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• The study makes the following main contributions: • 1) A mathematical layout is developed to include renewable resources for load scheduling in digital twin-based microgrids. • 2) Uncertainty modelling of PV production employing Beta PDF for load scheduling is utilized in digital twin environment. • 3) The load scheduling algorithm employing RL for minimizing the energy bills regarding a renewable resource is developed. Utility companies recognize the importance and necessity of demand response (DR) programs for reducing the increased production costs associated with rising energy demand. The advent of smart information and communication systems has made DR programs on the basis of cost a viable option to control load in smart microgrids. Small domestic consumers are rapidly using stochastic renewable energy resources such as photovoltaic (PV). The study examines an integrated layout for residential load scheduling or load commitment problems (LCP) with renewable energy resources no matter what kind of tariff is applied. Uncertainty-based decision-making problems are effectively solved using reinforcement learning (RL). The paper proposes an RL-enabled solution to the LCP in smart microgrids. An innovative aspect of the study is the development of an integrated layout containing an implementable solution that takes into account user satisfaction, stochastic renewable power, and tariffs. In simulation tests, the suggested layout is tested for its effectiveness and flexibility. An analysis of the algorithm's efficiency using a household user with schedule-able and non-schedulable devices, together with a PV resource, has been presented. [ABSTRACT FROM AUTHOR]

This paper concerns development and analysis of a solar-wind hybrid multigeneration system. Energy, exergy, exergoeconomic and exergoenvironmental analyses are performed. The analysis studies are undertaken by developing and constructing the codes in Engineering Equation Solver software. The effects of various input conditions on the system performance are investigated through both energy and exergy efficiencies, and an optimization study is undertaken of system efficiency and power output are obtained. The average number of Toronto suites that the system can supply is calculated. As a result, it is seen that energy and exergy efficiencies are higher than equivalent single energy systems. The system has 43% maximum energy efficiency and 65% maximum exergy efficiency. Maximum turbine output is 48 kW, while cooling effect is 28 kW and heating effect is 298.5 kW. There is resultant savings of 1614 tons of CO 2 per year by the system. This multigeneration system is capable of supplying at least at a minimum 49 suites. [ABSTRACT FROM AUTHOR]

Renewable hybrid systems with small capacities are widely installed for joining demand response in modern communities. Based on hybrid systems, renewable energy generation can be stored and used for power supply during peak load period. It is impractical to control power flow in such small-scale systems, because power flow dispatching asks for extra investment of expensive regulators and adaptors. In this paper, a switching grid connected photovoltaic system is studied for simplifying system installation. Optimal switching control model is proposed to sufficiently utilize the solar energy and to minimize electricity cost under the time-of-use program. As shown in results, optimal scheduling of the PV system can achieve promising cost savings. [ABSTRACT FROM AUTHOR]