Future power networks are certain to have high penetrations of renewable distributed generation such as photovoltaics (PV). At times of high PV generation and low customer demand (e.g., summer), network voltage is likely to rise beyond limits mandated by grid codes resulting in a curtailment of PV generation, unless appropriate control means are used. This leads to a reduction in energy yield and consequently reduces the economic viability of PV systems. This work focuses on scenario-based impact assessments underpinned by a net prosumer load forecasting framework as part of power system planning to aid sustainable energy policymaking. Based on use-case scenarios, the efficacy of smart grid solutions demand side management (DSM) and Active Voltage Control in maximizing PV energy yield and therefore revenue returns for prosumers and avoided costs for distribution networks between a developed country (the UK) and developing country (India) is analyzed. The results showed that while DSM could be a preferred means because of its potential for deployment via holistic demand response schemes for India and similar developing nations, technically the combination of the weaker low voltage network with significantly higher solar resource meant that it is not effective in preventing PV energy curtailment. K E Y W O R D Sactive voltage control, demand side management, distributed generation, energy yield curtailment INTRODUCTIONThe decarbonisation of the energy network has created higher demand for electricity over oil and coal. Some of the electrical power network assets such as transformers and switchgear assets were installed as early as the 1950s and are still in use today. 1 For example, the UK's National Infrastructure Delivery Plan 2016-2021 identifies that "much of the existing infrastructure which has served us well is now old" and that "major investment is required to accommodate new generation and replace aging assets". However, there is also a greater focus now on lowering the cost of deliveringThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

With the penetration of non-linear loads, renewables and distributed generation with power electronic converters, solutions for maintaining good power quality have become a major concern for the stakeholders of electrical power systems. In this paper, a machine learning based model for power quality event classification is developed and tested. 16 categories of the most commonly occurring power quality events are classified by means of wavelet transform and select machine learning based methods to evaluate the best performing machine learning model. The outcome of classifications and effectiveness of machine learning methods is evaluated using the 'Classification Learners' application in MATLAB. The selected machine learning model is implemented in Simulink for test distribution grid circuits. The results obtained from simulation showed acceptable accuracy and performance and demonstrated the efficiency of the model in different operating conditions.

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