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Accurate photovoltaic (PV) power forecasting is an essential tool that enables the efficient integration of solar electricity and energy trading. This work proposes a novel methodology for the implementation of a forecasting tool that provides aggregated PV production day‐ahead forecasts for the PV systems installed at a distribution level. Specifically, the tool is a cloud‐based platform, comprising of a data quality block, a weather forecasting model, a machine learning power prediction step and an up‐scaling aggregation stage. In this context and in the absence of a fully observable distribution system, the aggregated PV generation was estimated using a clustering approach and up‐scaling the measured generation of reference systems to the aggregated installed capacity. The results demonstrated that the implemented tool exhibited high forecasting accuracies, lower than 10% given by the mean absolute percentage error when applied to the distribution system of Cyprus. Furthermore, the comparative benchmarking of the up‐scaling techniques against the estimated aggregated PV generation demonstrated that the best‐performing approach was the hybrid model, which provided a normalised root mean square error of 10.29% and mean absolute percentage error of 9.11%. Finally, useful information is provided for establishing a robust day‐ahead forecasting methodology that is based on an optimal supervised learning and hybrid up‐scaling approach.

The ongoing efforts by many countries worldwide to increase the share of renewable energy sources in power generation has changed the nature of existing power grids and introduced numerous stability-related issues. To address these problems, more extensive and detailed stability analysis studies are therefore needed. Driven by this need, in this article, we present a passivity-based framework for stability analysis and control design that allows more accurate modeling of both the network and the power system components while facilitating the derivation of completely decentralized stability results. In particular, the proposed approach relies on the formulation of the network as a dynamical multivariable system, which is shown to be passive, even when the network’s dynamic and lossy nature are taken into account. The application of decentralized passivity conditions on bus dynamics is then further exploited, together with the incorporation of more accurate dynamical models for the power system components, to guarantee the asymptotic stability of the interconnected system. Moreover, we discuss the opportunities provided by the proposed approach regarding the incorporation of higher order dynamics, the derivation of significant stability results in a completely decentralized manner and the design of effective distributed control mechanisms. These opportunities are finally verified through the design of a demand-side voltage droop controller and several dynamic simulations on two typical test systems.

Accurate photovoltaic (PV) energy yield forecasting is an essential and cost-effective tool for the efficient integration and management of the electricity grid and for solar energy trading. The scope of this paper is to present a novel methodology for the implementation of a solar PV energy yield forecasting tool that provides hourly-averaged day-ahead PV power generation forecasts, based on data-driven machine learning techniques and statistical upscaling and aggregation algorithms. More specifically, the implemented tool in the form of an online software platform, comprises of a data quality validation functional block, a data-driven power output model and an upscaling aggregation clustering stage. Finally, the accuracy of the implemented tool was validated against simulated actual PV generation estimates for the entire installed capacity PV systems at the distribution system of Cyprus. The tool demonstrated high forecasting performance accuracies by yielding normalised root mean square error (nRMSE) of 8.34% and a mean absolute percentage error (MAPE) of 6.93%, over an evaluation period of 90 days.

We present a method to design distributed generation and demand control schemes for primary frequency regulation in power networks that guarantee asymptotic stability and ensure fairness of allocation. We impose a passivity condition on net power supply variables and provide explicit steady state conditions on a general class of generation and demand control dynamics that ensure convergence of solutions to equilibria that solve an appropriately constructed network optimization problem. We also show that the inclusion of controllable demand results in a drop in steady state frequency deviations. We discuss how various classes of dynamics used in recent studies fit within our framework and show that this allows for less conservative stability and optimality conditions. We illustrate our results with simulations on the IEEE 68 bus system and observe that both static and dynamic demand response schemes that fit within our framework offer improved transient and steady state behavior compared with control of generation alone. The dynamic scheme is also seen to enhance the robustness of the system to time-delays.

The continuously increasing penetration of renewables in combination with the unbundling of electricity markets during the last years, introduced serious voltage-related issues across the existing Distribution Systems. In order to overcome these issues operators sought for new, more effective ways to support voltage across distribution feeders and thus to ensure the quality of the electricity supply. Driven by the aforementioned need, in this paper we introduce an effective, demand-side Volt/Var/Watt (VVW) regulation scheme that is able to provide significant voltage support and assist in maintaining acceptable voltage levels across distribution grids. In particular, by considering certain passivity conditions on bus (load) dynamics, we design a VVW control mechanism that is able to regulate the voltage of feeders through the utilization of the available controllable loads. The proposed mechanism can be used either in a centralized or decentralized fashion and its formulation is carried out taking into account both the lossy and the dynamic nature of the distribution network. Its effectiveness is verified through several dynamic simulations on the IEEE 37 Node Test Feeder when either VVW or VV control is employed.

The increased penetration of renewables in power system generation has introduced several stability issues to existing power systems. Their intermittent nature in combination with the lack of rotational inertia made system operation even more difficult and frequency/voltage fluctuations larger. Storage systems have been identified as an ideal solution for mitigating these problems since their integration across the grid can reduce generation-load imbalances and assist in primary frequency regulation. Considering the importance storage systems have gained during the last years, in this paper we propose an energy management algorithm for a grid-connected PV system with battery storage. This model contains a Building Integrated Photovoltaic (BIPV) system connected to the grid through a DC-DC boost converter, a DC-AC inverter and a battery storage system in active parallel configuration. Considering that the consumption of the building is satisfied from either the PV, the low voltage grid and/or the battery storage system, a specific energy management algorithm is presented for this model, in order to provide an efficient power flow between the aforementioned sources and the building load. The proposed model is implemented and verified through several simulations in Matlab/Simulink.

Due to an increase in converter-interfaced renewable generation, power networks are becoming increasingly reliant on power converters, which have contrasting dynamical behaviour to rotating machines with large inertia. This results in new control challenges for the power grid. We focus in this paper on decentralized conditions that can provide stability guarantees for networks with grid-forming converters. In particular, we consider the dynamics of various grid-forming converter configurations and controllers, and show how these can be designed to satisfy an appropriately formulated local passivity condition that provides stability guarantees for the network. Simulations confirm that designs satisfying this property improve the stability and dynamic response of the power network.

During the last decades, significant advances have been made in the area of power system stability and control. Nevertheless, when this analysis is carried out by means of decentralized conditions in a general network, it has been based on conservative assumptions such as the adoption of lossless networks. In the current paper, we present a novel approach for decentralized stability analysis and control of power grids through the transformation of both the network and the bus dynamics into the system reference frame. In particular, the aforementioned transformation allows us to formulate the network model as an input-output system that is shown to be passive even if the network's lossy nature is taken into account. We then introduce a broad class of bus dynamics that are viewed as multivariable input/output systems compatible with the network formulation, and appropriate passivity conditions are imposed on those that guarantee stability of the power network. We discuss the opportunities and advantages offered by this approach while explaining how this allows the inclusion of advanced models for both generation and power flows. Our analysis is verified through applications to the Two Area Kundur and the IEEE 68-bus test systems with both primary frequency and voltage regulation mechanisms included.

Over the last decades, several techniques have been proposed for the optimal placement of FACTS devices across power systems. Although these techniques were shown to improve power system operation, they are usually computationally intractable while having serious inherent limitations. In this paper, we present a novel approach to guide the SVC location identification in order to enhance power system stability. Specifically, the proposed method exploits findings in passivity-based control analysis and design in order to address the most vulnerable-in terms of passivity-buses of the system and consequently the optimal locations for SVC installation. We then show how the incorporation of SVCs at the aforementioned buses can passivate the system and provide guarantees for increased stability. Furthermore, we provide a brief discussion regarding the sizing and the number of required SVC devices in order to guarantee such stability improvement. Finally, we illustrate our results with simulations on the IEEE 68 bus system and show that both the dynamic response and the damping of the system are significantly improved.

Reliable and efficient control of bus frequencies is crucial to the operation of any power grid. Classically, frequency control is implemented on the generation side via primary and secondary control schemes, with issues of optimality addressed at a much slower timescale via the optimal power flow (OPF) problem. In recent years, power systems have undergone significant changes, such as the liberalisation of the electricity markets, the introduction of new generation technologies, and the increased penetration of renewable energy sources. This expansion ofpower systems along with the stochastic nature of renewable energy inevitably lead to a need for faster, more efficient, and more reliable frequency control mechanisms. Furthermore, in a smart grid paradigm, frequency control schemes can be highly distributed due to the participation of the demand-side. Such approaches, incorporating control on both generators and loads, have the potential to reduce operational costs, improve system security, and increase the overall economic efficiency of the network's operation. In this chapter, we will discuss various approaches to distributed frequency control, paying particular attention to the incorporation of demand-side management and to the economic optimality ofthe schemes. We begin by describing the key concepts that will be considered.

Aim of the current work is the experimental study of the electric field distribution around a medium voltage metal oxide gapless polymeric housing surge arrester, since the study and the knowledge of the electric field around an arrester can be useful for diagnostic tests and design procedures. The measurements were carried out, using two appropriate calibrated field meters. The results are accompanied with their total uncertainties, which were computed using the obtained measurements and data from the calibrations certificates.