Future industrial systems can be realized using the cyber–physical systems (CPSs) that advocate the coexistence of cyber and physical counterparts in a network structure to perform the system’s functions in a collaborative manner. Multiagent systems share common ground with CPSs and can empower them with a multitude of capabilities in their efforts to achieve complexity management, decentralization, intelligence, modularity, flexibility, robustness, adaptation, and responsiveness. This work surveys and analyzes the current state of the industrial application of agent technology in CPSs, and provides a vision on the way agents can effectively enable emerging CPS challenges.

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Smart Agents in Industrial Cyber–Physical Systems

The energy landscape is experiencing accelerating change; centralized energy systems are being decarbonized, and transitioning towards distributed energy systems, facilitated by advances in power system management and information and communication technologies. This paper elaborates on these generations of energy systems by critically reviewing relevant authoritative literature. This includes a discussion of modern concepts such as ‘smart grid’, ‘microgrid’, ‘virtual power plant’ and ‘multi-energy system’, and the relationships between them, as well as the trends towards distributed intelligence and interoperability. Each of these emerging urban energy concepts holds merit when applied within a centralized grid paradigm, but very little research applies these approaches within the emerging energy landscape typified by a high penetration of distributed energy resources, prosumers (consumers and producers), interoperability, and big data. Given the ongoing boom in these fields, this will lead to new challenges and opportunities as the status-quo of energy systems changes dramatically. We argue that a new generation of holonic energy systems is required to orchestrate the interplay between these dense, diverse and distributed energy components. The paper therefore contributes a description of holonic energy systems and the implicit research required towards sustainability and resilience in the imminent energy landscape. This promotes the systemic features of autonomy, belonging, connectivity, diversity and emergence, and balances global and local system objectives, through adaptive control topologies and demand responsive energy management. Future research avenues are identified to support this transition regarding interoperability, secure distributed control and a system of systems approach.

Towards the next generation of smart grids: semantic and holonic multi-agent management of distributed energy resources

Power imbalances from fluctuating renewable electricity generators are counteracted by often expensive flexibility services. Heating, cooling, and air-conditioning (HVAC) of buildings, or domestic power-to-heat (P2H), are end uses of electricity that allow flexible load patterns due to the inertia of an attached thermal storage while meeting their quality constraints. Compared to smart appliances or electric vehicle charging, P2H exhibits large and predictable capacities of demand response (DR), because buildings in many countries account for 30–40% of the final energy demand, a large part of which is thermal. Yet, its practical flexibility potential remains largely unknown: is DR from P2H a mature technology for mass usage; is it cost-efficient, socially attractive, and ready to make key contributions to flexibility comparable to backup generators or battery storage? In the present paper, we review recent international field studies that are paving the way from research to practice. These field trials include real customers but have a broader research focus and a wider outreach than rolling out a new DR tariff or program or a specific new technology for DR. Their experience mirrors the technology readiness beyond revenue or policy studies, optimization frameworks or laboratory-scale micro-grids. We analyze the adequacy of the pricing mechanisms deployed for incentivization and remuneration and review the coordination mechanisms for balancing on different timescales including fast ancillary services. We conclude that current control and information technology and economic and regulatory frameworks which have been field-tested do not yet meet the flexibility challenges of smart grids with a very high share (>50%) of intermittent renewable generation.

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Large-scale grid integration of residential thermal energy storages as demand-side flexibility resource: A review of international field studies

Process mining automatically produces a process model while considering only an organization’s records of its operational processes. Over the last decade, many process discovery techniques have been developed, and many authors have compared these techniques by focusing on the properties of the models produced. However, none of the current techniques guarantee to produce sound (i.e., syntactically correct) process models. Furthermore, none of the current techniques provide insights into the trade-offs between the different quality dimensions of process models. In this thesis we present the Evolutionary Tree Miner (ETM) framework. Its main feature is the guarantee that the discovered process models are sound. Another feature is that the ETM framework also incorporates all four well-known quality dimensions in process discovery (replay fitness, precision, generalization and simplicity). Additional quality metrics can be easily added to the Evolutionary Tree Miner. The Evolutionary Tree Miner framework is able to balance these different quality metrics and is able to produce (a collection of) process models that have a specific balance of these quality dimensions, as specified by the user. The third main feature of the Evolutionary Tree Miner is that it is easily extensible. In this thesis we discuss extensions for the discovery of a collection of process models with different quality trade-offs, the discovery of (a collection of) process models using a given process model, and the discovery of a configurable process model that describes multiple event-logs. The Evolutionary Tree Miner is implemented as a plug-in for the process mining toolkit ProM. The Evolutionary Tree Miner and all of its extensions are evaluated using both artificial and real-life data sets.

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Flexible evolutionary algorithms for mining structured process models

Process mining methods analyze an organization’s processes by using process execution data. During the handling of a process instance data about the execution of activities is recorded. Process mining uses such data to gain insights about the real execution of processes. In this thesis, we address research challenges in which a multi-perspective view on processes is needed and that look beyond the control-flow perspective, which defines the sequence of activities of a process. We consider problems in which multiple interacting process perspectives — in particular control-flow, data, resources, time, and functions — are considered together. The contributed methods span several types of process mining: two are concerned with conformance checking, two are process discovery techniques, and one is a decision mining method. All methods have been implemented, evaluated, and applied in the context of four case studies.

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Multi-perspective process mining

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J.K.A.H.P.J. Kok

Papers

The PowerMatcher: Smart Coordination for the Smart Electricity Grid