one of the exact nonlinear formulations. Achieving convexity in the lower level is the foundation for bilevel modeling since traditional single-level reduction techniques do not hold for nonconvex models. The bilevel market participation problem with the AC OPF formulation in the lower level is transformed into a single-level problem and solved using multiple techniques such as the primal-dual counterpart, the strong duality theorem, the McCormick envelopes, the complementary slackness, the penalty factor, the interaction discretization as well as the proposed smoothing techniques. Due to an extensive amount of information and descriptions, the overall work is presented as a two-part paper. This first part provides a literature overview, positions the work and presents the model and the solution algorithm, while the solution techniques and case studies are provided in the accompanying paper.

eess.SY↗

arXiv2020

Electric Vehicle Aggregator as an Automatic Reserves Provider in the European Market Setting

Shift of the power system generation from the fossil to the variable renewable sources prompted the system operators to search for new sources of flexibility, that is, new reserve providers. With the introduction of electric vehicles, smart charging emerged as one of the relevant solutions. However, electric vehicle aggregators face the uncertainty of reserve activation on one side and electric vehicle availability on the other. These uncertainty can have a negative effect on both the aggregators' profitability and their users' comfort.State-of-the art literature mostly neglects the reserve activation or the related uncertainty. Also, they rarely model European markets or use real balancing data. This paper introduces a new method for modeling the reserve activation uncertainty based on actual data for the European power system. Three electric vehicle scheduling models were designed and tested: the deterministic, the stochastic and the robust one. The results demonstrate that the current deterministic approaches inaccurately represent the activation uncertainty and that the proposed models that consider uncertainty, both the stochastic and the robust one, substantially improve the results.

eess.SY↗