Abstract

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
Energy hubs (EHs) are units in which multiple energy carriers are converted, conditioned and stored to simultaneously supply different forms of energy demands. In this research, the objective is to develop a new stochastic model for unit commitment in EHs including an intelligent electric vehicle (EV) parking lot, boiler, photovoltaic (PV) module, fuel cell, absorption chiller, electric heat pump, electric/thermal/ cooling storage systems, with electricity and natural gas (NG) as inputs and electricity, heat, cooling and NG as demands. The uncertainties of demands, PV power and initial energy of EV batteries are modeled with Monte Carlo Simulation. The effect of demand response and demand participation factors as well as effect of EVs and storage systems on EH operation are investigated. The results indicate that thermal demand response is more effective than electric and cooling demand response; as it decreases EH operation cost by 12%, while electric demand response and cooling demand response decrease it respectively by 9.3% and 4.2%. The results show that at low electric/thermal/cooling demand participation factors, an increase in participation factor sharply decreases EH operation cost, while the same amount of increase at higher participation factors leads to a smaller decrease in operation cost. The results also indicate that thermal storage system and cooling storage system have significant effect on reduction of EH operation cost, while the effect of electric storage system is trivial.

Abstract
Increasing the penetration of renewables on prosumers' side brings about operational challenges in the distribution grid due to their variable and uncertain behavior. In fact, these resources have increased the distribution grid net load fluctuation during recent years. In this article, the flexibility-oriented stochastic scheduling of a microgrid is suggested to capture the net load variability at the distribution grid level. In this scheduling, the flexibility limits are set to manage the net load fluctuation at a desirable level for the main grid operator. The uncertainties of load and renewables are considered, and their uncertainties are under control by the risk-averse strategy. Moreover, multiperiod islanding constraints are added to the problem, preparing the microgrid for a resilient response to disturbances. The model is examined on a typical distribution feeder consisting of prosumers and a microgrid. The numerical results are compared for both flexibility-oriented and traditional scheduling of a microgrid at the distribution level. The proposed model reduces the net load ramping of the distribution grid using an efficient dispatch of resources in the microgrid. A sensitivity analysis is also carried out to show the effectiveness of the model.

Abstract
Energy hubs (EHs) are units wherein multiple energy carriers can be converted, stored and conditioned to simultaneously supply different energy demands. In this paper, a new model is proposed for unit commitment in renewable EHs with electric, thermal and cooling demands, different storage systems, combined heat and power (CHP) unit, boiler, electric chiller, absorption chiller, PV module, wind turbine and battery charging station (BCS). Using information gap decision theory (IGDT), day-ahead EH scheduling is done from risk-neutral, risk-averse and risk-seeking perspectives, considering the un-certainties of electric demands, BCS demands, heat demands, cooling demands, PV and wind power and electricity prices. Comprehensive models are used for storage systems considering their degradation, charging loss, discharging loss and storage loss; the ramp-up and ramp-down rate limits, start-up and shut-down costs of CHP, boiler and cooling components are considered. The effect of risk as well as effect of critical cost deviation factor and target cost deviation factor on EH operation cost and schedule of EH components is investigated. The findings indicate that the sensitivity of EH operation cost may be very different with respect to different sets of uncertain input data. The findings also show the significant effect of risk-awareness on schedule of EH components and its operation cost.

Abstract