Photovoltaic Generation Capacity Research Articles

Cooperative operation strategy of electric vehicle and photovoltaic power station considering carbon reduction benefit under dynamic electricity price.

In the context of the global green and low-carbon transformation, microgrids containing renewable energy have been widely developed. At present, renewable energy generation has the disadvantages of instability and low energy density. In addition, the high proportion of electric vehicles (EVs) connected to the state grid will cause different degrees of disturbance to its safe operation. Therefore, a coordinated operation strategy of EV and photovoltaic (PV)-energy-storage charging stations induced by dynamic electricity price considering carbon reduction benefit is proposed. On the power generation side, a dual-axis PV tracking control method with "fixed frequency + variable frequency" control is proposed. One day is used as a period to divide the time segments, and the same time segment uses the fixed frequency tracking method, while different time segments use the variable frequency tracking method to improve the power generation efficiency. On the electricity consumption side, a dynamic electricity price strategy is adopted, using the minimum carbon reduction cost as the reward function, optimizing the dynamic electricity price under the minimum carbon reduction cost using the deep deterministic policy gradient (DDPG) algorithm to promote the shifting of EV charging load to the effective hours of PV generation. In conclusion, the simulation analysis is carried out in Zibo City, and the generation capacity of the proposed tracking method on the power generation side is improved by about 32% compared with the fixed PV generation capacity. Compared with the time-of-use electricity price, the optimized dynamic electricity price under the minimum carbon reduction cost can better promote the load transfer and photoelectric consumption of EVs and reduce the carbon reduction cost.

Tipping the scale: Why utility-scale solar avoids a solar rebound and what it means for U.S. solar policy

Adoption of residential rooftop photovoltaic (PV) systems is increasingly widespread. However, empirical evidence shows that households who adopt rooftop PV increase total electricity consumption, a response known as the ‘solar rebound effect’ (SRE). The SRE implies that rooftop PV generation displaces conventional, grid-supplied electricity—still overwhelmingly generated by fossil fuel combustion—on a less than one-for-one basis. This article argues that utility-scale solar avoids a SRE because the SRE emerges as a household’s response to the self-generation of electricity by its rooftop PV system, which changes its electricity consumption incentives in ways that utility-scale solar does not. By avoiding a SRE, utility-scale solar allows the carbon reduction potential of increased PV generation capacity to be more fully realized, which has important implications for U.S. solar policy.

Optimal sizing and siting of energy storage systems considering curtailable photovoltaic generation in power distribution networks

This work proposes a method for optimal planning (sizing and siting) energy storage systems (ESSs) in power distribution grids while considering the option of curtailing photo-voltaic (PV) generation. More specifically, for a given PV generation capacity to install, this method evaluates whether curtailing PV generation might be more economical than installing ESS. Indeed, while curtailing excess PV generation might be considered a last resort to avoid grid violations during operations, it is typically neglected in the planning phase. The proposed method accounts for the constraints of the power grid (i.e., nodal voltages, lines, and substation transformer limits) modeled by linearized power flow equations to keep the problem formulation tractable. The planning problem minimizes the net investment costs of the ESSs, and the imported and exported electricity costs considering a planning horizon of 20 years. The results are presented for a medium voltage (MV) distribution grid with different levels of installed capacity of PV generation, reflecting future scenarios of PV generation development. The sensitivity of the ESSs’ sizes and investment costs to the electricity prices accounting for variable levels of PV production in the global generation mix is also investigated.

Innovative sensorless dual-axis solar tracking system using particle filter

Renewable energy (RE), particularly solar photovoltaic (PV) systems, is a promising alternative to traditional energy sources for worldwide electricity generation. In recent years, PV generation capacity has increased rapidly due to a considerable reduction in installation costs. Nevertheless, in PV systems, energy generation is significantly less than the installed capacity due to the PV modules’ orientation. Hence, this study proposes an innovative solar tracking system to enhance PV energy generation. Previous research classified solar tracking systems into two categories: sensor-based and sensorless solar tracking systems. However, tracking performance inevitably deteriorates when implementing sensor-based solar tracking systems in low-visibility conditions. Conversely, sensorless solar tracking systems require substantial historical meteorological data based on geographic location and complex mathematical models estimating the sun’s position, increasing computational complexity. This study aims to overcome these difficulties by proposing a sensorless dual-axis solar tracking system that does not require historical meteorological data, complex mathematical models, and sun position sensors to track the sun’s position. Particle filter (PF), a robust sampling-based tracking algorithm, is applied to develop an innovative solar tracking strategy. Initially, a set of samples (also called particles) is generated as inputs to PF corresponding to the proposed tracking system’s orientation angles: daily and elevation angles. Next, PV power is captured as a measurement of each particle, and a weight associated with the PV power is estimated to represent each particle’s relative significance. Furthermore, each particle is estimated and updated recursively, considering its measurement to determine the sun’s probable position. The tracking strategy is terminated once the particles’ weights are equal, indicating that the optimal position has been achieved (convergence). A comparative study is conducted over 60 days under various weather conditions to evaluate the proposed tracking system’s performance compared with that of a fixed flat-plate system. The experimental results indicate that the proposed tracking system improves energy generation performance (after accounting for operational energy consumption) by 20.1% compared with the fixed flat-plate system.

Maximum Hosting Capacity of Photovoltaic Generation in SOP-Based Power Distribution Network Integrated With Electric Vehicles

Photovoltaic generation is playing a significant role in realizing the target of carbon neutralization. This article proposes a two-stage robust optimization model to assess the maximum hosting capacity of photovoltaic generation in power distribution network (PDN) considering the integration of soft open point (SOP) and electric vehicles (EVs). Convex relaxation method is applied to convert the two-stage robust optimization model to the form of mixed-integer second-order cone programming. A solving strategy based on the column-and-constraint generation algorithm is developed to solve the model, in which linearized difference-of-convex strategy is proposed to deal with the relaxation gap. Case studies are carried out on the modified IEEE 33-bus PDN integrated with SOP and EVs, and the results show the advantages of the proposed model and solving strategy.

Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability

AbstractExpansion of distributed solar photovoltaic (PV) and natural gas‐fired generation capacity in the United States has put a renewed spotlight on methods and tools for power system planning and grid modernization. This article investigates the impact of increasing natural gas‐fired electricity generation assets on installed distributed solar PV systems in the Pennsylvania–New Jersey–Maryland (PJM) Interconnection in the United States over the period 2008–2018. We developed an empirical dynamic panel data model using the system‐generalized method of moments (system‐GMM) estimation approach. The model accounts for the impact of past and current technical, market and policy changes over time, forecasting errors, and business cycles by controlling for PJM jurisdictions‐level effects and year fixed effects. Using an instrumental variable to control for endogeneity, we concluded that natural gas does not crowd out renewables like solar PV in the PJM capacity market; however, we also found considerable heterogeneity. Such heterogeneity was displayed in the relationship between solar PV systems and electricity prices. More interestingly, we found no evidence suggesting any relationship between distributed solar PV development and nuclear, coal, hydro, or electricity consumption. In addition, considering policy effects of state renewable portfolio standards, net energy metering, differences in the PJM market structure, and other demand and cost‐related factors proved important in assessing their impacts on solar PV generation capacity, including energy storage as a non‐wire alternative policy technique.This article is categorized under: Photovoltaics > Economics and Policy Fossil Fuels > Climate and Environment Energy Systems Economics > Economics and Policy

Using rooftop photovoltaic generation to cover individual electric vehicle demand—A detailed case study

The introduction of battery electric vehicles (BEV) and the expansion of rooftop photovoltaic (PV) power generation are both progressing at a fast pace to decarbonize the transport and the energy sector in Switzerland. These parallel developments have an enormous synergy potential as the actual decarbonization impact of BEVs depends heavily on the carbon footprint of the power source and the PV expansion requires local storage as a buffer to reduce negative impacts on the distribution grid. We present an empirical analysis based on a detailed 10-month data set of the charging and mobility behavior of 78 BEV users in Switzerland. It is combined with a fine-grained digital surface model of Switzerland to extract the detailed roof geometry and the corresponding rooftop PV generation capacity of each of the BEV owner’s houses.We test four different smart charging strategies with a varying degree of complexity and find that when charging uncontrolled (the strategy used during the study), BEV owners can only cover 15 % of their BEV’s demand using PV generated from the roofs of their own houses. A simple controlled charging approach greatly increases the average coverage to 56 % and up to 90 % or 99 % when using an optimized charging strategy without or with a home battery storage. All charging strategies ensure that the individual mobility behavior of the BEV owners is not affected.We further show that using rooftop PV power generation for BEV charging has a large potential to further decrease the climate impact of BEVs and propose simple adjustments to consider in charging strategies that help to increase the owners’ PV consumption.

An available capacity evaluation method for photovoltaic generation considering hydrogen storage

With the rapid development of global photovoltaic applications in recent years, it is crucial to evaluate the photovoltaic generation capacity to achieve a secure and efficient utilization of photovoltaic resources. This paper proposes an available photovoltaic evaluation method for generation capacity in distribution networks. The evaluation model is based on the optimal power flow model, considering operational constraints with reactive power and voltage limits. A novel multi-segment boundary approximation method is proposed to solve the model and obtain the available photovoltaic generation capacity explicitly. In addition, the effect of hydrogen storage is explored to host the photovoltaic generation capacity of the distribution network. The accuracy and calculation speed of the proposed method is greatly improved compared with traditional methods. Furthermore, the proposed method can promote the utilization of photovoltaic generation and help dispatching, which guarantees security and improves economics in the distribution network. Simulations on the modified IEEE 33-bus system demonstrate the effectiveness of the proposed evaluation method.

A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation

Rooftop photovoltaic (PV) systems are usually behind the meter and invisible to utilities and retailers and, thus, their power generation is not monitored. If a number of rooftop PV systems are installed, it transforms the net load pattern in power systems. Moreover, not only generation but also PV capacity information is invisible due to unauthorized PV installations, causing inaccuracies in regional PV generation forecasting. This study proposes a regional rooftop PV generation forecasting methodology by adding unauthorized PV capacity estimation. PV capacity estimation consists of two steps: detection of unauthorized PV generation and estimation capacity of detected PV. Finally, regional rooftop PV generation is predicted by considering unauthorized PV capacity through the support vector regression (SVR) and upscaling method. The results from a case study show that compared with estimation without unauthorized PV capacity, the proposed methodology reduces the normalized root mean square error (nRMSE) by 5.41% and the normalized mean absolute error (nMAE) by 2.95%, It can be concluded that regional rooftop PV generation forecasting accuracy is improved.

Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification

Photovoltaic (PV) generation prediction is a critical technology for integrating solar energy in power systems and markets. Accuracy is the target for most PV prediction models, which represents the minimisation of the average error. However, minimisation of prediction error is to obtain a minimum cost from impact of prediction inaccuracy. The lowest average error may not always relate to the minimum cost. Thus, this paper proposes an integrated PV prediction structure that targets minimum industrial cost from prediction error other than using pure accuracy. The object of machine learning model is modified into the further industrial cost of prediction error, which is the cost of backup generation participation in power dispatch for power grid energy balancing. A feed-forward neural network is selected as typical machine learning model for integration. Additionally, to solve the nesting optimisation problem in network training, an equivalent model is constructed to remove the sub-optimisation and make gradient-based training optimisation feasible. A numerical study shows that the integrated structure leads to prediction results with a lower cost than those of an accuracy-based structure.

Photovoltaic Generation Integration with Radial Feeders Using GA and GIS

The electric power generated from different electricity sources are not used efficiently by end users in the world. This is due to the loss of power supplied in the case of electricity transmission and distribution to residential, commercial, and industrial loads. Even if the loss of power in the power system cannot be avoided 100%, it should be reduced to the minimum optimal value. The loss of power in the radial feeders can be minimized using an optimally allocated photovoltaic (PV) generation system by considering the information of geography, solar irradiance of the site, and space availability, which should not have shadow from large buildings and trees. The PV generation system eliminates the problem of power demand by enhancing the capacity of the power network as well as by reducing the depletion and consumption of fossil fuel resources. To reduce power loss and improve system loading capacity for demand response, the integration and finding the optimal place of photovoltaic generation take high concern from power system operators and technicians. The optimal allocation of PV has been done using the Genetic Algorithm (GA) for optimization of a multiobjective function with different constraints. The main objective of this paper is to minimize the power loss of the radial distribution networks by maintaining the phase voltage of the load in balance and improving the drop in voltage along the phase. So, GA is used to determine the best location and capacity of PV generation that can reduce the loss of power in the system. The IEEE-33 bus system is used to test the proposed method. Generally, using the GA and GIS methods results in a high accuracy for optimal placement of PV generation in the IEEE-33 bus radial feeder and enables to reduce the loss of power during transmission and distribution by maintaining the power quality for consumers.

Modelling and Control of Ensembles of Variable-Speed Air Conditioning Loads for Demand Response

Air conditioners (ACs) are an important resource for demand-response (DR) services in electricity networks and have motivated much research activity in recent years. Existing approaches to model and control aggregate AC demand largely focus on ensembles of fixed-speed (on/off) compressor technology. However, AC markets are dominated by variable-speed compressor technology, which is rapidly becoming widespread. This paper presents a mean-field, first-principle modelling formalism for the analysis and control of aggregate demand of heterogeneously distributed ensembles of variable-output compressor ACs (VOCACs). This formalism posits that the model structure of an individual VOCAC fitted with the mean values of the VOCAC parameters distributed in the ensemble can accurately represent the aggregate DR of the ensemble, in complete contrast with models for fixed-speed AC ensembles. The proposition is numerically validated in two prevalent DR applications of model predictive control (MPC): shaving peak demand and firming photovoltaic generation capacity. The proposed representative model is embedded in the MPC designs to predict the aggregate demand of a heterogeneously distributed ensemble of 1,000 VOCACs by centralised manipulation of temperature setpoints. The model is extended to enable DR by constraining power consumption, which is illustrated in an MPC implementation of the Australian/New Zealand Standard AS/NZS-4755 DR modes.

Optimal Sizing of Photovoltaic Generation in Radial Distribution Systems Using Lagrange Multipliers

The integration of renewable distributed generation into distribution systems has been studied comprehensively, due to the potential benefits, such as the reduction of energy losses and mitigation of the environmental impacts resulting from power generation. The problem of minimizing energy losses in distribution systems and the methods used for optimal integration of the renewable distributed generation have been the subject of recent studies. The present study proposes an analytical method which addresses the problem of sizing the nominal power of photovoltaic generation, connected to the nodes of a radial distribution feeder. The goal of this method is to minimize the total energy losses during the daily insolation period, with an optimization constraint consisting in the energy flow in the slack bus, conditioned to the energetic independence of the feeder. The sizing is achieved from the photovoltaic generation capacity and load factors, calculated in time intervals defined in the typical production curve of a photovoltaic unit connected to the distribution system. The analytical method has its foundations on Lagrange multipliers and relies on the Gauss-Jacobi method to make the resulting equation system solution feasible. This optimization method was evaluated on the IEEE 37-bus test system, from which the scenarios of generation integration were considered. The obtained results display the optimal sizing as well as the energy losses related to additional power and the location of the photovoltaic generation in distributed generation integration scenarios.

Modelling and allocation planning of voltage‐sourced converters to improve the rooftop PV hosting capacity and energy efficiency of distribution networks

This paper presents a planning approach for improving the rooftop photovoltaic (PV) hosting capacity and energy efficiency of distribution networks with the placement of voltage-sourced converters (VSCs). The PV hosting capacity (PVHC) is the maximum amount of PV generation that a distribution network can accommodate without deteriorating the operational performance of the network. The PVHC is derived under the constraint of a maximum allowable network power loss. Two different types of VSCs are modelled for the placement in distribution networks to improve the PVHC. These are series and shunt VSCs. The series VSC is designed to inject a series voltage in quadrature with the line current to maintain a constant bus voltage. The shunt VSC is designed to improve the load power factor to unity by injecting a shunt compensating current in quadrature with the bus voltage. The objective function is to maximise the PVHC to obtain the maximum PV generation capacity to be integrated in each bus. The particle swarm optimisation is used as a solution approach. The results show that the placements of VSCs improve the PVHC. This also reduces energy loss when there is no PV generation available.

Simultaneous optimisation of photovoltaic hosting capacity and energy loss of radial distribution networks with open unified power quality conditioner allocation

This study presents a multi-objective planning approach to optimally place open unified power quality conditioner (UPQC-O) by simultaneously optimising the photovoltaic (PV) hosting capacity (PVHC) and energy loss of distribution networks. The modelling of UPQC-O consisting of a series and a shunt inverter is carried out so as to place in distribution networks. The Pareto-dominance-based approach is used to simultaneously optimise the objective functions to determine the optimal PV generation capacity in each bus and the locations of the inverters of UPQC-O. The limits on bus voltage magnitude, line current flow, maximum PV generation capacity in each bus, and the percentage of voltage sag mitigated load are considered to be the operational constraints in this planning problem. The solution strategy used is the multi-objective particle swarm optimisation. The approach provides the Pareto-approximation set consisting of a number of trade-off solutions in view of PVHC and energy loss. A utility can choose a solution for implementation depending on its desired PVHC and/or energy loss level. The results show that the PV deployment in distribution networks reduces the energy loss. However, the amount of energy loss reduction diminishes with higher and higher values of PV capacity integration.

Comparing supply and demand models for future photovoltaic power generation in the USA

AbstractWe explore the plausible range of future deployment of photovoltaic generation capacity in the USA using a supply‐focused model based on supply‐chain growth constraints and a demand‐focused model based on minimizing the overall cost of the electricity system. Both approaches require assumptions based on previous experience and anticipated trends. For each of the models, we assign plausible ranges for the key assumptions and then compare the resulting PV deployment over time. Each model was applied to 2 different future scenarios: one in which PV market penetration is ultimately constrained by the uncontrolled variability of solar power and one in which low‐cost energy storage or some equivalent measure largely alleviates this constraint. The supply‐focused and demand‐focused models are in substantial agreement, not just in the long term, where deployment is largely determined by the assumed market penetration constraints, but also in the interim years. For the future scenario without low‐cost energy storage or equivalent measures, the 2 models give an average plausible range of PV generation capacity in the USA of 150 to 530 GWdc in 2030 and 260 to 810 GWdc in 2040. With low‐cost energy storage or equivalent measures, the corresponding ranges are 160 to 630 GWdc in 2030 and 280 to 1200 GWdc in 2040. The latter range is enough to supply 10% to 40% of US electricity demand in 2040, based on current demand growth.

Photovoltaic-Model-Based Solar Irradiance Estimators: Performance Comparison and Application to Maximum Power Forecasting

Due to the increasing proportion of distributed photovoltaic (PV) production in the generation mix, the knowledge of the PV generation capacity has become a key factor. In this paper, we propose to compute the PV plant maximum power starting from the indirectly estimated irradiance. Three estimators are compared in terms of ability to compute the PV plant maximum power, bandwidth, and robustness against measurements noise. The approaches rely on measurements of the DC voltage, current, and cell temperature, and on a model of the PV array. We show that the considered methods can accurately reconstruct the PV maximum generation even during curtailment periods, i.e., when the measured PV power is not representative of the maximum potential of the PV array. Performance evaluation is carried out by using a dedicated experimental setup on a 14.3 kWp rooftop PV installation. Results also proved that the analyzed methods can outperform pyranometer-based estimations with a simple sensing system. We show how the obtained PV maximum power values can be applied to train time series based solar maximum power forecasting techniques. This is beneficial when the measured power values, commonly used as training, are not representative of the maximum PV potential.

Solar Photovoltaic Energy and Its Spatial Dependence

In the last decade, solar photovoltaic has started to play a significant role in the energy mix consumption. Although this growth has involved almost all the western countries, marked differences in the regional distribution of photovoltaic generation capacity have been observed. These differences appear to be weakly related to climate conditions in general, and to solar radiation specifically. The literature has started to investigate the other underlying determinants, suggesting to consider the occurrence of spatial proximity effects. Accordingly, this study aims to analyze whether and to what extent the photovoltaic energy production depends on local factors, such as climate, demand, income, innovative and responsible behavior, and so forth. Through a spatial autoregressive model, we find that the regional distribution of photovoltaic production capacity is affected by strong spatial dependence. We show that the availability of photovoltaic energy may be explained by peer effects, such as diffusion of habits and emulation of neighbors.

The Impact and Placement of Photovoltaic Distributed Generation in Ship Distribution Network for Loss Reduction

Recent advances in photovoltaic (PV) technologies, controls, and power converter topologies have resulted in the increasing application of green energy from solar in ship power system. However, there is very few research on the loss changes of shipboard distribution network produced by introduction of PV distributed generation. In this paper, we report on one ideal model and one model with an example of chain nine nodes distribution network for analyzing the impact and placement of ship distribution network with PV distributed generation. After empirical analyses, we concluded major results as follows: 1) the proper configuration of PV generation in the traditional power network can reduce the loss of distributed network effectively; 2) PV generation should be installed at the middle location of total loads; 3) the capacity of PV generation should approach the capacity of total loads; 4) PV generation should work at the lagging power factor.

Fundamental Study of Voltage Stability in a Power System with Large Penetration of Photovoltaic Generation Considering Induction Motor Dynamics

SUMMARYBecause of global warming, attention to photovoltaic (PV) generation has increased, and considerable PV generation capacity will be installed in the power system. In Japan, most PV generation will be connected to the distribution system, which will cause changes in power flow in the power system. Forward power flow (flow from the higher voltage system to the lower voltage system) will be reduced and reverse power flow may occur. Because of the change in power flow, the voltage characteristics will also be changed. Consequently, it is expected that the penetration of PV generation will have some impact on voltage stability. In previous studies, the voltage characteristics in a simple power system model consisting of one infinite bus, one load, and one PV power source, have been investigated. It was found that there is not only a forward power flow limit but also a reverse power flow limit (the left nose in the PV curve), and that when the output power from PV generation is very large, the left nose sometimes has some impact on voltage stability. In this paper, the voltage stability in a simple power system model considering the dynamics of an induction motor is discussed.