A new grey wolf optimization-based parameter estimation technique of solar photovoltaic

Hybrid electric propulsion, using batteries for energy storage, is making significant inroads into railway transportation because of its potential for notable fuel savings and the related reductions in greenhouse gases emissions of hybrid railway traction over non-electrified railway lines. Due to the inherent complexity of hybridized powertrains, combining different power conversions and energy storage capabilities, the corresponding operation of their energy management needs to be precisely optimized in order to achieve the minimum possible fuel consumption. Having this in mind, this paper proposes a real-time energy management control strategy for a diesel–electric hybrid locomotive based on the optimization results obtained by means of a dynamic programming optimization algorithm aimed at fuel consumption minimization while honoring the battery state-of-charge constraints and powertrain physical constraints. The final optimization result, expressed in terms of the optimal battery state-of-charge reference (target), is used as an additional input into the state-of-charge controller within the real-time energy management system. The subsequent simulation analysis shows clear fuel economy improvement with 22.9% of fuel savings obtained for the locomotive featuring a hybrid powertrain equipped with batteries over the conventional one.

/pdf/optimized-energy-management-control-of-a-hybrid-electric-jdt70vgy.pdf

Optimized Energy Management Control of a Hybrid Electric Locomotive

Photovoltaic solar power referred to as solar power using photovoltaic cells, is a renewable energy source. The solar cells' electricity may be utilized to power buildings, neighborhoods, and even entire cities. A stable and low-maintenance technology, photovoltaic solar power is an appealing alternative for generating energy since it emits no greenhouse gases and has no moving components. This paper aimed to provide a photovoltaic solar power generation forecasting model developed with machine learning approaches and historical data. In conclusion, this type of predictive model enables the evaluation of additional non-traditional sources of renewable energy, in this case, photovoltaic solar power, which facilitates the planning process for the diversification of the energy matrix. Random Forests obtain the highest performance, with this knowledge power systems operators may forecast outcomes more precisely, this is the main contribution of this work.

/pdf/predictive-modeling-of-photovoltaic-solar-power-generation-3fvq7nyr.pdf

Predictive Modeling of Photovoltaic Solar Power Generation

Power to gas (PtG) system, consisting of electrolytic hydrogen production and carbon capture from coal-fired power plant (CP), is emerging as the large-scale energy storage technology in addressing the fluctuations of the intermittent renewable energy power generation. Based on this, a coal-fired power plant based renewable power to gas system (CP-PtG) is constructed. However, the scheduling and configuration of the system is challenging due to the stochastic uncertainty of the renewable source and load demand. The difficulty is exacerbated by the slow dynamics of CP, which is generally neglected in the operational optimization for simplicity. To this end, this paper develops a two-layer stochastic optimization method. There are 9 given scenarios generated by PV-load joint temporal sequence strategy that preserves historical features, of which the upper layer utilizes the system life-cycle economic cost expectation from the lower layer as the fitness value. Fitness value is a necessary evolutionary criterion in the genetic algorithm searching for the optimal capacity; The lower layer is for the scheduling optimization, in which mixed integer linear programming (MILP) is used for the operational cost minimization of a typical day. The thermal dynamics is treated in this layer as the additional discretized state constraints of the optimization. The upper and lower layer are collaboratively optimized to obtain the optimal system capacity. The optimization results show that the capacity configuration of system is more sensitive to uncertainty, while its operational behavior is more sensitive to the dynamics of PGU. When the uncertainty is considered, the capacity of the key equipment increases by about 10%, which effectively improves the operation stability under intermittent and fluctuation. When the dynamic is considered, the actual output of PGU can better track its scheduling instructions by reducing about 7 times tracking deviation, which ensures system economical and stable operation. 

Unified scheduling and configuration optimization for power to gas system considering thermal dynamics and uncertainties

AIM-CARE: Autonomous intelligent modular charging apparatus for roadway electrics

Experimental verification of a dynamic programming and IoT-based simultaneous load-sharing controller for residential homes powered with grid and onsite solar photovoltaic electricity

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Experimental verification of a dynamic programming and IoT-based simultaneous load-sharing controller for residential homes powered with grid and onsite solar photovoltaic electricity