Photovoltaic Hosting Capacity Research Articles

Photovoltaic hosting capacity assessment of a distributed network based on an improved holomorphic embedding load flow method and stochastic scenario simulation

The increasing proportion of distributed energy in the distribution network poses a significant challenge to effectively absorbing distributed generation (DG). On this premise, the DG hosting capacity (HC) assessment in the future distribution network based on the secure operation boundary of the power grid is necessary. In this paper, an improved holomorphic embedding load flow method (HELM) is employed to construct the relationship between the photovoltaic HC (PVHC) and each constraint to determine the upper limit of the comprehensive PVHC. Furthermore, a screening method of PV locations in the distribution network is constructed to screen the alternative PV locations with strong voltage regulation ability and reduce the alternative multi-point schemes, paving the way for simplifying the PVHC assessment based on a stochastic scenario simulation. Finally, a PVHC assessment based on the stochastic scenario simulation is proposed to expand the applicability of the assessment based on improved HELM. The negative impact of PV uncertainty is quantified by generating stochastic load flow scenarios based on the upper limit of the PVHC from improved HELM. The PVHC assessment in the high-voltage distribution network IEEE30 is analyzed to prove the efficiency and comprehensiveness of PVHC assessment in the distribution network.

Allocation and smart inverter setting of ground-mounted photovoltaic power plants for the maximization of hosting capacity in distribution networks

As the integration of solar photovoltaic (PV) power plants into distribution networks grows, quantifying the amount of PV power that distribution networks can host without harmfully impacting power quality becomes critical. This work aims to determine the best number, location, and size of PV systems to be installed on a distribution feeder, as well as the best control set-points of the PV inverters, to maximize the PV hosting capacity (HC). Therefore, a simulation-optimization framework is proposed for siting and sizing ground-mounted PV power plants equipped with smart inverters (SIs). Single (decentralized) and multiple (distributed) allocations are analyzed by considering the connection of one, two, and three PV systems. Genetic algorithm (GA) and particle swarm optimization (PSO) metaheuristics are employed to solve the optimization problem. The simulation-optimization framework is tested on a real-world feeder model from an Ecuadorian utility. Installing two PV systems with their SIs operating with the Volt-VAr control function yields maximum PV HC, which is increased by 32.1 % compared to a single PV power plant operating at a unity power factor. Moreover, a comparative analysis of the two metaheuristic algorithms reveals that the PSO method provides better results than GA.

On the impact of tidal generation and energy storage integration in PV-rich electric distribution systems

Deep decarbonization of power system operations requires the maximal utilization of available renewable resources. At distribution-level operations, however, grid operators can face numerous challenges in integrating renewables at scale owing to the inherent intermittence of renewable energy resources. These include phenomena such as voltage fluctuations, which are typically mitigated through control actuators such as on-load tap changers (OLTC) as well as energy storage devices, such as battery energy storage systems (BESS). On the one hand, high intermittence of the available renewable portfolio may require increasingly aggressive control of actuators, thereby accelerating the probability of equipment failure. On the other hand, integrating BESS operations and having a diverse renewable generation portfolio can typically help stagger power/energy flow to mitigate the aforementioned adverse impacts. In this paper, we employ a Bayesian framework for equipment lifetime estimation to understand the impact of including tidal energy resources and BESS in distribution system operations for feeders having substantial distribution photovoltaic generation. Our results indicate that while tidal energy alone may slightly decrease equipment reliability, the adverse impact on reliability is significantly magnified by a generation portfolio consisting of tidal generation and photovoltaic generation. We also study the tidal and photovoltaic hosting capacity problem with and without energy storage systems using equipment reliability as an added constraint. We conclude that energy storage increases the reliability-constrained hosting capacity of the distribution system.

SVR Control Method Adapted to Three‐Phase Unbalanced Voltage in Distribution Systems with Photovoltaics and Electric Vehicles

This paper proposes a control method for step voltage regulators (SVRs) to avoid voltage violations in distribution systems with unbalanced voltages due to photovoltaic (PV) generation and electric vehicle (EV) charging. Distribution system operators must effectively address voltage problems resulting from the widespread installation of distributed energy resources under the optimizing the utilization of existing voltage regulators to establish an efficient and rational infrastructure. Therefore, this study focused on advancements in voltage control using SVR based on line‐drop compensator (LDC) control, which is widely used in actual operations. We propose a method that utilizes voltage data from information technology switches to detect and address voltage unbalances in the control section, allowing for the sequential adjustment of the monitoring target in LDC control to maintain appropriate voltage limits under unbalanced conditions. We conducted power flow calculations using a distribution system model that represents an actual distribution system in the Hokuriku area. The effectiveness of the proposed method was evaluated in terms of its impact on PV and EV hosting capacity, as well as the frequency of tap operations. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Development of PV hosting-capacity prediction method based on Markov Chain for high PV penetration with utility-scale battery storage on low-voltage grid

ABSTRACT The previous stochastic hosting capacity prediction method using the Monte Carlo method for high photovoltaic (PV) penetration with a battery energy storage system (BESS) required a large number of computations to achieve the expected accuracy. The problem of high computational load must be addressed so that the electrical distribution planner can practically use the PV hosting capacity prediction in actual situations. Therefore, this study developed a Markov-chain-based PV hosting capacity prediction method for high PV penetration using BESS. The proposed method is described in detail, followed by case and validation studies. The obtained hosting capacity was 123.58 kW, which increased to 3676.4 kW after the utility-scale BESS implementation. The results demonstrate that the proposed Markov-chain-based PV hosting capacity prediction method outperforms the Monte Carlo method, which is the most popular stochastic hosting capacity method, in terms of accuracy and computational cost.

Hosting capacity improvement for solar systems based on model predictive controller of volt-watt-var smart inverter functions

ABSTRACT Photovoltaic (PV) system adoption is accelerating quickly, raising worries among electricity network service providers about how to accommodate more PV while keeping system operating indices within acceptable bounds. This idea, which is typically about Hosting Capacity (HC), has recently gotten more interest. Modern smart inverters provide control possibilities in Volt-Var and Volt-Watt based on system voltage level management. In this paper, the impact of various connection standards that encompass these tactics on the capacity of solar PV systems to store energy and their usability in the delivery of low-voltage systems is thoroughly examined. Modeling of smart inverters has been validated using MATLAB SIMULINK with various Volt-Var and Volt-Watt control functions. Volt-Watt-Var control has been designed based on Model Predictive Control (MPC) and comparing the results with a PID controller. The proposed MPC control has been applied on the IEEE-9 bus with combined controls. The impact of various connection requirements on photovoltaic Hosting Capacity (PVHC) is investigated to determine the best connection strategy(s) to handle the problem of voltage encroachment. In addition, the paper gives a deeper knowledge of HC enhancement using sophisticated inverter control features within a low-voltage distribution network of converters, besides it shows the effects of over-voltage and under-voltage on the system parameters.

Photovoltaic hosting capacity improvement based on the economic comparison between curtailment and network upgrade

AbstractThe upcoming network investment decisions and regulatory framework adopted by distribution system operators (DSOs) are most likely to be impacted by the integration of fluctuating distributed generation. The economical hosting capacity (HC) improvement method is investigated in this paper as a trade‐off between curtailment and upgrade using a Monte Carlo simulation procedure. The associated costs of both methods are vital indicators for network operators that are trying to maximize the HC and minimize cost. In addition, the breakeven point where curtailment and upgrade costs intersect is the decisive point at which network upgrade becomes sensible as marginal curtailment cost exceeds upgrade cost. A shift in global climate conditions can impact the photovoltaic (PV) levels that motivate network operators to investigate PV penetration, especially in colder climate regions. Thus, the primary objective of this paper is to investigate the shift of the breakeven point to guide DSOs to either adopt PV curtailment as a temporary measure or grid upgrade as a risk aversion strategy considering Finnish climate and load patterns. The real‐time load and PV generation data are utilized for the simulations to consider the dynamic performance indication of three Finnish distribution networks. Curtailment remains a low‐cost option to obtain a percentage HC rise of 13%, 7%, and 8% for rural, suburban, and urban regions, respectively, beyond which curtailment compensation cost surpasses upgrade cost. In essence, PV curtailment serves as an immediate and least‐cost solution to relieve network violations and defer network investment until the HC level (118%, 106%, 97%) making the upgrade a practical option afterwards.

An Efficient Hybrid Particle Swarm and Gradient Descent Method for the Estimation of the Hosting Capacity of Photovoltaics by Distribution Networks

With many distribution networks adopting photovoltaic (PV) generation systems in their networks, there is a significant risk of over-voltages, reverse power flow, line congestion, and increased harmonics. Therefore, there is a need to estimate the amount of PV that can be injected into the distribution network without pushing the network towards these threats. The largest amount of PV a distribution system can accommodate is the PV hosting capacity (PVHC). The paper proposes an efficient method for estimating the PVHC of distribution networks that combines particle swarm optimization (PSO) and the gradient descent algorithm (GD). PSO has a powerful exploration of the solution space but poor exploitation of the local search. On the other hand, GD has great exploitation of local search to obtain local optima but needs better global search capabilities. The proposed method aims to harness the advantages of both PSO and GD while alleviating the ills of each. The numerical case studies show that the proposed method is more efficient, stable, and superior to the other meta-heuristic approaches.

Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review

The increasing demand for electricity and the need for environmentally friendly transportation systems has resulted in the proliferation of solar photovoltaic (PV) generators and electric vehicle (EV) charging within the low voltage (LV) distribution network. This high penetration of PV and EV charging can cause power quality challenges, hence the need for hosting capacity (HC) studies to estimate the maximum allowable connections. Although studies and reviews are abundant on the HC of PV and EV charging available in the literature, there is a lack of reviews on HC studies that cover both PV and EVs together. This paper fills this research gap by providing a detailed review of five commonly used methods for quantifying HC including deterministic, time series, stochastic, optimization, and streamlined methods. This paper comprehensively reviews the HC concept, methods, and tools, covering both PV and EV charging based on a survey of state-of-the-art literature published within the last five years (2017–2022). Voltage magnitude, thermal limit, and loading of lines, cables, and transformers are the main performance indices considered in most HC studies.

Does the current electricity grid support a just energy transition? Exploring social and economic dimensions of grid capacity for residential solar photovoltaic in Sweden

Through the Just Energy Transition in the Green New Deal, the European Union has emphasized the importance of inequality related to the energy transition. Yet, inequalities related to the hosting capacity for residential solar PV has gotten limited attention. Using estimations of hosting capacity for residential solar PV we analyze how residential solar PV hosting capacity is distributed based on social and economic status in Sweden. We find that solar photovoltaic hosting capacity is not equally distributed and that households with a high socio-economic burden tend to have less available hosting capacity for residential solar PV than households with a low socio-economic burden. A distribution of solar photovoltaic systems that best utilizes the existing grid would therefore on average be unfavorable for households with a high socio-economically burden. These findings could present a barrier for a Just energy transition towards a climate neutral energy system, and lead to increased inequalities in cost and access to renewable energy technologies, especially for countries with lower hosting capacity levels. To reduce potential inequality in grid access, we propose that policies either support the adoption of many small solar PV systems to avoid hosting capacity issues, or support households that currently lacks physical access to the residential solar PV market by allowing investments in solar PV systems at another property.

Simulation-based optimization framework to increase distribution system photovoltaic hosting capacity through optimal settings of smart inverter Volt-VAr control function

Simulation-based optimization framework to increase distribution system photovoltaic hosting capacity through optimal settings of smart inverter Volt-VAr control function

On the properties of residential rooftop azimuth and tilt uncertainties for photovoltaic power generation modeling and hosting capacity analysis

One of the essential epistemic uncertainties that has not yet been studied enough for distributed photovoltaic systems is the azimuth and tilt of rooftop photovoltaic panels, as previous studies of grid impacts and hosting capacity have tended to assume uniform and optimal roof facet conditions. In this study, rooftop facet orientation distributions are presented and analyzed for all single-family buildings in the Swedish city of Uppsala, based on LiDAR-based data that consist of every roof facet from the around 13,500 single-family buildings in the city. From these distributions, novel methods to proportionally include less suitable roofs for every penetration level are proposed using a simple method based on normal and uniform probability density functions, and are tested for both time-series and stochastic hosting capacity analysis. The results show that under the assumption that the best roof facets are utilized first, a uniform distribution for rooftop facet azimuth and a normal distribution for rooftop facet tilt with parameters that depend linearly on the penetration level were shown to be accurate. The hosting capacity simulations demonstrate how the proposed methods perform significantly better in estimating the photovoltaic hosting capacity than the more common simplified methods for both time-series and stochastic hosting capacity analysis. The proposed model could help distribution system operators as well as researchers in this area to model the rooftop facet orientation uncertainty better and improve the quality of aggregated photovoltaic generation models and hosting capacity analyses.

Stochastic Approach for Increasing the PV Hosting Capacity of a Low-Voltage Distribution Network

In recent years, the emerging fear of an energy crisis in central Europe has caused an increased demand for distributed energy resources (DER), especially small photovoltaic rooftop installations up to 10 kWp. From a technical point of view, distributed PV in low-voltage networks is associated with the risk of power quality violation, overvoltage, voltage unbalance, harmonics, and violation of the thermal limit of phase conductors, neutral conductors, and transformers. Distribution system operators (DSO) are currently in a position to determine the amount of installed PV power for which reliable and safe network operation is ensured, also known as the photovoltaic hosting capacity (PVHC). The presented study describes a stochastic methodology for PVHC estimation and uses it to analyze a typical LV rural network in the Slovak Republic. Detailed and precise calculations are performed on the 4-wire LV model with accurate results. In this study, we, thus, profoundly analyze the problems with voltage violation, unbalanced voltage energy losses, and the thermal loading effect of increasing PV penetration. The results show that overvoltage events are the main factor limiting the PVHC in LV systems. This conclusion is in accordance with the experience of the DSO in the Slovak and Czech Republic. Subsequently, the study focuses on the possibilities of increasing PVHC using those tools typically available for DSO, such as changes in PV inverter power factors and no-load tap changer transformers. The results are compared with those derived from similar analyses, but we ultimately find that the proposed solution is problematic due to the high variability of approaches and boundary conditions. In conclusion, the paper discusses the issue of the acceptable risk of overvoltage violation in the context of PVHC and lowering losses in LV networks.

A flexible stochastic PV hosting capacity framework considering network over-voltage tolerance

Integration of PV resources in distribution networks has become a world-wide trend. This has been necessitated by the ever-decreasing photovoltaic (PV) technology cost and the need for clean, carbon-emission-free power generation. Furthermore, the current situation which has resulted in high oil and gas prices has paved extra way for more integration of renewable energy (REs) technologies such as solar PV and wind. However, high proliferation of PV power has several negative effects for the grid. For example, there is a high likelihood of over-voltage occurrences in the network, potential reverse power flow (which can affect protection system operation) and line congestion which may lead to thermal capitulation of conductors and cables. It is, therefore, important to establish the limit of PV that can be injected in the network without stretching the system operating performance indicators into extreme violation, here-in called the PV hosting capacity (PVHC). PV hosting capacity is estimated either by deterministic means or by stochastic methods. Deterministic methods are very good at obtaining the siting and optimal sizing of mega PV plants. The weakness of deterministic methods is that they do not incorporate uncertainty in the load demand nor the uncertainty in the PV output. Thus, their models are mostly unrealistic. The stochastic methods are very good at incorporating uncertainties and model the system input random variables in a more realistic way. However, stochastic methods also suffer from unrealistically too many scenarios to be considered for an accurate analysis. This results in a huge computational burden and large memory requirement. Furthermore, in most of these estimations, the voltage limit is set as a hard constraint for estimating the PVHC. In this paper, we propose a 2-stage method employing both the deterministic and the stochastic approaches. The deterministic stage is used to obtain the optimum PV plant locations and the optimum sizing ratios. The stochastic stage is used for incorporating uncertainty in the input random variables. We also propose the use of probabilistic voltage violation framework to explore extra PV installation for slight voltage violation tolerant feeders or networks. This is then used to obtain output probabilistic maximum node voltages for different PV sizes, which are used to estimate the PV hosting capacity of a network. The efficacy and validity of the approach is demonstrated through numerical simulations conducted on IEEE test distribution networks.

Analyzing the Impact of EV and BESS Deployment on PV Hosting Capacity of Distribution Networks

The current article analyzes the impact of charging electric vehicles and battery energy storage systems on the photovoltaic hosting capacity of low-voltage distribution networks. A Monte Carlo-based simulation is used to analyze predominantly rural, intermediate and predominantly urban residential regions facing different penetrations of electric vehicles utilizing uncontrolled and controlled charging, and evaluate their impact on photovoltaic hosting capacity. Subsequently, electric vehicles are replaced or supplemented by residential battery energy storage systems, and their combined impact on the hosting capacity is studied. The results revealed that electric vehicles solely do not improve the hosting capacity unless they are connected to the network during sunshine hours. However, controlled storage provides a remarkable increase to the hosting capacity and exceptional contribution in combination with electric vehicles and customers with high loads. Finally, a feasibility analysis showed that controlled charging of the storage has a lower marginal cost of increasing hosting capacity as compared to network reinforcement.

Recent investigations on the evaluation of solar PV hosting capacity in LV distribution networks constrained by voltage rise

Evaluation of the maximum connected solar photovoltaic (PV) capacity without violating stipulated network voltage limits is essential in managing the voltage rise in low-voltage (LV) networks under increasing solar penetration levels. In this regard, this paper reviews recent investigations completed in relation to solar PV hosting capacity (HC) assessment work in LV networks. A feeder based approach developed for evaluating solar PV HC constrained by over-voltage conditions in LV networks is summarised. Further, a novel nomographic tool developed for HC calculation based on the influential factors on HC as identified by solar PV location, feeder length/loading levels, conductor type and stipulated voltage limits, is reviewed. In addition, a three-stage solar PV connection criteria which was developed based on the rigorous outcomes of nomogram and the feeder based evaluation approach is presented in the paper. The HC assessment and solar PV connection criteria presented in this paper would be a contribution to further improvement of the available utility guidelines/standards on solar PV installations in LV networks.

Possibilistic evaluation of photovoltaic hosting capacity on distribution networks under uncertain environment

The large-scale access of distributed photovoltaic (PV) in distribution networks (DNs), if not properly evaluated, brings several problems. The uncertainties with PV outputs and load demand are nonnegligible in PV hosting capacity (PVHC) evaluation. However, not all DNs have sufficient historical data or smart meters to support the accurate probability uncertainty model. This paper proposes a α-Cut-based possibilistic PVHC evaluation method that does not rely on probability density functions. First, to characterize exogenous and endogenous uncertainty, the interval model is applied to model the PV outputs, whereas the membership function is used to model the load demand. Second, the definition of system violation risk (SVR) is introduced to quantify the risk caused by uncertainties. Then, the possibilistic PVHC evaluation method considering SVR is proposed. The test on the IEEE 34-bus system illustrates the effectiveness of the proposed method. The PVHC result is 2–10% of penetration higher than over-conservative results obtained by the traditional approach due to the quantification of uncertainties and the introduction of violation risk. Third, the defuzzification of fuzzy results is discussed, followed by a comparison between the function of crisp value and fuzzy number in the actual decision-making of utilities. The sensitivity of simulation parameters and circuit characteristics is analyzed. Finally, the proposed method is proven on the IEEE 123-bus DN to validate its adaptability to a more extensive system.

Combined PV-PEV Hosting Capacity Analysis in Low-Voltage Distribution Networks

The increasing penetration of low and zero carbon (LZC) technologies, such as residential-scale photovoltaic (PV) systems and plug-in electric vehicles (PEV) in low-voltage (LV) distribution networks, can become highly problematic for utilities if combined PV-PEV connections exceed the hosting capacity of such networks. This is due to technical impacts caused by such LZC technologies on voltage quality, such as voltage rise, voltage drop, voltage unbalance, and on assets loading (transformer and conductors). This paper proposes a stochastic approach based on Monte Carlo simulations to estimate combined PV-PEV hosting capacity in LV networks. In addition, the proposed approach can be used to assist utility engineers to undertake planning actions based on the potential risk of violation. From the results obtained by applying the proposed approach to a real Brazilian LV network, overvoltage and unbalance voltage issues are found to be the most limiting factors of combined PV-PEV connections. Further, it is shown that the combined PV-PEV penetration levels need to be considered to avoid under or overestimations of both PV and PEV hosting capacity.

Equilibrium Optimizer-Based Approach of PV Generation Planning in a Distribution System for Maximizing Hosting Capacity

This paper presents an Equilibrium Optimizer (EO)-based method that simultaneously determines locations and sizes of multiple photovoltaic (PV) units for maximizing a distribution system’s PV hosting capacity (PVHC). The proposed method avoids various search-space restrictions seen in recent literature. The constraints in this paper are separated into two types based on the necessity of power flow analysis. The proposed method is implemented through co-simulation between MATLAB and OpenDSS. PV output and load demand variation is considered through a 24-hour profile representing a season. Smart inverter is considered through the PV inverter volt-var control (VVC) scheme. The proposed method is tested on the IEEE 123-bus benchmark system. Four test cases are considered to determine the impact of reverse power flow (RPF) limit and the impact of VVC toward the optimal PV planning. The results show that EO produces superior results compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Coyote Optimization Algorithm (COA).

Probabilistic Assessment of PV Hosting Capacity Under Coordinated Voltage Regulation in Unbalanced Active Distribution Networks

The increasing penetration of photovoltaic (PV) generators has led to a shift of the operational policy of the distribution system operator (DSO) from passive to active intervention in distribution networks (DNs), where a centralized controller governs the operation of voltage regulation devices. However, the PV output uncertainty hinders the verification of the impact of PVs on DNs. Therefore, the hosting capacity (HC) analysis framework for PV penetration should reflect both the operational benefit of the DSO and the PV output uncertainty. Thus, in this study, a two-stage optimization-based framework is proposed to analyze the probabilistic HC for PV under active network management (ANM) of DNs. In the first stage, the optimal PV base capacity (PVBC) to maximize the sum of the HC for PVs is determined based on a heuristic optimization method; in the second stage, using the predefined load and PV output profile, the maximum available power generation curve for PVBC is calculated, and the maximum HC for PV is derived by the calibration of PVBC through a comparison with the actual PV generation profile curve. The proposed method considers the time-series-based load flow results, reflecting the time-scheduling strategy by the DSO. Moreover, the uncertain characteristics of PV output are stochastically considered using a Monte Carlo simulation-based repetitive calculation approach. Case studies were implemented using the modified IEEE-123 test system, and the simulation results provided a quantitative comparison of the effect of the probabilistic HC improvement on the utilization of controllable resources and the centralized ANM by the DSO.