Abstract
This article presents a robust chance-constrained optimization framework for the optimal operation management of an energy hub (EH) in the presence of electrical, heating, and cooling demands, and renewable power generation. The proposed strategy can be used for optimal decision making of operators of EHs or energy providers. The electrical energy storage device in the studied EH can handle the fluctuations in operating points raised by such uncertainties. In order to model the hourly demands and renewable power generation uncertainties, a robust chance-constrained close-to-real-time model is adopted in this article. The considered EH in this study follows a centralized framework and the EH operator is responsible for the optimal operation of the hub assets based on the day-ahead scheduling. A thorough analysis of energy flows with different carriers is presented. In addition, a numerical stability test regarding the selection of the time step size is performed to guarantee the solution's time resolution independence, occurring in previous studies.

Abstract

Abstract
This paper presents a self-scheduling model for home energy management systems (HEMS) in which a novel formulation of a linear discomfort index (DI) is proposed, incorporating the preferences of end-users in the daily operation of home appliances. The HEMS self-scheduling problem is modelled as a mixed-integer linear programming (MILP) multi-objective problem, aimed at minimizing the energy bill and DI. In this framework, the proposed DI determines the optimal time slots for the operation of home appliances while minimizing end-users? bills. The resulting multi-objective optimization problem has then been solved by using the epsilon-constraint technique and the VIKOR decision maker has been employed to select the most desired Pareto solution. The proposed model is tested considering tariffs in the presence of various price-based demand response programs (DRP), namely time-of-use (TOU) and real-time pricing (RTP). In addition, different scenarios considering the presence of electrical energy storage (EES) are investigated to study their impact on the optimal operation of HEMS. The simulation results show that the self-scheduling approach proposed in this paper yields significant reductions in the electricity bills for different electricity tariffs.

Abstract
An original graph-based model and algorithm for optimal industrial task scheduling is proposed in this article. The innovative algorithm designed, dubbed "Dijkstra optimal tasking" (DOT), is suitable for fully distributed task scheduling of autonomous industrial agents for optimal resource allocation, including energy use. The algorithm was designed starting from graph theory fundamentals, from the ground up, to guarantee a generic nature, making it applicable on a plethora of tasking problems and not case-specific. For any industrial setting in which mobile agents are responsible for accomplishing tasks across a site, the objective is to determine the optimal task schedule for each agent, which maximizes the speed of task achievement while minimizing the movement, thereby minimizing energy consumption cost. The DOT algorithm is presented in detail in this manuscript, starting from the conceptualization to the mathematical formulation based on graph theory, having a thorough computational implementation and a detailed algorithm benchmarking analysis. The choice of Dijkstra as opposed to other shortest path methods (namely, A* Search and Bellman-Ford) in the proposed graph-based model and algorithm was investigated and justified. An example of a real-world application based on a refinery site is modeled and simulated and the proposed algorithm's effectiveness and computational efficiency is duly evaluated. A dynamic obstacle course was incorporated to effectively demonstrate the proposed algorithm's applicability to real-world applications.

Abstract
Due to technological advances, the growing need for a decarbonized economy, and the desire to reduce urban air pollution, electric vehicles (EVs) are seen as promising developments for the progressive decarbonization of the transport sector. The potential for large scale integration of solar photovoltaic (PV) systems has not been explored fully, compared to other renewable energy uses in power systems. One of the proposed paths for the sustainable integration of EVs and solar exploitation in power systems involves the creation or installation of parking lots fitted with solar PV systems on their rooftops. This concept increases the reliability and robustness of the power system, as studies show that EVs are parked for the vast majority of the time. The EVs can then be optimally managed to assist the network in critical moments. The solar parking lot would then serve as a backup to manage the EVs' state-of-charge (SoC) which would guarantee the owners' comfort and help to accelerate the decarbonization of the economy. The present work presents a comprehensive survey of the state-of-the-art concepts of photovoltaic (PV) panels, EVs and batteries, and how the different associated technologies can be applied in the concept of solar parking lots.