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This special issue contains six revised and extended versions of tool papers that appeared in the proceedings of TACAS 2021, the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems. The issue is dedicated to the realization of algorithms in tools and the studies of the application of these tools for analysing hard- and software systems. [ABSTRACT FROM AUTHOR]

Heatpump-based floor-heating systems for domestic heating offer flexibility in energy consumption patterns, which can be utilized for reducing heating costs—in particular when considering hour-based electricity prices. Such flexibility is hard to exploit via classical Model Predictive Control (MPC), and in addition, MPC requires a priori calibration (i.e., model identification) which is often costly and becomes outdated as the dynamics and use of a building change. We solve these shortcomings by combining recent advancements in stochastic model identification and automatic (near-)optimal controller synthesis. Our method suggests an adaptive model-identification using the tool CTSM-R , and an efficient control synthesis based on Q-learning for Euclidean Markov Decision Processes via Uppaal Stratego. This paper investigates three potential control strategy perspectives (i.e., fixed-target, target-band, and setbacks) to achieve energy efficiency in the heating system. To examine the performance of the suggested approaches, we demonstrate our method on an experimental Danish family-house from the OpSys project. The results show that a fixed-target strategy offers up to a 39 % reduction in heating cost while retaining comparable comfort to a standard bang-bang controller. Even better, target-band and setbacks strategies gain up to 46-49 % energy cost savings. Furthermore, we show the flexibility of our method by computing the Pareto-frontier that visualizes the cost/comfort tradeoff. Additionally, we discuss the applicability of Stratego for an old-fashioned binary-mode heat-pump system and report significant cost savings (33 %) as compared to the bang-bang controller. Moreover, we also present the performance analysis of Stratego against an industry-standard control strategy. [ABSTRACT FROM AUTHOR]

Increasing the penetration of Renewable Energy Sources (RES), e.g. wind and solar, intermittency and volatility of the supply-side are increasing in power systems worldwide. Therefore, the power systems need alternative forms of flexibility potentials to hedge against the intermittent power. District Heating Systems (DHS), especially Heat Pump Systems (HPS), show true potentials to provide demand-side flexibility for power systems. This paper aims to survey the literature on the applications of DHS in power system flexibility. In this way, first of all, the basic structure of the DHS, including the heat resources, thermal units, and thermal storage, is surveyed to give insight into the DHS problem. Afterward, classifying flexibility potentials of the DHS, the conventional and advanced control strategies of the DHS are reviewed comprehensively to investigate the role of heat controllers on power system flexibility. To study the compatibility of the controllers to flexible energy markets, the roles of different control schemes in successive trading floors of the electricity markets, from the energy market to ancillary service markets, are investigated. Finally, the optimization solutions, including mathematical and heuristic approaches, with software tools are reviewed to give readers general insight into the way a DHS problem is modeled and solved. • Review of flexibility potentials of district heating in uncertain electricity markets. • Classification of heat controllers based on flexibility requirements of supply-side. • Hierarchical integration of flexibility potentials of district heating systems. • Focus on software tools to unlock heat flexibility of district heating systems. • Applications of the flexibility of thermal inertia of building, heat carriers, and heat storage. [ABSTRACT FROM AUTHOR]