Abstract
Cascaded utilization of natural gas, electric power, and heat could leverage synergetic effects among these energy resources, precipitating the advent of integrated energy systems. In such infrastructures, energy hub is an interface among different energy systems, playing the role of energy production, conversion, and storage. The capacity of energy hub largely determines how tightly these energy systems are coupled and how flexibly the whole system would behave. This paper proposes a data-driven two-stage robust stochastic programming model for energy hub capacity planning with distributional robustness guarantee. Renewable generation and load uncertainties are modelled by a family of ambiguous probability distributions near an empirical distribution in the sense of Kullback-Leibler (KL) divergence measure. The objective is to minimize the sum of the construction cost and the expected life-cycle operating cost under the worst-case distribution restricted in the ambiguity set. Network energy flow in normal operating conditions is considered; demand supply reliability in extreme conditions is taken into account via robust chance constraints. Through duality theory and sampling average approximation, the proposed model is transformed into an equivalent convex program with a nonlinear objective and linear constraints, and is solved by an outer-approximation algorithm that entails solving only linear program. Case studies demonstrate the effectiveness of the proposed model and method.

Abstract
Nowadays, vehicle to grid (V2G) capability of the electric vehicle (EV) is used in the smart distribution network (SDN). The main reasons for using the EVs, are improving air quality by reducing greenhouse gas emissions, peak demand shaving and applying ancillary service, and etc. So, in this chapter, a non-linear bi-level model for optimal operation of the SDN is proposed where one or more solar based-electric vehicle parking lots (PLs) with private owners exist. The SDN operator (SDNO) and the PL owners are the decision-makers of the upper-level and lower-level of this model, respectively. The objective functions at two levels are the SDNO’s profit maximization and the PL owners’ cost minimization. For transforming this model into the single-level model that is named mathematical program with equilibrium constraints (MPEC), firstly, Karush-Kuhn-Tucker (KKT) conditions are used. Furthermore, due to the complementary constraints and non-linear term in the upper-level objective function, this model is linearized by the dual theory and Fortuny-Amat and McCarl linearization method. In the following, it is assumed that the SDNO is the owner of the solar-based EV PLs. In this case, the proposed model is a single-level model. The uncertainty of the EVs and the solar system, as well as two programs, are considered for the EVs, i.e., controlled charging (CC) and charging/discharging schedule (CDS). Because of the uncertainties, a risk-based model is defined by introducing a Conditional Value-at-Risk (CVaR) index. Finally, the bi-level model and the single-level model are tested on an IEEE 33-bus distribution system in three modes; i.e., without the EVs and the solar system, with the EVs by controlled charging and with/without the solar system, and with the EVs by charging/discharging schedule and with/without the solar system. The main results are reported and discussed. © Springer Nature Switzerland AG 2020.

Abstract
This paper presents the optimal operation strategy for home energy management system (HEMS) in the presence of the inverter-based heating, ventilation and air conditioning (HVAC) system. The main target of this paper is to find the optimal scheduling of the home appliances in line with the optimal operation of the air conditioner system to reduce the daily bills while the end-users discomfort index would be minimized. In this paper, the mathematical formulation is represented in mixed-integer linear programming (MILP) framework to reduce the computational burden and easily be adapted by hardware for implementation. The HEMS is the main responsible for optimal scheduling of controllable and interruptible loads as well as serving the fixed loads. The electricity tariff is based on time-of-use (TOU) mechanism and three different tariffs have been considered during the daily consumptions. The simulation results for the daily operation of a residential home confirms that the proposed model can effectively reduce the electricity bill while the consumer predefined comfort level is appropriately maintained.

Abstract
Micromouse is one of the most popular competitions among mobile robotics researchers. This competition brings together several challenges in the field of mobile robotics. It represents an excellent tool in competition field since it stimulates the development and multidisciplinary knowledge as well as group cooperation to carry out the best approach. This work presents a contribution to this issue on exploring the unknown environment and the robot location to obtain an optimized trajectory in the maze, using the modified Floodfill algorithm that considers the cost for the robot rotations around its axis. A comparison is conducted between the modified algorithm and the traditional Floodfill procedure.

Abstract
Today's museums assume an increasing dynamism with the society. This new reality requires the continuous process of readjusting its activities. In this context, it is possible to see that the subject of Sustainable Development and Museums is becoming more and more present. However, to recognize, contribute or even know what to do in the face of this new challenge, a set of interdisciplinary actions is needed in the search for models, processes and modes of operation that can contribute to this new paradigm. In the face of this challenge, an initial study is presented that aims to draw attention to the need to measure the real contribution of Museums to Sustainable Development and suggests the continuity of the research with the organization of a methodological process that intends to select indicators to measure the levels of Museums' sustainability and, based on these results, recommends the elaboration of a Good Practices guide for Museums in Portugal.

Abstract
Assessing the dynamical complexity of biological time series represents an important topic with potential applications ranging from the characterization of physiological states and pathological conditions to the calculation of diagnostic parameters. In particular, cardiovascular time series exhibit a variability produced by different physiological control mechanisms coupled with each other, which take into account several variables and operate across multiple time scales that result in the coexistence of short term dynamics and long-range correlations. The most widely employed technique to evaluate the dynamical complexity of a time series at different time scales, the so-called multiscale entropy (MSE), has been proven to be unsuitable in the presence of short multivariate time series to be analyzed at long time scales. This work aims at overcoming these issues via the introduction of a new method for the assessment of the multiscale complexity of multivariate time series. The method first exploits vector autoregressive fractionally integrated (VARFI) models to yield a linear parametric representation of vector stochastic processes characterized by short- and long-range correlations. Then, it provides an analytical formulation, within the theory of state-space models, of how the VARFI parameters change when the processes are observed across multiple time scales, which is finally exploited to derive MSE measures relevant to the overall multivariate process or to one constituent scalar process. The proposed approach is applied on cardiovascular and respiratory time series to assess the complexity of the heart period, systolic arterial pressure and respiration variability measured in a group of healthy subjects during conditions of postural and mental stress. Our results document that the proposed methodology can detect physiologically meaningful multiscale patterns of complexity documented previously, but can also capture significant variations in complexity which cannot be observed using standard methods that do not take into account long-range correlations.