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Highlights • This paper presents the specific technical aspects associated with CVR and VAr management for an urban distribution system operation. • The focus is explicitly on VAr management during CVR, since it has not been addressed in the literature in greater detail. • In summary, it can be seen that there are possibilities for both positive and negative effects under each simulated stand-alone Volt/VAr control method. • It is revealed that a carefully designed Volt/VAr control strategy is required for successful implementation of CVR under high penetration of solar-PV. Abstract Modern distribution systems are mainly characterized by the behavior of non-linear consumer loads and their stochasticity, intermittency of renewable energy resources; and recurrent operation of Volt/VAr control devices. Conservation voltage reduction (CVR) has been adopted by most of the distribution utilities around the world as a viable technique for peak load shaving and long-term energy savings. Due to the diversified and inherent characteristics associated with the modern distribution systems with high levels of solar-PV penetration, achieving significant voltage reduction, high power factor, minimum number of tap changer operations and low power losses could be challenging. This paper has addressed the issue of VAr management in urban distribution systems while implementing CVR in the presence of highly intermittent solar-PV. Three conventional Volt/VAr control strategies were used to identify the specific technical aspects associated with CVR and VAr management in distribution systems in the presence of high solar-PV penetration. A case study on an urban distribution system with high penetration of solar-PV has been conducted in this paper. It is revealed that a carefully designed Volt/VAr control strategy is required for successful implementation of CVR under high penetration of solar-PV. [ABSTRACT FROM AUTHOR]

Abstract: The implementation of climate change mitigation strategies may significantly affect the current practices for electricity network operation. Increasing penetration of renewable energy generation technologies into electricity networks is one of the key mitigation strategies to achieve greenhouse gas emission reduction targets. Additional climate change mitigation strategies can also contribute to emission reduction thereby supplementing the renewable energy generation participation, which may be limited due to technical constraints of the network. In this paper, the penetration requirements for different renewable energy generation resources are assessed while concurrently examining other mitigation strategies to reduce overall emissions from electricity networks and meet requisite targets. The impacts of climate change mitigation strategies on the demand and generation mix are considered for facilitating the penetration of renewable generation. New climate change mitigation indices namely change in average demand, change in peak demand, generation flexibility and generation mix have been proposed to measure the level of emission reduction by incorporating different mitigation strategies. The marginal emissions associated with the individual generation technologies in the state of New South Wales (NSW) are modelled and the total emissions associated with the electricity grid of NSW are evaluated. [Copyright &y& Elsevier]

Highlights: [•] Difficulties in assessing distribution network adequacy with DG are addressed. [•] Indices are proposed to assess adequacy of energy supply and service continuity. [•] Analytical methodology is developed to assess the proposed indices. [•] Concept of joint probability distribution of demand and generation is applied. [ABSTRACT FROM AUTHOR]

Abstract: The time-varying demand and stochastic power generation from renewable distributed generating resources necessitate an exhaustive assessment of distribution feeder parameters for the purpose of long-term planning. This paper proposes a novel formulation of probabilistic load flow for distribution feeders with high penetration of renewable distributed generation. The dependency between the load demand of different consumer classes and generation from different types of renewable resources is addressed in this study. In order to capture the coincidental variations of demand and generation, associated time series data for the same time instances are used. A transformation matrix based probabilistic load flow is formulated using the method of cumulants. Moreover, Pearson distribution functions are used to estimate the probability distribution of the line flows. The proposed load flow method is tested on a practical distribution feeder with high penetration of solar photovoltaic and wind energy conversion systems. The results demonstrate the aptitude of the proposed method for conducting probabilistic load flow studies with dependent non-Gaussian distribution of load and generation. [Copyright &y& Elsevier]

Electricity generation using renewable energy generation technologies is one of the most practical alternatives for network planners in order to achieve national and international Greenhouse Gas (GHG) emission reduction targets. Renewable Distributed Generation (DG) based Hybrid Energy System (HES) is a sustainable solution for serving electricity demand with reduced GHG emissions. A multi-objective optimisation technique for minimising cost, GHG emissions and generation uncertainty has been proposed in this paper to design HES for sustainable power generation and distribution system planning while considering economic and environmental issues and uncertainty in power availability of renewable resources. Life cycle assessment has been carried out to estimate the global warming potential of the embodied GHG emissions from the electricity generation technologies. The uncertainty in the availability of renewable resources is modelled using the method of moments. A design procedure for building sustainable HES has been presented and the sensitivity analysis is conducted for determining the optimal solution set. [ABSTRACT FROM AUTHOR]

Selection of appropriate climatic variables for prediction of electricity demand is critical as it affects the accuracy of the prediction. Different climatic variables may have different impacts on the electricity demand due to the varying geographical conditions. This paper uses multicollinearity and backward elimination processes to select the most appropriate variables and develop a multiple regression model for monthly forecasting of electricity demand. The former process is employed to reduce the collinearity between the explanatory variables by excluding the predictor which has highly linear relationship with the other independent variables in the dataset. In the next step, involving backward elimination regression analysis, the variables with coefficients that have a low level of significance are removed. A case study has been reported in this paper by acquiring the data from the state of New South Wales, Australia. The data analyses have revealed that the climatic variables such as temperature, humidity, and rainy days predominantly affect the electricity demand of the state of New South Wales. A regression model for monthly forecasting of the electricity demand is developed using the climatic variables that are dominant. The model has been trained and validated using the time series data. The monthly forecasted demands obtained using the proposed model are found to be closely matched with the actual electricity demands highlighting the fact that the prediction errors are well within the acceptable limits. [ABSTRACT FROM AUTHOR]

Integration of solar photovoltaic systems to low-voltage distribution networks is witnessing an unprecedented growth in many parts of the world. Although solar photovoltaic generation is of significant benefit from a number of angles, exceedance of hosting capacity levels by such installations in low-voltage distribution networks continue to cause significant technical challenges in network operation, especially to the management of network voltage. Modern smart inverters are equipped with Volt-VAr and Volt-Watt control capabilities, which can assist in the management of network voltage levels. This paper provides a detailed analysis of the influence of different connection standards which cover these strategies on solar photovoltaic hosting capacity and their applicability in low-voltage distribution networks. Smart inverters with differing Volt-VAr and Volt-Watt control functions are modelled in the DIgSILENT PowerFactory platform. Influence of different connection standards on solar photovoltaic hosting capacity is analysed to investigate the most beneficial connection approach/es to address the issue of voltage violations. Furthermore, the work presented in this paper provides a greater understanding on the hosting capacity improvement by employing advanced inverter control functions where such improvements are subjected to locational aspects of inverters in low-voltage distribution systems. • Control strategies: Volt-VAr, Volt-Watt and combined Volt-VAr & Volt-Watt were assessed. • Combined Volt-VAr & Volt-Watt control is effective in enhancing hosting capacity. • Hawaii Rule 14 and IEEE 1547 are effective to increase solar PV hosting capacity. • Volt-VAr control is the most effective with bare conductors. • Volt-Watt control is the most effective with bundle cable type feeders. [ABSTRACT FROM AUTHOR]

Abstract: Global temperature rise due to climate change has become a major concern for the policy makers. Since rising temperature has significant impact on electricity demand, the expansion of the electricity network and related augmentation plan of power industries will be affected due to the change in climatic conditions. This paper proposes an analytical tool to examine the effect of climate change on the electricity demand of New South Wales, Australia. Multiple linear regression analysis has been applied for the historical climatic and non-climatic variables to establish a correlation between per capita electricity demand and associated key variables. Time series analysis is then carried out to predict future temperature and corresponding cooling and heating degree days of New South Wales. Using the proposed analytical tool, per capita electricity demand rise is estimated solely due to temperature change. The results obtained indicate that electricity demands in summer and spring will increase due to climate change. It is found that an increase in temperature alone may lead to 1.36%, 2.72% and 6.14% rise in per capita demand during the summer season and 2.09%, 4.5% and 11.3% rise in per capita demand rise during the spring session of 2030s, 2050s and 2100s respectively. [Copyright &y& Elsevier]

In the current context, climate change has become an unequivocal phenomenon. Although it primarily encompasses change in temperature, nevertheless other weather variables such as rainfall, wind speed, evaporation and humidity can also be affected as a result of climate change. Addressing the impacts of climate change on electricity demand is essential for predicting the future demand. For example, cooling and heating requirements change significantly with respect to climate change that may result to the change in electricity load demand. In this paper, a backward elimination based multiple regression approach is proposed for analyzing the influence of climatic variables on load forecasting. A correlation analysis has been carried out using Pearson’s correlation coefficient to examine the interdependency between different climatic variables in the context of Sydney, one of the most densely populated cities in Australia. Regression based analysis has been performed to examine the relationship between per capita electricity demand and associated climatic variables. ‘Degree Days’ concept has been utilized to determine balance point temperature. Backward elimination based multiple regression is used to exclude non-significant climatic variables and evaluate the sensitivity of significant variables related to the load demand. Average change in future per capita electricity demand has been predicted using the proposed approach for the city of Sydney, Australia. Results indicate that the demand for Sydney will increase by 6% by 2030. [ABSTRACT FROM AUTHOR]

• Deterministic approach for evaluation of solar PV hosting capacity subjected to over-voltage curtailment in LV networks. • Solar PV hosting capacity limits in an LV feeder or network based on deterministic studies. • Nomographic tool for feeder based solar PV hosting capacity assessment in LV networks. • Three stage solar PV connection criteria for LV distribution networks. Proliferation of solar photovoltaic (PV) generation in low voltage (LV) distribution networks has imposed a set of challenges in network operation and control. Voltage rise is currently the main constraint that limits solar PV capacity increase in LV networks. Together with this, there is a growing need for a generalised and versatile tool which utilities can use to deal with customer requests for new solar PV connections. This paper proposes a generalised approach to assess solar PV hosting capacity (HC) subjected to over-voltage curtailment based on a Nomogram representation, which facilitates reasonable modeling insights for HC assessment in LV networks. In addition, solar PV connection criteria are further developed using the Nomogram representation of HC evaluation. The proposed Nomogram based approach for HC assessment and connection criteria will contribute to further improvement of available guidelines on solar PV connections in LV networks. [ABSTRACT FROM AUTHOR]