Marco Perez Hernandez |

Collective Dynamics of Behaviourally-Motivated Energy Peak Moderation

Thu, 01 Jan 2026 00:00:00 +0000

Abstract

Managing peak loads from uneven daily demand is critical for modern national grids. Most approaches address this from technical, financial, or other individual perspectives. While the multidimensional nature of energy demand is recognised among researchers and industry, the dynamics and drivers of individual and collective behaviours needed to protect infrastructures are not fully understood. At the residential level, studies lack analysis of the complex mix of social and individual motivations behind energy management decisions. Moreover, research concentrates on consumption reduction, showing decreasing marginal impact long term. This work addresses this gap by modelling household energy demand dynamics emphasising three combined motivations: social identity, household energy practices, and individual constraints. The proposed agent-based model integrates reference theory, empirical data from Bristol and Glasgow households, and UK statistics. The approach considers household awareness of energy challenges, members' routines and the role of community identity in energy-related decisions. The dynamics are analysed considering time-of-use variations, revealing adaptation behaviours with potential for consistent long-term response. Extensive simulations and sensitivity analysis show distinct effects of household profiles on demand patterns, with strong social identity provoking firm collective response reflected in rapid demand adaptation to community needs. The model enables exploration of energy demand dynamics within communities and evaluation of factors promoting consistent behaviours that contribute to grid peak load moderation.

Source: https://ieeexplore.ieee.org/abstract/document/11456890/

Behavioural analysis of independent value-based learning in non-cooperative games

Wed, 01 Jan 2025 00:00:00 +0000

Abstract

Multi-agent reinforcement learning has received increased attention in cooperative games. However, research in non-cooperative games is lagging behind. Independent value-based learning algorithms have demonstrated simplicity and versatility in various contexts. In this paper, we study the behavior of these algorithms in non-cooperative settings. We explain the conditions that a game must satisfy for the algorithms to work. We further test the algorithms in our proposed game Food Chain that simulates an ecosystem. Our results show that independent value-based learning algorithms can converge to Nash equilibrium, only when the Nash equilibrium consists of uniformly random policies over the feasible actions.

Source: https://www.tandfonline.com/doi/abs/10.1080/17445760.2025.2601000

An agent-based approach for energy-efficient sensor networks in logistics

Mon, 01 Jan 2024 00:00:00 +0000

Abstract

As part of the fourth industrial revolution, logistics processes are augmented with connected information systems to improve their reliability and sustainability. Above all, customers can analyse process data obtained from the networked logistics operations to reduce costs and increase margins. The logistics of managing liquid goods is particularly challenging due to the strict transport temperature requirements involving monitoring via sensors attached to containers. However, these sensors transmit much redundant information that, at times, does not provide additional value to the customer, while consuming the limited energy stored in the sensor batteries. This paper aims to explore and study alternative approaches for location tracking and state monitoring in the context of liquid goods logistics. This problem is addressed by using a combination of data-driven sensing and agent-based modelling techniques. The simulation results show that the longest life span of batteries is achieved when most sensors are put into sleep mode yielding an increase of× 21. 7 and× 3. 7 for two typical routing scenarios. However, to allow for situations in which high quality sensor data is required to make decisions, agents need to be made aware of the life cycle phase of individual containers. Key contributions include (1) an agent-based approach for modelling the dynamics of liquid goods logistics to enable monitoring and detect inefficiencies (2) the development and analysis of three sensor usage strategies for reducing the energy consumption, and (3) an evaluation of the trade-offs between energy consumption and location tracking precision for timely decision making in resource constrained monitoring systems.

Source: https://www.sciencedirect.com/science/article/pii/S0952197623013829

Multi-agent learning of asset maintenance plans through localised subnetworks

Mon, 01 Jan 2024 00:00:00 +0000

Abstract

Maintenance planning of networked multi-asset systems is a complex problem due to the inherent individual and collective asset constraints and dynamics as well as the size of the system and interdependencies among assets. Although multi-asset systems have been studied numerous times in the past decades, maintenance planning implications of the system’s network characteristics have been barely analysed. Likewise, solutions that consider the network perspective suffer from scalability issues as a network-wide observability is assumed. This paper proposes a network maintenance planning approach based on the decomposition of the multi-asset network into fixed-size localised subnetworks. The overall network maintenance plan is produced by aggregating the subnetwork maintenance plans, which are computed independently via a multi-agent deep reinforcement learning (MARL) algorithm. The results are evaluated against a network-wide approach as well as the commonly-used individual approach. The paper also introduces a systematic approach to integrate the MARL resulting policy in a multi-asset agent-based model. Simulation results of several random asset networks and a large nationwide network infrastructure show that, although a network-wide approach outperforms, on average, other approaches considered, the localised subnetworks approach, provides an acceptable alternative in networks with small-world properties, without the need of a network-wide view.

Source: https://www.sciencedirect.com/science/article/pii/S0952197623015464

Maintenance strategies for networked assets

Sat, 01 Jan 2022 00:00:00 +0000

Abstract

The purpose of this paper is to analyse the effect of different maintenance strategies for a network of assets whose condition deteriorates progressively along the time. We propose both an agent-based model that considers the dynamics of data traffic and asset deterioration in a data packet transport network; and a network-wide maintenance planning optimisation algorithm. Several network topologies are used to evaluate the maintenance strategies and determine the magnitude of the differences. Simulation results, in networks of different sizes and configurations, suggest that there are cases when a network-wide maintenance strategy could be up to 38% more effective in reducing the impact of the unavailability of assets due to maintenance, while keeping the lowest cost, compared to analysed alternatives.

Source: https://www.sciencedirect.com/science/article/pii/S2405896322014148