arXiv subjects

Wicak Ananduta

Publications and source records attributed to Wicak Ananduta.

13 recordsLinked to original sources

arXiv2025

Ensuring Grid-Safe Forwarding of Distributed Flexibility in Sequential DSO-TSO Markets

This paper investigates sequential flexibility markets consisting of a first market layer for distribution system operators (DSOs) to procure local flexibility to resolve their own needs (e.g., congestion management) followed by a second layer, in which the transmission system operator (TSO) procures remaining flexibility forwarded from the distribution system layer as well as flexibility from its own system for providing system services. As the TSO does not necessarily have full knowledge of the distribution grid constraints, this bid forwarding can cause an infeasibility problem for distribution systems, i.e., cleared distribution-level bids in the TSO layer might not satisfy local network constraints. To address this challenge, we introduce and examine three methods aiming to enable the grid-safe use of distribution-located resources in markets for system services, namely: a corrective three-layer market scheme, a bid prequalification/filtering method, and a bid aggregation method. Technically, we provide conditions under which these methods can produce a grid-safe use of distributed flexibility. We also characterize the efficiency of the market outcome under these methods. Finally, we carry out a representative case study to evaluate the performances of the three methods, focusing on economic efficiency, grid-safety, and computational load.

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arXiv2024

Operating envelopes for the grid-constrained use of distributed flexibility in balancing markets

The increasing share of distributed energy sources enhances the participation potential of distributed flexibility in the provision of system services. However, this participation can endanger the grid-safety of the distribution networks (DNs) from which this flexibility originates. In this paper, the use of operating envelopes (OE) to enable the grid-safe procurement of distributed flexibility in centralized balancing markets is proposed. Two classes of approaches for calculating OEs (one-step and two-step methods) are compared in terms of the level of distribution grid safety they can provide, the impact they can have on the market efficiency, and the volume of discarded flexibility they can yield. A case study considering different system scenarios, based on Monte Carlo simulations, highlights a trade-off between the market efficiency, DN flexibility resource utilization, and the grid safety delivered by the different OE methods. The results showcase that the use of the two-step OE approach results in a more grid-secure albeit less-efficient use of distributed flexibility.

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arXiv2023

A Semi-Decentralized Tikhonov-based Algorithm for Optimal Generalized Nash Equilibrium Selection

To optimally select a generalized Nash equilibrium, in this paper, we propose a semi-decentralized algorithm based on a double-layer Tikhonov regularization method. Technically, we extend the Tikhonov method for equilibrium selection in non-generalized games to the generalized case by coupling it with the preconditioned forward-backward splitting, which guarantees linear convergence to the solutions of the inner layer problem and allows for a semi-decentralized implementation. We then establish a conceptual connection and draw a comparison between the proposed algorithm and the hybrid steepest descent method, the other known distributed framework for solving the selection problem.

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arXiv2022

A two-stage approach for a mixed-integer economic dispatch game in integrated electrical and gas distribution systems

We formulate for the first time the economic dispatch problem in an integrated electrical and gas distribution system as a game equilibrium problem between distributed prosumers. Specifically, by approximating the non-linear gas-flow equations either with a mixed-integer second order cone or a piece-wise affine model and by assuming that electricity and gas prices depend linearly on the total consumption we obtain a potential mixed-integer game. To compute an approximate generalized Nash equilibrium, we propose an iterative two-stage method that exploits a problem convexification and the gas flow models. We quantify the quality of the computed solution and perform a numerical study to evaluate the performance of our method.

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arXiv2022

Approximate solutions to the optimal flow problem of multi-area integrated electrical and gas systems

We formulate the optimal flow problem in a multi-area integrated electrical and gas system as a mixed-integer optimization problem by approximating the non-linear gas flows with piece-wise affine functions, thus resulting in a set of mixed-integer linear constraints. For its solution, we propose a novel algorithm that consists in one stage for solving a convexified problem and a second stage for recovering a mixed-integer solution. The latter exploits the gas flow model and requires solving a linear program. We provide an optimality certificate for the computed solution and show the advantages of our algorithm compared with respect to the state-of-the-art method via numerical simulations.

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arXiv2022

Bregman algorithms for mixed-strategy generalized Nash equilibrium seeking in a class of mixed-integer games

We consider the problem of computing a mixed-strategy generalized Nash equilibrium (MS-GNE) for a class of games where each agent has both continuous and integer decision variables. Specifically, we propose a novel Bregman forward-reflected-backward splitting and design distributed algorithms that exploit the problem structure. Technically, we prove convergence to a variational MS-GNE under mere monotonicity and Lipschitz continuity assumptions, which are typical of continuous GNE problems. Finally, we show the performance of our algorithms via numerical experiments.

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arXiv2022

Operationally-Safe Peer-to-Peer Energy Trading in Distribution Grids: A Game-Theoretic Market-Clearing Mechanism

In future distribution grids, prosumers (i.e., energy consumers with storage and/or production capabilities) will trade energy with each other and with the main grid. To ensure an efficient and safe operation of energy trading, in this paper, we formulate a peer-to-peer energy market of prosumers as a generalized aggregative game, in which a network operator is only responsible for the operational constraints of the system. We design a distributed market-clearing mechanism with convergence guarantee to an economically-efficient and operationally-safe configuration (i.e., a variational generalized Nash equilibrium). Numerical studies on the IEEE 37-bus testcase show the scalability of the proposed approach and suggest that active participation in the market is beneficial for both prosumers and the network operator.

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arXiv2022

Optimal selection and tracking of generalized Nash equilibria in monotone games

A fundamental open problem in monotone game theory is the computation of a specific generalized Nash equilibrium (GNE) among all the available ones, e.g. the optimal equilibrium with respect to a system-level objective. The existing GNE seeking algorithms have in fact convergence guarantees toward an arbitrary, possibly inefficient, equilibrium. In this paper, we solve this open problem by leveraging results from fixed-point selection theory and in turn derive distributed algorithms for the computation of an optimal GNE in monotone games. We then extend the technical results to the time-varying setting and propose an algorithm that tracks the sequence of optimal equilibria up to an asymptotic error, whose bound depends on the local computational capabilities of the agents.

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arXiv2021

The distributed dual ascent algorithm is robust to asynchrony

The distributed dual ascent is an established algorithm to solve strongly convex multi-agent optimization problems with separable cost functions, in the presence of coupling constraints. In this paper, we study its asynchronous counterpart. Specifically, we assume that each agent only relies on the outdated information received from some neighbors. Differently from the existing randomized and dual block-coordinate schemes, we show convergence under heterogeneous delays, communication and update frequencies. Consequently, our asynchronous dual ascent algorithm can be implemented without requiring any coordination between the agents.

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arXiv2021

Distributed Augmented Lagrangian Method for Link-Based Resource Sharing Problems of Multi-Agent Systems

A multi-agent optimization problem motivated by the management of energy systems is discussed. The associated cost function is separable and convex although not necessarily strongly convex and there exist edge-based coupling equality constraints. In this regard, we propose a distributed algorithm based on solving the dual of the augmented problem. Furthermore, we consider that the communication network might be time-varying and the algorithm might be carried out asynchronously. The time-varying nature and the asynchronicity are modeled as random processes. Then, we show the convergence and the convergence rate of the proposed algorithm under the aforementioned conditions.

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arXiv2020

Accelerated Multi-Agent Optimization Method over Stochastic Networks

We propose a distributed method to solve a multi-agent optimization problem with strongly convex cost function and equality coupling constraints. The method is based on Nesterov's accelerated gradient approach and works over stochastically time-varying communication networks. We consider the standard assumptions of Nesterov's method and show that the sequence of the expected dual values converge toward the optimal value with the rate of $\mathcal{O}(1/k^2)$. Furthermore, we provide a simulation study of solving an optimal power flow problem with a well-known benchmark case.

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arXiv2020

Event-triggered Partitioning for Non-centralized Predictive-Control-based Economic Dispatch of Interconnected Microgrids: Technical Report

A non-centralized model predictive control (MPC) scheme for solving an economic dispatch problem of electrical networks is proposed in this paper. The scheme consists of two parts. The first part is an event-triggered repartitioning method that splits the network into a fixed number of non-overlapping sub-systems {(microgrids)}. The objective of the repartitioning procedure is to obtain self-sufficient microgrids, i.e., those that can meet their local loads using their own generation units. However, since the algorithm does not guarantee that all the resulting microgrids are self-sufficient, the microgrids that are not self-sufficient must then form a coalition with some of their neighboring microgrids. This process becomes the second part of the scheme. By performing the coalition formation, we can decompose the economic dispatch problem of the network into coalition-based sub-problems such that each subproblem is feasible. Furthermore, we also show that the solution obtained by solving the coalition-based sub-problems is a feasible but sub-optimal solution to the centralized problem. Additionally, some numerical simulations are also carried out to show the effectiveness of the proposed method.

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arXiv2018

Resilient Distributed Energy Management for Systems of Interconnected Microgrids

In this paper, distributed energy management of interconnected microgrids, which is stated as a dynamic economic dispatch problem, is studied. Since the distributed approach requires cooperation of all local controllers, when some of them do not comply with the distributed algorithm that is applied to the system, the performance of the system might be compromised. Specifically, it is considered that adversarial agents (microgrids with their controllers) might implement control inputs that are different than the ones obtained from the distributed algorithm. By performing such behavior, these agents might have better performance at the expense of deteriorating the performance of the regular agents. This paper proposes a methodology to deal with this type of adversarial agents such that we can still guarantee that the regular agents can still obtain feasible, though suboptimal, control inputs in the presence of adversarial behaviors. The methodology consists of two steps: (i) the robustification of the underlying optimization problem and (ii) the identification of adversarial agents, which uses hypothesis testing with Bayesian inference and requires to solve a local mixed-integer optimization problem. Furthermore, the proposed methodology also prevents the regular agents to be affected by the adversaries once the adversarial agents are identified. In addition, we also provide a sub-optimality certificate of the proposed methodology.

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