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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.

Electricity networks are experiencing increasing congestion and reliability issues as current generation and transmission infrastructures endeavour to match the supply with demand. The integration of intermittent renewable energy resources such as photovoltaic (PV) systems has led to a large variation in energy production and increased supply uncertainty in power systems. In this context, demand-side management (DSM) schemes can be used to motivate prosumers to refine their energy behaviours by offering them various incentives. This study presents a universally-applied methodology that will promote the deployment of effective price-based DSM for residential prosumers. The proposed methodology can be applied on both prosumers and consumers since the utilization of the net-load profile was found to reduce the percentage of unintended revenues by 15%. Additionally, the effectiveness of the methodology was validated through a pilot-network of 300 residential prosumers with installed rooftop PV systems, and resulted in seasonally dependent peak consumption reduction in the range of 1.03% and 3.19% and a reduction of the overall consumption by approximately 2%. Finally, the conducted cost-benefit analysis demonstrated an overall net-benefit of €4.62mln, over a 15-year period, when considering assumptions that are linked to the costs and benefits of a nationwide deployment of the proposed DSM scheme.

A range of technologies must be developed and deployed for achieving a decarbonised energy system. A smart grid aims to integrate these different technologies into a single, over-arching system that is at the same time both complex and interoperable, which cannot be achieved without standardisation. Moreover, standardisation is a method of transferring research into successful processes and products. Without this, existing conventional energy systems will not change much longer, as it is more difficult to achieve significant market penetration of new technologies and deployment of new functions and applications. It seems that standardisation issues are not sufficiently addressed in scientific publications and are treated as a very specific topic by community of researchers despite the fact that standards may serve as a knowledge base for further research and improvement of emerging technologies and approaches. This paper presents a bird's-eye smart grid standardisation review based on a unique functionality - technology approach developed within Horizon 2020 project PANTERA.

The paper uncovers the barriers limiting wider involvement of lagging countries in Research and Innovation (R&I) activities in the smart grid domain. The data have been collected through a dedicated stakeholder survey. The analysis presented in the paper underlines the key challenging issues: proposal preparation and consortium building process, insufficient support, lack of information and bureaucracy. An attempt is made to identify pathways to overcome these barriers based on identified best practices in more advanced countries.

The distributed and stochastic nature of Renewable Power Sources is certainly forming considerable challenges for the operation of the power system. Specifically, the stability of the system can be jeopardized when the penetration of inverter-based systems is high. Storage and the proper design of controllers is seen as part of the solution for supporting the future expansion of distributed systems. Thus, control strategies need to be designed to provide the appropriate support to the system and be capable of keeping the variation of the frequency within limits to keep the reliability of the system as high as possible. The main challenge is the appropriate parameterization of these distributed controllers and their coordination under the integrated grid approach in securing the stability of the system at all times. In this paper, a smart energy controller is utilized and incorporated into the projection case study for Cyprus’ real distribution grid for the year 2050 to evaluate its behavior and identify possible weaknesses in its usage. It was found that the parameterization and not only the architecture of such controllers is crucial in coping with the frequency variation and stability problem. From the simulation work and recorded results, it was observed that the smart energy controllers can maintain frequency variation within the desirable range when the parametrization of the controllers is chosen appropriately. This specific observation highlights the need to evaluate and configure the smart controllers while operating in the field, and possibly further research is required to provide the advanced capability to such systems to adjust dynamically during field operation, thereby achieving better response during abnormal conditions.

The stochastic nature of renewable energy sources (RES) imposes great challenges to the emerging energy grid. RES uncertainties influence the impact of how distributed energy resources (DER) as emerging technologies will be scheduled in the short-term, to provide the available flexibilities as an important contribution to the balancing of the system at all times. This chapter describes the RES uncertainties and assesses how DER operating strategies can soften their effect on the grid. This chapter proposes an analytical approach to assess the uncertainties of active distribution networks and the effect to the short-term scheduling of operators. The same approach will also determine the reliability/resilience of the system under study as a function of the evaluated uncertainties as well as DER operating strategies and the flexibility that they can offer. The effectiveness of the presented assessment is validated using extensive numerical studies on a real grid of Cyprus with high RES penetration.

Along with the increasing share of non-synchronous power sources, the inertia of power systems is being reduced, which can give rise to frequency containment problems should an outage of a generator or a power infeed happen. Low system inertia is eventually unavoidable, thus power system operators need to be prepared for this condition. This paper addresses the problem of low inertia in the power system from two different perspectives. At a system level, it proposes an operation planning methodology, which utilises a combination of power flow and dynamic simulation for calculation of existing inertia and, if need be, synthetic inertia (SI) to fulfil the security criterion of adequate rate of change of frequency (RoCoF). On a device level, it introduces a new concept for active power controller, which can be applied virtually to any power source with sufficient response time to create synthetic inertia. The methodology is demonstrated for a 24 h planning period, for which it proves to be effective. The performance of SI controller activated in a battery energy storage system (BESS) is positively validated using a real-time digital simulator (RTDS). Both proposals can effectively contribute to facilitating the operation of low inertia power systems.

The electrical power network is gradually migrating from a centralized generation approach to a decentralized generation with high shares of renewable energy sources (RES). However, power systems with low shares of synchronous generation and consequently low total system inertia, are vulnerable to power imbalances. Such systems can experience frequency stability problems, such as high frequency excursions and higher rates of change of frequency even under small disturbances. This phenomenon is intensified when the grid under investigation has low or no interconnections (islanded) and thus the challenge for stable operation becomes more significant for the operators. This work focuses on how the frequency stability is affected when a photovoltaic (PV) inverter is integrated into a real non- interconnected distribution grid in Cyprus. In order to capture the realistic interactions of this integration, stability experiments in a hardware-in-the-loop (HIL) environment are performed with the aim to provide insightful results for the grid operator.

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.

Over the last few years, the increased electricity demand coupled with high penetration of intermittent Renewable Energy Sources (RES) and the advent of smart grid technologies have enhanced the role of demand side management in the efficient operation of the electricity network. The potential participation of end-users in the electricity markets has also highlighted the value of demand response (DR) in the power system through new market entities, called DR Aggregators. The DR Aggregators undertake the representation of a sum of domestic users, acting as third party intermediaries. This paper examines the current participation schemes of the DR Aggregators in European spot and balancing markets, following the concept of the Target Model. Furthermore, the case of a non-interconnected distribution grid, like the one encountered in the island of Cyprus, is examined. Opportunities and challenges of introducing the role of the DR Aggregator are explored, while the results are validated by demonstrating the DR Aggregator role in the case of the University of Cyprus (UCY) campus, acting as a microgrid.

Electrochemical storage technology and especially battery energy storage (BES) is emerging as one of the fastest growing storage technologies for grid-connected applications. It has an important role to play in the proposed energy transition offering suitable services for domestic and utility scale applications such as the balancing of the intermittent electricity generated by solar photovoltaic (PV) systems. On the other hand, barriers concerning security and financial compensation must be lifted before it becomes a reliable and profitable option. This paper outlines the primary targets of StoRES project towards eliminating the uncertainties related to battery storage. For this purpose, coupled PV-BES residential storage systems will be developed in five countries aiming at optimizing the utilization of storage towards achieving higher PV penetration. The process of selecting the pilot sites in each country is analysed highlighting the importance of the selection criteria. Another focus of this paper is the implementation of a community (social) storage system which will be installed only in Cyprus. This will be connected to the same distribution feeder supplying the local residential pilot systems in order to make a credible comparison between the centralized and distributed storage. In light of the above, a market research was performed in order to determine and compare the system configurations that will be applied for the pilot system design. Finally, the methodology for acquiring and monitoring the data from the pilot sites along with the expected outputs is outlined.

The capabilities offered by using Demand Response (DR) to aid the integration of photovoltaic (PV) systems into medium voltage (MV) distribution networks, are explored in this paper. The methodology followed focuses on the optimal usage scheduling of deferrable loads in order to coincide with forecasted PV production profiles by applying DR techniques on a day-ahead load profile. The obtained results highlight that the application of DR techniques can maintain the upper voltage level at 1.031 p.u. without violating the voltage regulation limits, while the lower level remained relatively unaffected. Furthermore, a significant reduction in active power levels, up to 5 MW, was observed, thus resulting in a feasible extension of the existing PV capacity.

I. IntroductionThe trend towards high penetration of renewable energy sources (RES) in the energy mix and particularly grid-connected photovoltaic (PV) systems in the low voltage (LV) network, offers the benefits of green decentralized generation, at the cost of the development of energy management tools to alleviate potential problems. More specifically, the fact that for most consumption profiles the PV energy production does not coincide with the electricity demand, forces the grid to act as a sink and a source thus requiring re-adaptation of the grid operation [1]. To this extent, an advanced demand side management (DSM) scheme can be introduced to mitigate RES operational issues and contribute to managing effectively congestion problems. In this work, a price-based DSM tool has been developed in order to arrive at an effective Time of Use (ToU) tariff with improved DSM results. In this scope, smart meters (SMs) have been deployed at three hundred households with grid-connected PV systems installed at their rooftops, in order to acquire consumption and production profile details of typical Cypriot prosumers. The selected pilot sites that already have a 3 kWp grid-connected PV system, have been offered a ToU tariff allowing rates and charges to vary based on the time and date of consumption, i.e. day or night and seasonality. This aims to examine how financial implications can alter the energy behavior of prosumers [2]. In addition, the prosumers are divided into three groups each receiving a distinct method of monitoring their energy habits: one group will have In-House Displays (IHDs), the other will have access through a web application, and the third will receive information about their energy habits through the traditional bi-monthly mail bill. This will facilitate an in-depth examination of the prosumers’ response based on the information sent to them [2].II. BackgroundTheory Price-based DSM programs offer an alternative to the traditional flat tariffs and comprise of Critical Peak Pricing (CPP), Real-Time Pricing (RTP) and Time-of-Use (ToU) tariffs [3], [4]. Amongst the different schemes, ToU tariffs are commonly preferred because the price of energy consumption is fixed for different periods of the day in contrast to other price-based DSM programs where the price fluctuates following the real time cost of electricity [3], [4]. The development of an efficient DSM system offers the advantage of generating cost reductions for grid utilities and the increase of operational efficiency. However, in order to achieve a balanced DSM scheme, the existing flat tariffs need to be transformed to ToU tariffs providing the necessary monetary incentives for domestic consumers to flatten their load profile. Even though, ToU tariff schemes offer the advantage of price certainty, the effectiveness of such tariff schemes must be verified prior to implementation because of the risk of a new peak appearing through load shifts at cheaper price periods, posing negative effects on the optimal operation of the system [5]–[8].III. Prosumer FeaturesIn support of this work, three hundred prosumers in Cyprus have been selected through the implementation of the SmartPV project (http://www.smartpvproject.eu/), in order to acquire real-time information of the consumption and production profiles and to identify the potential problems and limitations of the existing energy policy. All participating prosumers are geographically spread in Cyprus, in order to cover different socio-geographical conditions and thus targets a variety of consumers. In addition, prosumers with higher total yearly electricity consumption (in kWh) compared to the typical energy production from a 3 kWp grid-connected PV system were selected.IV. MethodologyThe development of a dynamic ToU tariff tool to enable price-based DSM relies strongly on the analysis of the basic input parameters such as electricity demand and PV electricity production profiles. Consumption and production data acquired from the three hundred prosumers was used to optimize the dynamic ToU tariff algorithm for the case of Cyprus. It has to be pointed out that this is a benchmark tool which can be used by any other country. The first step in the development of the algorithm was to identify the maxima and minima power consumption periods of the provided average domestic consumption profiles, an approach already implemented and verified [9]. The load duration curve of the provided average domestic consumption profile in Cyprus for each season was analyzed in order to identify possible inflection points. The different inflection points of the curve represent the various load segments which were used in order to obtain the probability density function (PDF) (at a 95% confidence interval) [1]. The PDF of each segment represented the ToU block period. The dynamic tariff tool developed from the above statistical analysis is capable of deriving the ToU blocks with a mean absolute percentage error (MAPE) and root mean square error (RMSE) between the ToU block periods and the load profile of 8.65% and 19.95%, respectively [1]. In order to further improve the initial algorithm, a function based model was developed. Optimization methods were used in combination with the statistical results [1]. In this approach the statistical output ToU block periods are used as the initial condition of the optimization procedure using the Matlab Optimization ToolboxTM. The ToU blocks are directly compared with the load profile rather than extracting the ToU blocks from the load duration curve. The objective function of the optimization procedure minimizes the RMSE as described by (1): where is the derived ToU block period, is the load profile and is the total sampling interval. Based on this equation, is the variable to be optimized and changes according to the desired levels. To achieve this, the developed optimization tool uses a hybrid optimization function such as simulated annealing [10] and pattern search [11]. In summary, two different methods were used to derive the final ToU block periods: a) combining statistical analysis using the load duration profile and b) optimization methods applied to the load profile.V. ResultsA. Dynamic ToU Tariff tool A software application tool was developed in order to assist users to visualize the impact on their electricity bill from the different ToU blocks. The derived ToU blocks for the winter period were compared using two different approaches (figure 1). The MAPE and RMSE between the load curve and the ToU blocks were improved by utilizing the optimization tool reducing them by 2.43% and 7.63%, respectively when compared to the statistical approach. Furthermore, the optimization approach clearly demonstrated that the peak consumption period is charged with the higher tariff, while the lowest tariff occurs during the valley period. Through this approach another period is clearly identified representing the transitional period: from the minima to the maxima and vice-versa. These time periods are important as they can be used by prosumers to cover their needs that can be shifted from the peak periods but cannot wait until the off-peak period (e.g. cooking, devices without smart control etc). B. Acquired data from three hundred prosumers Data-sets collected during the summer period from the three hundred prosumers equipped with Smart Meters (SMs), before the application of ToU tariffs, were analysed in order to evaluate the self-consumption index. Figure 2 presents the consumption and PV production profiles from the pilot sites. The evaluation of the self-consumption energy was calculated using the equations below [12] and the results are presented in Table I. The average self-consumption energy for the participating prosumers during the summer months is calculated to be 738.87 kWh hence, 53.52% of the energy produced is directly consumed on site while the remaining energy is exported to the grid. Comparing this with the typical prosumer of Cyprus, the self-consumption rate is more than 10% higher. This is mainly due to the fact that the SmartPV sample has on average a higher load demand when compared to the typical prosumer.VI. ConclusionsIn this work, the application of DSM schemes aiming at raising the awareness of the consumers, coupled with financial incentives has been demonstrated through dataset collection from 300 prosumers. In this domain, a new tool for evaluating the dynamic ToU tariffs has been developed based on two different methods, in order to promote effective price-based DSM practices in the electricity network of Cyprus. This is based on statistical analysis of the provided average consumption profiles and optimization procedures, aiming to derive the most appropriate ToU tariffs. The statistical method showed a MAPE and RMSE of 8.22% and 19.95%, respectively, by comparing the resulted ToU blocks to the load profile. On the other hand, the optimization method resulted in a MAPE and RMSE of 6.22% and 12.32% respectively, proving its effectiveness and improved accuracy. In addition, energy data-sets have been collected from participating prosumers before the implementation of the ToU tariff and comparisons between measured data and a typical prosumer was made. The results indicated an average self-consumption index with the existing net metering scheme for the summer months of about 53%.AcknowledgmentThe project is co-financed by the program LIFE (LIFE+ Environment Policy and Governance) of the European Union under the grant agreement number LIFE 12/ENV/CY/000276.References[1] N. Philippou, M. Hadjipanayi, G. Makrides, V. Efthymiou, and G. E. Georghiou, “Effective dynamic tariffs for price-based Demand Side Management with grid-connected PV systems,” in PowerTech, 2015 IEEE Eindhoven, 2015, p. 5.[2] B. Davito, H. Tai, and R. Uhlaner, “The smart grid and the promise of demand-side management,” 2010.[3] M. H. Albadi and E. F. El-Saadany, “A summary of demand response in electricity markets,” Electr. Power Syst. Res., vol. 78, no. 11, pp. 1989–1996, Nov. 2008.[4] B. Severin, M. Jaske, and A. Rosenfeld, “Dynamic Pricing, Advanced Metering, and Demand Response in Electricity Markets.” Center for the Study of Energy Markets, p. 103, 2002.[5] S. Gottwalt, W. Ketter, C. Block, J. Collins, and C. Weinhardt, “Demand side management—A simulation of household behavior under variable prices,” Energy Policy, vol. 39, no. 12, pp. 8163–8174, Dec. 2011.[6] J. Torriti, “Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy,” Energy, vol. 44, no. 1, pp. 576–583, Aug. 2012.[7] J. Thumim, “Investigating the potential impacts of Time of Use (TOU) tariffs on domestic electricity customers Report to Ofgem,” 2014.[8] C. M. Flath, “An optimization approach for the design of time-of-use rates,” in IECON - 39th Annual Conference of the IEEE Industrial Electronics Society, 2013, pp. 4727–4732.[9] Q. Hui-ting and L. Yi-jie, “Research of Peak and Valley Period Partition Approach on Statistics,” in 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 2011, pp. 1774–1779.[10] L. Ingber, “Adaptive simulated annealing (ASA): Lessons learned,” Invit. Pap. to a Spec. issue Polish J. Control Cybern. “Simulated Annealing Appl. to Comb. Optim., 1995.[11] T. G. Kolda, R. M. Lewis, and V. Torczon, “A generating set direct search augmented Lagrangian algorithm for optimization with a combination of general and linear constraints,” 2006.[12] O. Schaefer, S. Willborn, S. Goeke, J. A. Toledo, V. Cassagne, and A. Roesch, “Self Consumption of PV Electricity,” EPIA, 2013.

Overcoming local limitations in distribution grids with high photovoltaic (PV) penetration may be a challenging issue with non-optimal costs if appropriate planning strategies and regulations are not utilized. The installation of PV systems should be done according to local grid capability in order to avoid costly reinforcements. Careful consideration of the PV hosting capacity limits can help significantly in maintaining the secure and healthy operation of the electricity grid and can help avoid damages caused by violating thermal limits or by exceeding the voltage limits set by EN 50160 during times of high PV power production. In this paper, the methodology and approach followed to define the maximum PV capacity of a typical urban distribution grid is presented and analysed. The application of alternative measures at distribution grid level such as the local voltage support via reactive power regulation is investigated in finding optimal operating conditions for increasing PV capacity. It has been demonstrated that the urban distribution grid under consideration can accommodate a PV hosting capacity that is comparable to the firm capacity (the allowed operating capacity of the substation by adapting the requirement of the Grid Rules for one contingency) of the transmission substation.

In recent years, increasing concerns about climate change and the liberalisation of the energy market have provided the necessary impetus for a revolutionary restructuring of the electricity network. Traditional power networks are designed to operate in a passive and unidirectional way as their main functionality encompasses the transfer of energy from the power stations to the customer, with minimum loss. Increased electricity production from renewable energy sources (RES) coupled with energy efficiency lie at the heart of the ambitious targets set by Europe and globally in the quest to curb greenhouse gas emissions and to reach energy sustainability. As a result, complete restructuring of the electricity networks will have to take place in order to accommodate increased penetration of RES and distributed generation (DG) which is associated with electricity production from RES. Especially in regions with high solar irradiance, the penetration of photovoltaic (PV) systems is expected to increase in the near future as the technology becomes more competitive. High penetration of PV systems will definitely have serious consequences on the operation of the electricity grid and further challenges will arise as penetration levels increase. Thereafter the security and stability of the power system should be considered carefully to identify possible impacts due to uncontrolled deployment. The occurrence of power quality problems is not only negatively affecting utility customers but is also affecting the generated energy from Photovoltaic (PV) systems and the stability of the power system. The severity and frequency of occurrence of power quality issues can be the result of distribution grid topology/dynamics, arising due to high PV system concentration or even abnormal PV system operation. Analytical tools and accurate models of PV systems must be developed in order to evaluate their behaviour in the context of the full network. The utilization of accurate simulation models is of great importance in an attempt to assess the real consequences of localized energy production from distributed energy sources and in particular PV.A common detailed PV system model is formed by a PV array, an inverter and a power grid interface as shown in Fig. 1. Figure 1: General schematic of a Detailed Photovoltaic System.The PV array is affected by the solar irradiance, the temperature and the specific characteristics of the chosen PV module technology. The PV array converts the solar irradiance into DC power which is then delivered to the distribution grid via the PV inverter. A Maximum Power Point Tracking (MPPT) Controller is used to absorb the maximum available energy. The MPPT Controller varies the duty cycle of a DC/DC converter to adjust the voltage at the output of the PV array (DC link A). The DC/DC converter is connected to a DC link (DC link B) of which the voltage is maintained constant by a DC/AC inverter circuit topology. In more detail, the DC/AC inverter is set to inject the power reaching the DC link B into the electricity grid and in that way the DC link B remains constant. A filter is always placed at the output of the inverter to eliminate undesirable harmonic currents produced by the switching operation of the inverter [1].A generic PV system model for transient studies, the parameters of which can be tuned using transient data is developed in this work. The adopted analysis utilizes existing knowledge to formulate an accurate transient representation that considers the PV system control circuit and dynamics [2], [3]. The model is tuned and validated using transient data obtained from a detailed PV system circuit topology developed in Matlab Simulink via the Nelder-Mead simplex algorithm [4]. Its abstract representation is shown in Fig. 2. The proposed model is actually a three phase representation capable of simulating with sufficient accuracy normal/unbalanced operating conditions and voltage regulation. Harmonics are also incorporated into the model to reveal its capability for use in complete power quality studies. Figure 2: Proposed Transient Photovoltaic System Model (TPVSM).This PV system model is able to characterize the transient behaviour of PV systems in a generic way via a parameter estimation process. In addition, it enables the analysis of more aspects of power quality and voltage stability with higher accuracy under balanced and unbalanced conditions. It must also be pointed out that the proposed model is simpler and faster, thus allowing the computationally efficient simulation of complex problems. Figure 3: Output current response to a step change in active power input.The transient response of both the detailed and the proposed PV system model during a step change in active power input is shown in Fig. 3 (the reactive power input is kept at a zero value). In the next step, the developed model is used to assess the voltage transient response of a distribution grid busbar (point of common coupling of the PV system with the electricity grid) and the results are shown in Fig. 4.The comparison is made with the “Theil inequality coefficient”. The specific inequality coefficient provides a measure of how well a time series of observed values compares to a corresponding time series of estimated values. A value of 0 indicates zero difference or perfect predictions, whereas a value of 1 indicates poor model performance. Values lower than 0.3 depict good agreement between estimated and observed data. As can be seen from the results and inequality coefficient in Figs. 3 and 4, good agreement has been obtained between the detailed and the proposed PV system model. It is important to stress that the proposed generic model can been tuned by using experimental data as well. Figure 4: Transient reponse of voltage during a step change in reactive power reference.In summary, the proposed generic model is in line with current DG standards as it can be used for studies of voltage regulation/power quality. The aim of the aforementioned research is to enhance the effort of assessing the consequences of high PV penetration and facilitate corrective action with appropriate technical solutions so as to enable the safe and unrestricted deployment of these technologies in electricity grids [5].References[1] S.-K. Kim, J.-H. Jeon, C.-H. Cho, E.-S. Kim, and J.-B. Ahn, “Modeling and simulation of a grid-connected PV generation system for electromagnetic transient analysis,” Sol. Energy, vol. 83, no. 5, pp. 664–678, May 2009.[2] M. Liang and T. Q. Zheng, “Synchronous PI control for three-phase grid-connected photovoltaic inverter,” in 2010 Chinese Control and Decision Conference, CCDC 2010, 2010, no. 2, pp. 2302–2307.[3] Z. Xueguang, X. Dianguo, and L. Weiwei, “A novel PLL design method applied to grid fault condition,” in Conference Proceedings-IEEE Applied Power Electronics Conference and Exposition-APEC, 2008, pp. 2016–2020.[4] W. Bao, X. Zhang, and L. Zhao, “Parameter estimation method based on parameter function surface,” Sci. China Technol. Sci., vol. 56, no. 6, pp. 1485–1498, 2013.[5] M. Patsalides, A. Stavrou, V. Efthymiou, and G. E. Georghiou, “A Generic Transient PV System Model for use in Power Quality Studies,” Renew. Energy, 2015, Accepted.

31st European Photovoltaic Solar Energy Conference and Exhibition; 2945-2951, Integration in the power system is becoming a limiting factor to the further development of photovoltaics. Proper quantification is needed to evaluate both issues and solutions; the share of annual electricity demand is widely used but we found that some of the metrics which are related to power rather than energy better reflect the impact on networks. Barriers to wider deployment of PV into power grids can be split between local technical issues (voltage levels, harmonics distortion, reverse power flows and transformer loading) and system-wide issues (intermittency, reduction of system resilience). Many of the technical solutions to these issues rely on the inverters as actuators (e.g., for control of active and reactive power) or as interfaces (e.g., for local storage). This role requires further technical standardisation and needs to be taken into account in the planning of power networks. Forecasting, storage, and combination with other renewable sources are interdependent solutions to solve the intermittency issue. Finally, we found that PV is also an opportunity to reduce some investment required to upgrade existing power networks. Through integration with micro-grids and hybrid generators, it can form the basis of novel power systems.