Electricity power systems worldwide have traditionally been designed to a vertically connected scheme characterised by centralised generation. Over the last few decades, several factors have dictated a gradual shift from the central-control approach to a more distributed layout where distributed generation (DG) technologies are effectively integrated and not just connected (appended) to the networks; amongst others liberalisation of electricity markets, security and quality of supply and environmental issues. Photovoltaic powered distributed generation (PV-DG), although still having a much lesser impact than other DG technologies, is increasingly being embedded into electricity distribution networks worldwide within the framework of successful regulatory state and marketing programmes. PV-DG has added values (benefits) for the electricity systems that extend from peak power and load reduction (when deployed close to electricity consumption points) to participation in grid-supporting or grid-forming modes of operation. The question arises as to what the present situation of PV technology is for its optimal integration in distribution networks, whether there are still technical barriers to overcome as well as new opportunities for PV in future renewably supplied electricity systems. This paper presents the current state of knowledge concerning these topics from a European perspective with regard to different grid structures. It also discusses existing standards, new opportunities to provide grid services and research and development needs identified to fully exploit the added-value-and still developing-benefits of PV-DG.

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Abstract-Due the many uncertainties present in the evolution of loads and distributed generation, the use of probabilistic load flow in low voltage (LV) networks is essential for the evaluation of the robustness of these networks from a planning perspective. The main challenge with the assessment of LV-networks is the sheer number of networks which need to be analysed. Moreover, most loads in the LV-network have a volatile nature and are hard to approximate using conventional probability distributions. This can be overcome by the use of a Gaussian mixture distribution in load modelling. Taking advantage of its radial nature and high R/X ratios, the LV-network can be analysed more efficiently from a computation viewpoint. By the application of simplifications defined in this paper, the backwards-forwards load flow can be solved analytically. This allows for the direct computation of the load flow equations with a Gaussian mixture distribution as load. When using this new approach, the required calculation time for small networks can be decreased to 3% of the time it takes to generate a similar accuracy with a Monte Carlo approach. The practical application of this load flow calculation method is illustrated with a case study on PV penetration.

This paper presents a demand response (DR) framework that intertwines thermodynamic building models with a genetic algorithm (GA)-based optimization method. The framework optimizes heating/cooling schedules of end-users inside a business park microgrid with local distributed generation from renewable energy sources (DG-RES) based on two separate objectives: net load minimization and electricity cost minimization. DG-RES is treated as a curtailable resource in anticipation of future scenarios where the infeed of DG-RES to the regional distribution network could be limited. We test the DR framework with a case study of a refrigerated warehouse and an office building located in a business park with local PV generation. Results show the technical potential of the DR framework in harnessing the flexibility of the thermal masses from end-user sites in order to: (1) reduce the energy exchange at the point of connection; (2) reduce the cost of electricity for the microgrid end-users; and (3) increase the local utilization of DG-RES in cases where DG-RES exports to the grid are restricted. The results of this work can aid end-users and distribution network operators to reduce energy costs and energy consumption.

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