During the last two decades, the use of residential photovoltaic (PV) systems has been widely promoted by governments through various support mechanisms such as feed‐in‐tariffs, net‐metering, net‐billing, etc. These support schemes have developed a secure investment environment, increasing the penetration level of PVs in low‐voltage distribution grids. Nonetheless, increased PV integration may introduce several technical problems regarding the secure operation of distribution grids. Battery energy storage (BES) systems can mitigate such challenges, but the high capital cost is one of the most important limiting factors towards the widespread use of these systems. In fact, the financial viability of integrated PV and BES systems under different support schemes remains an open issue. In this study, the profitability of PV and BES systems is evaluated through an advanced techno‐economic model, that provides the optimal size of PV‐BES system in terms of net present value, based on the electricity production and consumption profile of the installation, PV and BES systems costs, and electricity charges. The proposed model may be a useful tool for prosumers, grid operators and policymakers, to assess the impact of various incentive policy schemes and different BES operation strategies on the economic viability of PV‐BES systems.

The ever-increasing demand for electricity, the advent of microgrids and the increasing penetration of distributed generators has renewed the interest in dynamic equivalencing, due to to its importance on power system analysis and control applications. This paper introduces a robust measurement-based framework for the online derivation of dynamic equivalent models. First, events/disturbances suitable for the derivation of dynamic equivalent models are automatically detected. Next, signal processing techniques are applied to recover missing samples and to remove noisy components from measured data. To exclude unnecessary post-disturbance data, a fine-tuning technique of the signal window length is also proposed as a supplementary offline process. Finally, model parameters are estimated using nonlinear least-squares optimization. The performance of the proposed methodology is tested using artificially created signals, simulation results obtained from a modified benchmark distribution grid and measurements acquired from a laboratory-scale active distribution network.

The increasing penetration of distributed renewable energy sources drastically alters the dynamic characteristics of distribution networks (DNs). Therefore, several equivalent models have been recently proposed, to analyze more accurately the complex behavior of modern DNs. However, relatively simple models are still commonly used in practice for dynamic power system studies. In addition, dynamic equivalent models for DNs are sensitive to different operating conditions and there is lack of systematic understanding of their performance. Scope of this paper is to propose a methodology for identifying the applicability range in terms of accuracy and generalization capability of several conventional and newly developed equivalent models for the dynamic analysis of modern DNs. A set of metrics is used for the modelling accuracy assessment and a sensitivity analysis framework is introduced to fully quantify the generalization capabilities of DN equivalent models. Based on the above, guidelines and recommendations for the development of robust equivalent models for DN analysis are proposed.

To monitor network operation in real-time, power system operators have developed wide-area monitoring systems (WAMS). However, the centralized communication and information processing architecture of WAMS cannot be extended easily to distribution networks. In this aspect, a three-level distributed network monitoring architecture is proposed in this paper, concerning the dynamic analysis of transmission, primary and secondary distribution networks by exploiting measurements of ambient data and transient responses. In the proposed architecture, operators are responsible for the operation and analysis of their own grid but also can share an overview of the system performance to facilitate their operational coordination. Different online and offline applications are supported within the architecture, including small-signal, transient and frequency stability analysis as well as dynamic equivalencing and real-time inertia estimation. Measurement-based algorithms and models are proposed for each case. Finally, the performance of the developed algorithms has been tested by using a combined transmission and distribution power system model.