Strategic recommendations for financing green and sustainable energy projects

The main hindrances to the large-scale development of renewable-energy projects are the lack of bankability and the inability to align investments and investors with suitable financial instruments or robust policy measures.To illustrate a bankable project,this paper presents a research-based case study on the installation of solar photovoltaic panels on the rooftops of 195 trains of the Indian Railways.Detailed information on the annual running hours,exposure to sunlight,efficiency of solar photovoltaic generation and electrical power demands of each rail coach is considered to conduct a quantitative measure of the tentative amount of fossil fuel sav-ings.The purpose is to provide insight into the types of renewable-energy projects that can be highly attractive to financial institutions and promoters due to their lucrative internal return on investment.As seen in this case study,there are annual savings in diesel of 12323088 litres and a CO_(2) reduction of 32755 tonnes,with return on investment of 1.3 years.Furthermore,this study conducts a com-prehensive analysis of the limitations of existing renewable-energy project financing mechanisms in India.Subsequently,three policy measures are recommended to develop a robust financial mechanism that can effectively meet the needs of investors and investors.These measures include increasing equity injection through a buy-and-hold strategy,providing direct tax benefits to promoters and financing through real-estate investment trusts.The findings are highly relevant to address the challenges associated with bridging the financial gap between access to finance and capital investment in the renewable-energy sector,especially for Asian countries.

Approximating multi-purpose AC Optimal Power Flow with reinforcement trained Artificial Neural Network

Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment from renewable DERs(Distributed Energy Resource).Mathematical solvers are often able to solve the AC-OPF problem but need significant computation time.Artificial neural networks(ANN)have a good application in function approximation with outstanding computational performance.In this paper,we employ ANN to approximate the solution of AC-OPF for multiple purposes.The novelty of our work is a new training method based on the reinforcement learning concept.A high-performance batched power flow solver is used as the physical environment for training,which evaluates an augmented loss function and the numerical action gradient.The augmented loss function consists of the objective term for each use case and the penalty term for constraints violation.This training method enables training without a reference OPF and the integration of discrete decision variable such as discrete transformer tap changer position in the constrained optimization.To improve the optimality of the approximation,we further combine the reinforcement training approach with supervised training labeled by reference OPF.Various benchmark results show the high approximation quality of our proposed approach while achieving high computational efficiency on multiple use cases.