Publications
Journals
Abstract
The Internet of Things equips citizens with phenomenal new means for online participation in sharing economies. When agents self-determine options from which they choose, for instance their resource consumption and production, while these choices have a collective system-wide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens’ autonomy. This paper envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy and participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for decentralized collective learning. This disruptive concept is realized by I-EPOS, the Iterative Economic Planning and Optimized Selections, accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that involve non-local brute-force operations or exchange full information. This paper contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic trade-offs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies.
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Abstract
Supply-demand systems in Smart City sectors such as energy, transportation, telecommunication, are subject of unprecedented technological transformations by the Internet of Things. Usually, supply-demand systems involve actors that produce and consume resources, e.g. energy, and they are regulated such that supply meets demand, or demand meets available supply. Mismatches of supply and demand may increase operational costs, can cause catastrophic damage in infrastructure, for instance power blackouts, and may even lead to social unrest and security threats. Long-term, operationally offline and top-down regulatory decision-making by governmental officers, policy makers or system operators may turn out to be ineffective for matching supply-demand under new dynamics and opportunities that Internet of Things technologies bring to supply-demand systems, for instance, interactive cyberphysical systems and software agents running locally in physical assets to monitor and apply automated control actions in real-time, e.g. power flow redistributions by smart transformers to improve the Smart Grid reliability. Existing work on online regulatory mechanisms of matching supply-demand either focuses on game-theoretic solutions with assumptions that cannot be easily met in real-world systems or assume centralized management entities and local access to global information. This paper contributes a generic decentralized self-regulatory framework, which, in contrast to related work, is shaped around standardized control system concepts and Internet of Things technologies for an easier adoption and applicability. The framework involves a decentralized combinatorial optimization mechanism that matches supply-demand under different regulatory scenarios. An evaluation methodology, integrated within this framework, is introduced that allows the systematic assessment of optimality and system constraints, resulting in more informative and meaningful comparisons of self-regulatory settings. Evidence using real-world datasets of energy supply-demand systems confirms the effectiveness and applicability of the self-regulatory framework. It is shown that a higher informational diversity in the options, from which agents make local selections, results in a higher system-wide performance. Several strategies with which agents make selections come along with measurable performance trade-offs creating a vast potential for online adjustments incentivized by utilities, system operators and policy makers.
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Abstract
The increase in deployment of smart meters has enabled collection of fine-grained energy consumption data at consumer premises. Analysis of this real-time energy consumption data bestows new opportunities for better demand-response (DR) programs. This work offers a new perspective to study energy demand and helps in designing novel mechanisms for decentralized demand-side management. Specifically, a new concept of finding the demand states using energy consumption of consumers over time and, feasible transitions therein, are introduced. It is shown that the orchestration of temporal transitions between the demand states can meet broad range of Smart Grid objectives. An online demand regulation model is developed that captures the temporal dynamics of energy demand to identify target consumers for different DR programs. This methodology is empirically evaluated and validated using data from more than 4000 households, which were part of a real-world Smart Grid project. This work is the first one to comprehensively analyze the temporal dynamics of demands.
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Abstract
The robustness of Smart Grids is challenged by unpredictable power peaks or temporal demand oscillations that can cause black-outs and increase supply costs. Planning of demand can mitigate these effects and increase robustness. However, the impact on consumers in regards to the discomfort they experience as a result of improving robustness is usually neglected. This paper introduces a decentralized agent-based approach that quantifies and manages the trade-off between robustness and discomfort under demand planning. Eight selection functions of plans are experimentally evaluated using real data from two operational Smart Grids. These functions can provide different quality of service levels for demand-side energy self-management that capture both robustness and discomfort criteria.
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Abstract
Distributed management of complex large-scale infrastructures, such as power distribution systems, is challenging. Sustainability of these systems can be achieved by enabling stabilisation in global resource utilisation. This paper proposes EPOS, the Energy Plan Overlay Self-stabilisation system, for this purpose. EPOS is an agent-based approach that performs self-stabilisation over a tree overlay, as an instance of a hierarchical virtual organisation. The global goal of stabilisation emerges through local knowledge, local decisions and local interactions among software agents organised in a tree. Two fitness functions are proposed to stabilise global resource utilisation. The first proactively keeps deviations minimised and the second reactively reverses deviations. Extensive experimentation reveals that EPOS outperforms a system that utilises resources in a greedy manner. Finally, this paper also investigates and evaluates factors that influence the effectiveness of EPOS.
Abstract
The increase in deployment of smart meters has enabled collection of fine-grained energy consumption data at consumer premises. Analysis of this real-time energy consumption data bestows new opportunities for better demand-response (DR) programs. This work offers a new perspective to study energy demand and helps in designing novel mechanisms for decentralized demand-side management. Specifically, a new concept of finding the demand states using energy consumption of consumers over time and, feasible transitions therein, are introduced. It is shown that the orchestration of temporal transitions between the demand states can meet broad range of Smart Grid objectives. An online demand regulation model is developed that captures the temporal dynamics of energy demand to identify target consumers for different DR programs. This methodology is empirically evaluated and validated using data from more than 4000 households, which were part of a real-world Smart Grid project. This work is the first one to comprehensively analyze the temporal dynamics of demands.
[fa type=”file-pdf-o”] Download
[fa type=”file-pdf-o”] Supplementary Information
Abstract
The robustness of Smart Grids is challenged by unpredictable power peaks or temporal demand oscillations that can cause black-outs and increase supply costs. Planning of demand can mitigate these effects and increase robustness. However, the impact on consumers in regards to the discomfort they experience as a result of improving robustness is usually neglected. This paper introduces a decentralized agent-based approach that quantifies and manages the trade-off between robustness and discomfort under demand planning. Eight selection functions of plans are experimentally evaluated using real data from two operational Smart Grids. These functions can provide different quality of service levels for demand-side energy self-management that capture both robustness and discomfort criteria.
[fa type=”file-pdf-o”] Download
Abstract
Distributed management of complex large-scale infrastructures, such as power distribution systems, is challenging. Sustainability of these systems can be achieved by enabling stabilisation in global resource utilisation. This paper proposes EPOS, the Energy Plan Overlay Self-stabilisation system, for this purpose. EPOS is an agent-based approach that performs self-stabilisation over a tree overlay, as an instance of a hierarchical virtual organisation. The global goal of stabilisation emerges through local knowledge, local decisions and local interactions among software agents organised in a tree. Two fitness functions are proposed to stabilise global resource utilisation. The first proactively keeps deviations minimised and the second reactively reverses deviations. Extensive experimentation reveals that EPOS outperforms a system that utilises resources in a greedy manner. Finally, this paper also investigates and evaluates factors that influence the effectiveness of EPOS.
Conferences
Abstract
The democratization of Internet of Things and ubiquitous computing equips citizens with phenomenal new ways for online participation and decision-making in application domains of smart grids and smart cities. When agents autonomously self-determine the options from which they make choices, while these choices collectively have an overall system-wide impact, an optimal decision-making turns into a combinatorial optimization problem known to be NP-hard. This paper contributes a new generic self-adaptive learning algorithm for a fully decentralized combinatorial optimization: I-EPOS, the Iterative Economic Planning and Optimized Selections. In contrast to related algorithms that simply parallelize computations or big data and deep learning systems that often require personal data and overtake of control with implication on privacy-preservation and autonomy, I-EPOS relies on coordinated local decision-making via structured interactions over tree topologies that involve the exchange of entirely local and aggregated information. Strikingly, the cost-effectiveness of I-EPOS in regards to performance vs. computational and communication cost highly outperforms other related algorithms that involve non-local brute-force operations or exchange of full information. The algorithm is also evaluated using real-world data from two state-of-the-art pilot projects of participatory sharing economies: (i) energy management and (ii) bicycle sharing. The contribution of an I-EPOS open source software suite implemented as a paradigmatic artifact for community aspires to settle a knowledge exchange for the design of new algorithms and application scenarios of sharing economies towards highly participatory and sustainable digital societies.
Abstract
Pervasive technologies in socio-technical domains such as smart cities and smart grids question the values required for designing sustainable and participatory digital societies. Privacy-preservation, scalability, fairness, autonomy, and social-welfare are vital for democratic sharing economies and usually require computing systems designed to operate in a decentralized fashion. This paper examines sonification as the means for the general public to conceive decentralized systems that are too complex or non-intuitive for the mainstream thinking and general perception in society. We sonify two complex datasets that are generated by a prototyped decentralized system of computational intelligence operating with real-world data. The applied sonification methodologies are largely ad-hoc and address a series of concerns that are of both artistic and scientific merit. We create informative, effective and aesthetically meaningful soundworks as the means to probe and speculate complex, even unknown or unidentified, content. In this particular case, the sonifi- cation represents the constitutional narrative of two complex application scenarios of decentralized systems towards their equilibria.
Abstract
This paper illustrates socio-technical trade-offs in selfregulating Smart Grids. Social and technical factors such as robustness, discomfort and fairness are measured and evaluated using data from real-world operational Smart Grids projects. Results show a broad spectrum of socio-technical trade-offs required for effectively self-regulating Smart Grids. Such trade-offs can make future societies more participatory and self-sustainable.
Abstract
Demand-side energy management improves robustness and efficiency in Smart Grids. Load-adjustment and loadshifting are performed to match demand to available supply. These operations come at a discomfort cost for consumers as their lifestyle is influenced when they adjust or shift in time their demand. Performance of demand-side energy management mainly concerns how robustness is maximized or discomfort is minimized. However, measuring and controlling the distribution of discomfort as perceived between different consumers provides an enriched notion of fairness in demand-side energy management that is missing in current approaches. This paper defines unfairness in demand-side energy management and shows how unfairness is measurable and controllable by software agents that plan energy demand in a decentralized fashion. Experimental evaluation using real demand and survey data from two operational Smart Grid projects confirms these findings. Index Terms—unfairness, fairness, agent, planning, demand, Smart Grid, load-adjustment, load-shifting.
Abstract
Distributed management of complex, distributed systems is the focus of this paper. Adaptation through local deliberation by software agents within a hierarchical virtual organization is the approach taken. Global stabilization of resource utilization is the goal. Electricity networks are used to illustrate the potential of two fitness functions on the basis of which local choices for resource utilization are made: minimizing oscillations is the first function considered, reversing oscillations the second. Results reveal considerable increase in the stabilization of resource utilization compared to a system that utilizes resources in a greedy manner.
Abstract
Problem
Load peaks in energy consumption lead to higher cost for suppliers and consumers
Approach
Adaptive distributed coordination of agents’ plans through decentralized hierarchical aggregation
Results
Higher stabilization: (i) than random plan selection, (ii) as number of possible plans increases, (iii) as more agents are connected to the tree
Abstract
Distributed management of complex, distributed systems is the focus of this paper. Adaptation through local deliberation by software agents within a hierarchical virtual organization is the approach taken. Global stabilization of resource utilization is the goal. Electricity networks are used to illustrate the potential of two fitness functions on the basis of which local choices for resource utilization are made: minimizing oscillations is the first function considered, reversing oscillations the second. Results reveal considerable increase in the stabilization of resource utilization compared to a system that utilizes resources in a greedy manner.
Workshops
Abstract
Synchronization of energy consumption is a key determinant for the stabilization of smart energy grids. This paper proposes software agents that locally synchronize the energy usage of appliances to minimize the oscillations in global energy consumption. Agents can manage demand-side devices with periodic operation and synchronize their consumption locally resulting in an emerging global stability of energy consumption. The benefits and challenges of such an approach are discussed in this paper.
Abstract
Global stabilization of energy networks without centralized control is the challenge this paper addresses. An agent-based approach to decentralized self-management of networked appliances is the solution this paper explores. Software agents represent thermostatically controlled appliances (TCAs), generate energy plans for expected energy consumption, and interact with each other as peers within a tree based peer-to-peer overlay. The Energy Plan Overlay Summation (EPOS) mechanism proposed, propagates plans generated by individual TCA agents to aggregators within this structure to achieve self-optimization/stabilization of energy requests. Preliminary results in a small-scale and restrictive environment are promising: a 15% increase in energy stabilization is achieved.
Abstract
Global stabilization of energy networks without centralised control is a challenge. This paper explores the potential of coordination of energy usage of intelligent thermostatically controlled appliances (TCA) using a p2p network. A p2p tree overlay provides the basic structure for distributed plan aggregation and distribution. The approach is presented and discussed in the light of the risks and benefits identified.
Theses
Summary
The design and management of networked systems that are large-scale and decentralized is challenging. These systems are usually organized in virtual networks: the overlay networks. An overlay network lies at the application-level and on top of physical or other overlay networks. Overlay networks implement complex application and organizational functionality not supported by underlying network services. This integration and design approach results in low abstraction, modularity and reconfigurability of applications that are based on overlay networks. In contrast to this practice, this thesis introduces the conceptual architecture of ASMA, the Adaptive Self-organization in a Multi-level Architecture. ASMA is the main contribution of this thesis and is designed for building middleware systems of overlay networks that provide generic capabilities to di”erent distributed applications: the overlay services. The abstraction, modularity and reconfigurability of ASMA is achieved by its multilevel design approach. Three conceptually defined levels of overlay networks and their interactions provide discovery, structuring and coordination of system entities without a centralized management authority. The interactions between the three levels of ASMA form feedback loops that improve the quality of an overlay service incrementally. This thesis shows that a few lines of algorithmic expressions defined by ASMA are adequate to realize the complex system functionality of two introduced overlay services: (i) AETOS, the Adaptive Epidemic Tree Overlay Service and (ii) DIAS, the Dynamic Intelligent Aggregation Service. Both overlay services advance the state of the art by providing two generic application capabilities. AETOS builds and maintains overlay networks organized in tree topologies that meet di”erent application criteria. DIAS computes di”erent aggregation functions over a set of dynamically changing values distributed in an overlay network. Both overlay services of ASMA provide a proof-of-concept about their higher abstraction, modularity and reconfigurability at the cost of higher communication overhead compared to related work. AETOS provides self-organization of tree topologies with the graph properties of degree-bounding, ordering, balancing and completeness. AETOS performs a gossip-based discovery of nodes in a network. These nodes, ranked according to application criteria, are clustered based on their proximity computed by their ranking distance. Clustering of nodes as candidate parents and children provides a more cost-e”ective search space compared to random searching. Bidirectional links are negotiated and established with these parents and children based on ‘request’, ‘acknowledgment’, ‘rejection’ and ‘removal’ interactions. Di”erent tree topologies can be self-organized by adopting adaptation strategies that hide complex clustering and selection configurations. Experimental evaluation illustrates the performance trade-o”s and reconfigurability of AETOS in various experimental settings. This evaluation concludes that AETOS is a generic and flexible overlay service for the self-organization of tree topologies. DIAS makes aggregates, such as average, summation, maximum, etc., locally available in every node of an overlay network. In contrast to other related methodologies, aggregation in DIAS is function-independent, routing-independent and dynamic as aggregates are adapted if distributed input values change during runtime. DIAS achieves this abstraction and flexibility by introducing the concept of aggregation memberships. An aggregation membership provides historic information about a computed aggregation value by indicating if this value is new, outdated or duplicate. This distinction guarantees accurate computation of aggregates. It also provides two adaptation strategies based on which new or outdated aggregation values may be preferred in computations of aggregates. An explicit storage of aggregation memberships is not a scalable and decentralized aggregation approach. Nevertheless, DIAS stores aggregation memberships in probabilistic data structures: the bloom filters. A bloom filter provides large space savings at the cost of false positives. A distributed consistency mechanism is introduced to detect false positives and, therefore, prevent inaccuracies in the computations of aggregates. Experimental evaluation confirms the high accuracy of DIAS under di”erent experimental settings and performance trade-o”s. The applicability of AETOS and DIAS is studied in the domain of the Smart Power Grid. More specifically, two decentralized demand-side energy management mechanisms are introduced based on these overlay services: (i) EPOS, the Energy Plan Overlay Selfstabilization and (ii) ALMA, the Adaptive Load Adjustment by Aggregation. EPOS and ALMA are the contributions of this thesis in the application domain of the Smart Power Grid. EPOS coordinates the energy consumption of a large number of thermostatically controlled devices such as water heaters, refrigerators etc., to achieve the global system objectives. More specifically, EPOS performs self-stabilization by eliminating the oscillations and power peaks in the total energy consumption if and when it is required. Thermostatic devices are controlled by communicating software agents that generate, select and execute operational plans for their devices without direct involvement and impact on consumers. EPOS achieves energy self-stabilization by using AETOS to selforganize agents in a tree overlay network within which they perform a decentralized aggregation and coordinated decision-making of their local energy consumption. Experimental evaluation using synthetic data shows the high energy stabilization achieved in various experimental settings. ALMA complements EPOS under extreme conditions in which the Smart Power Grid requires an actual decrease or increase in the power demand due to failures or excessive micro-generation. ALMA achieves adjustments of aggregate energy consumption with possible demand options of local energy consumption, representing a wide range of comfort and economy levels, that can be pre-defined and dynamically selected by incentivized consumers. Aggregate information about power demand can be made locally available to consumers by the DIAS overlay service. The feasibility of ALMA is evaluated analytically using data from an operational Smart Power Grid: the Olympic Peninsula Smart Grid Demonstration Project. In conclusion, this thesis indicates that introducing decentralized computing systems in an information era expanding to new critical application domains, such as the Smart Power Grid, is a promising endeavor towards more sustainable development and a resource-based economy in future societies.