PV Integration Research Articles

Optimization and enviro-economic assessment of hybrid sustainable energy systems: The case study of a photovoltaic/biogas/diesel/battery system in Xuzhou, China

The current study intends to optimally design and carry out the economic and environmental assessment of a hybrid photovoltaic/biogas/diesel/battery (PV/BG/DG/battery) sustainable system to meet the electricity load of a village in Xuzhou, east China. HOMER (Hybrid Optimization of Multiple Energy Resources) software was used to perform the analyses and the results indicated that the PV/BG/battery hybrid system comprising 400 kWp PV modules, a 100 KW biogas generators (BGs), 400 batteries, and a 200 kW sized converter was the most cost-effective one. Besides, the optimal system operated in the load following (LF) strategy and had a total net present cost (NPC) and a cost of energy (COE) of $1,808,992 and 0.24 $/kWh, respectively. Compared to diesel generator (DG), the proposed system would save 1,297,174 kg of carbon dioxide (CO2) per year. The PV/battery hybrid system is the most environmentally friendly one with a complete absence of emissions of pollutants, whereas the DG-only is the most polluted system with the most emissions of pollutants. Additionally, the sensitivity analysis results showed that a reduction in the costs of BGs and batteries could effectively reduce the cost of the hybrid PV/BG/battery system. The integration of PV and BGs into hybrid sustainable energy systems may be a good choice for villages in developing countries if economic and environmental considerations are taken into account.

Allocation of Reserved Green Energy Transmission Corridors to Secure Power Purchase Agreements in Smart Power Networks during Congestion Management

Large scale PV integration is considered a good alternative for supplying large quantities of clean electrical energies. Setting the Power Purchase Agreements (PPAs) in smart power networks supplied undisputed committed power through PV plants in the networks. Sometimes, this power purchase agreement gets violated due to system constraints, especially in congestion management. The present study proposes a new solution for the security of PPAs in large-scale PV integrated smart power networks during congestion management via allocation of Reserve Green Energy Transmission Corridors (RGETCs) with reserved capacity in the power networks. The aim is to maximize the PV power utilization during congestion management. The Power Transfer Distribution Factor (PTDF) Indices have been used to identify the RGETCs in the existing power networks. The generation rescheduling via security constraints based optimal power flow has been adopted to manage the congestion in the networks. The non-linear interior point method has been chosen to obtain the optimal results. The modified IEEE-30 bus system and modified IEEE-118 bus system have been modelled to implement the proposed solution method. The test results show that the proposed solution provides security to the PPAs during congestion management and provides considerable technical and economic benefits.

Recognition of Islanding and Operational Events in Power System With Renewable Energy Penetration Using a Stockwell Transform-Based Method

Integration of RE sources to the utility grid offers technical and operational challenges causing problems of PQ, stability, identification of operational events, etc. This article presents an algorithm to identify events including islanding, grid integration, and outage of the solar PV and WG plants in grid using a ST. Islanding event may occur in the presence of any kind of plant. Processing of negative sequence component of voltage is performed by utilizing ST based multiresolution analysis at the test node and the output matrix is evaluated. The features (F1–F4), VI and STD indexes are obtained from this matrix. These features are utilized for identifying the events and transient phenomenon. The VI and STD indexes are used to recognize the type of RE source present during the islanding and outage events. Moreover, for recognizing the type of RE source at the time of synchronization event, an SI is proposed. This is computed by the ST depended processing of voltage signals. Performance of the algorithm is found satisfactory for all incidence angles and complete voltage cycle under the noisy conditions of 10 dB SNR. As compared to the time–frequency transform based coefficients of the voltage signal, the proposed technique is found to be superior in terms of small NDZ and low computation time and least affected by noise. Further, the developed technique is also efficient to detect various events stated above and the type of RE source. Study is performed using MATLAB/Simulink software and validated in real time using RTDS.

Multi-objective optimisation for building integrated photovoltaics (BIPV) roof projects in early design phase

Building-integrated photovoltaics (BIPV) is an excellent renewable energy application for building envelopes. In Australia, BIPV roofing is considered to be promising because of recent major concerns about fire risks of façades. However, the integration of PV into building envelope elements is a complicated process which, if not done properly, may lead to failure of the BIPV system. Therefore, feasible BIPV design solutions need to be examined in terms of life-cycle cost and energy performance from the early design phase of a BIPV envelope project. There have been few investigations of the identification of feasible BIPV design options in the early design phase to enable product selection and parametric tests. This study explores a multi-objective optimization method which formulates feasible BIPV envelope design options which have relatively high energy generation and low life cycle costs to informed decision making. The optimisation process has four segments: data inputs, simulator, optimizer and pareto front based optimised BIPV solutions. The proposed multi- objective optimization process is applied in a BIPV roof application using a case study in Australia with roof sheets and skylights. The results demonstrate seven optimum roof sheet BIPV design solutions and fourteen optimum skylight BIPV design solutions to enable design makers to compare and select the most appropriate. The life cycle cost, life cycle energy, CO2 emissions avoided, net present value, payback period, and levelized cost of energy of BIPV solutions vary based on the BIPV modules, tilt angles and placement locations. The proposed optimization method is a helpful guide for BIPV design professionals to identify suitable BIPV design options in the early design phase instead of relying on rule-of-thumb experience.

Module trainer of photovoltaic integrated low voltage grid (PV-LV grid) with a variety load

This research is a simulation of the penetration of renewable energy at the primary source. The entire system is a compound system, especially on the load. Load types provide different voltage and current treatment. The simulation is carried out with a mini-grid device consisting of five buses with interface sensors to monitor voltage and current. Photovoltaic grid module (PV)-LV as a trainer, uses two power sources to supply the load to the voltage distribution network module. The loading scenario h two plans, where the difference lies in the type of inductive load (scenario A) and capacitive (scenario B). The types of load consist of incandescent lamps, fluorescent lamps, energy-saving lamps, drilling machines, and soldering tools. Variations in this load to meet predetermined loading scenarios. The PV measurement results show maximum power generation at noon and flowed to the load trainer via a 300W grid-tie inverter of 191.1W. The average load bus voltage increases for scenario A and scenario B by 1.045% and 1.36%, respectively. The result indicates that the integration of PV in low voltage systems positively impacts the voltage profile for both inductive and capacitive loads. The amount of voltage improvement depends on the value of the active power injection of the PV and the load supplied by the system.

Characterizing the economic competitiveness of battery electric vehicles in India

Electric Vehicles being at a nascent stage in India, albeit at a slower growth rate, faces a significant barrier of economic compatibility. For countries like India, the economic aspect plays a vital role in the widespread of Electric Vehicles. Concentrating on the cost aspect, this paper contemplates the economic compatibility of battery electric vehicles (BEVs) with conventional IC Engine Vehicles (ICEVs), considering a paring vehicle approach and analysing the case study of India. As less research is carried out for India, we aim to develop an extensive total cost of ownership (TCO) model. Furthermore, the Sensitivity analysis presents the effect on TCO by considering parameters as incentives, reduction in battery prices, finance interest rates and Annual Kilometre Travelled (AKT) &EV + PV integration. For the base case, a much abysmal performance is observed for BEVs. While accounting for various Sensitivity analysis parameters, some BEVs reach cost parity while some become more economical than ICEVs.

Technical Analysis of Grid Connected 1 kWP Rooftop Solar PV for Residential Consumer at Urban Areas of Lalitpur, Nepal

Integration of rooftop solar PV by a residential consumer to the grid is beneficial to both consumer and grid if integrated optimally. The minimization of power loss by improving the voltage profile on the grid side and reducing the electricity bill by improving energy deficiency faced by the residential consumer is the benefit of the integrated PV system. The preparation of this paper is based on the study of grid impact analysis and performance analysis of solar among the urban areas of Lalitpur. First, the grid impact and optimum size calculation are performed in IEEE-33 and IEEE-15 bus test system, and Pulchowk feeder is taken for the study of real scenario. Improvement of voltage profile, loss reduction, and the number of houses feasible for injecting PV generation has been demonstrated graphically. The power loss is found to be reduced by 62.5% after injecting solar PV into the grid. PVsyst has been used for the performance analysis of the PV array and the performance ratios are within limits with a capacity factor of 0.177 and performance ratio of 0.74 and system yield factor of 4.24 kWh/kWp/day. From all the results and performance analysis, it can be concluded that integration of 1 kWp rooftop solar is technically feasible if the optimum number of houses are allowed to inject PV array power at the optimum location.

Green and Transportable Modular Building: a prefabricated prototype of resilient and efficient house

Remote working proved to be an advantage in terms energy-saving as well as, for many, in terms of quality of life, while it was a necessity during the pandemic crisis of 2020/2021. This new habit is now promoted by many big enterprises due to associated cost savings. Some experiences of remote working in different locations have started to be supported by incentives in small municipalities to repopulate remote locations in Italy and people are now more inclined to move not just for vacation. These conditions could lead to a sort of “climatic nomadism” achievable, among other, through transportable homes. The net-zero energy Green and Transportable Modular Building (GTMB) project is able to cut its environmental impact. It can assume different configurations according to the user’s needs. The house is based on a timber construction system, easily adjustable and adaptable according to a modular framework. The energy performance has been simulated by means of BIM/BEM methodologies. The building has been tested in two different climates and configurations according to variable social interaction. The energy balance of the house in the life cycle showed the achievement of a carbon zero balance mainly due to timber technology, a heat pump, and PV integration.

Optimal integration of photovoltaic power into the electricity network using Slime mould algorithms: Application to the interconnected grid in North Cameroon

This document proposes a method for determining the optimal point of integration of PV Generated into the electricity grid. The Slime mould optimization algorithm ( SMOA) is applied to determine the best node in the case of one and two injection points. The problem has been modelled as an optimization problem where the objective is to minimize joule losses and the main constraint is to regulate the voltage at each point. The proposed method was implemented in MATLAB and applied in the nodes of the IEEE networks 33 and 69 and the north interconnected grid (NIG) of north Cameroon. The results allowed us to determine the optimal points of the NIG allowing us to inject with less losses. Comparison of the results obtained with other algorithms showed that the SMOA has the best reduction in power losses and a good improvement in the voltage profile.

INFLUENCE OF OPAQUE PHOTOVOLTAIC SHADING ON MICROCLIMATE AND GROWTH OF STRAWBERRY IN GREENHOUSES

The effect of photovoltaic PV panels on strawberry growth and the greenhouse microclimate has been studied meanwhile; the annually generated electric energies were estimated by the PolySun program. The peak power of the opaque PV was 310 W. There were four PV panels distributed on the top of greenhouse south plastic roof at a vertical distance of 0.08 m as an isolated air layer under the PV panels at a tilt angle of 30 . Meanwhile at a horizontal distance between panels of 1.1 m to allow the solar light entering greenhouse. Results revealed that the greenhouse air temperature and solar radiation under the un-shaded greenhouse were higher than that of the shaded greenhouse by opaque PV. Moreover, the opaque PV temperatures ranged from 45 ᵒC to 47.5 ᵒC. The simulation results showed that the annual generated electric energy of the opaque PV panels was 3195 kWh which could cover approximately 30.4% of the annual electric energy demand for environmental control. In addition, there were no significant differences in the number of leaves, flowers including fruits, and chlorophyll contents between strawberries under the shaded and un-shaded greenhouses. In a word, the integration of opaque PV at a specific distribution is highly recommended to provide the required light for photosynthesis and energy for greenhouse without harming the growth of strawberries.

Analysis of Dual Input Buck-Boost Converter for Solar PV Integration with Wireless Electric Vehicle Charger

Investigation of on-board renewable solar PV and wireless EV chargingstation integration is studied in this paper. Integration of on-board solar PVpower with EV charger power will reduce the stress on the grid without theneed for extra ground for solar plant installation. A dual-input buck-boostconverter (DIBBC) is used to integrate the two power sources and chargethe EV battery. A small-signal model of the converter is used to design thecontroller for three switches of the DIBBC. The simulation model of the inte-grated solar PV system and wireless power transfer (WPT) system is designedfor charging a battery of 120V/165Ah at 130V. The hardware prototype ofthe proposed EV battery charging system is designed for 1.5kW to verify thesimulation results. WPT system is developed for circular spiral-shaped coils,which are series-series compensated for 85kHz resonance frequency. SolarPV is replaced by a solar simulator programmed to operate with the samespecifications used in the simulation. Results and analysis of the DIBBCbased charger with charging voltage 130V showed higher efficiency up to 92% when both the sources are supplying power to DIBBC. The proposedcharging system gives better efficiency with higher source voltages and whenthe difference in power supplied by the two sources is less. Thus, highervoltage sources are beneficial for improving the efficiency of the integratedcharging system. Further, loss analysis in major components of the converteris discussed.

A multi‐port MMC topology with reduced capacitor size for use in grid‐connected PV systems

AbstractIn this article, a novel multi‐port modular multilevel inverter topology is presented for grid‐connected photovoltaic systems, which feature a smaller capacitor size compared with its counterparts. In the proposed MMC topology, each submodule is an individual input port being fed by an independent PV array through a boost converter. This allows modularity of the proposed topology, easing its maintenance and facilitating its expansion for PV integration. Individual maximum power point tracking controllers are used to operate the boost converters in each submodule to extract maximum energy from their PV arrays under different radiation conditions. A model‐predictive control scheme is designed for regulating the proposed multi‐port MMC topology to inject the harvested solar power to the grid. This controller enables minimized circulating currents in the MMC arms and balanced capacitor voltages in the MMC submodules, while total harmonic distortion (THD) of injected current to the grid is kept within the limits. The proposed multi‐port MMC topology with model‐predictive control is validated under different radiation conditions for a grid‐connected PV system to indicate that the proposed topology has a desirable performance under different radiation conditions.

Power Quality and Performance Analysis of Grid-Connected Solar PV System Based on Recent Grid Integration Requirements

The electrical energy demand is steadily growing, and hence, the integration of photovoltaic system to the distribution networks is also dramatically increasing though it has a significant effect on the network’s power quality. The purpose of this paper is to analyze the impact of solar PV integration on the power quality of distribution networks. The study is conducted using ETAP software, taking one of the radial distribution networks available in Bahir Dar city during the peak of connected loads which has the least voltage profile. Furthermore, the optimal location of the PV in the network is done using particle swarm optimization. Accordingly, the appropriate location of the PV system is determined to be the farthest end bus (bus 34). Also, the impact in terms of voltage and current harmonic distortion on the distribution feeder network is comparatively discussed by comparing the distribution system parameters with different penetration levels of solar PV system. The simulation results obtained demonstrate that high harmonic distortion level is injected correspondingly as the penetration capacity of PV system increased which indicates that the solar PV system should be integrating only up to a maximum possible capacity the network can carry. The integration of the PV system beyond this maximum penetration level causes production of high harmonic distortion which adversely affects the system performance. At the maximum penetration level which allows the acceptable harmonic distortion limit, the total voltage harmonic distortion and current demand distortion are found to be 4.97% and 14.98%, respectively.

Linearised three‐phase optimal power flow for coordinated optimisation of residential solid‐state power substations

With the increasing integration of single-phase resources, for example, PV, batteries and solid-state power substations (SSPSs), in residential distribution networks, voltage regulation and phase balancing of distribution feeders are facing growing challenges. To solve these challenges, a linearised three-phase optimal power flow (OPF) model is proposed to coordinate the operation of residential solid-state power substations for voltage regulation and phase balancing of distribution feeders. The proposed three-phase OPF model schedules both real and reactive power of each components in the SSPSs with the purpose of minimising the total operating cost as well as improving the grid performance, for example, voltage regulation and phase balancing. Results of case studies validate the accuracy of the proposed three-phase OPF model. In addition, the effectiveness of coordinating multiple SSPSs for voltage regulation and phase balancing has been demonstrated.

Quantification of Demand-Supply Balancing Capacity among Prosumers and Consumers: Community Self-Sufficiency Assessment for Energy Trading

With the increased adoption of distributed energy resources (DERs) and renewables, such as solar panels at the building level, consumers turn into prosumers with generation capability to supply their on-site demand. The temporal complementarity between supply and demand at the building level provides opportunities for energy exchange between prosumers and consumers towards community-level self-sufficiency. Investigating different aspects of community-level energy exchange in cyber and physical layers has received attention in recent years with the increase in renewables adoption. In this study, we have presented an in-depth investigation into the impact of energy exchange through the quantification of temporal energy deficit–surplus complementarity and its associated self-sufficiency capacities by considering the impact of variations in community infrastructure configurations, variations in household energy use patterns, and the potential for user adaptation for load flexibility. To this end, we have adopted a data-driven simulation using real-world data from a case-study neighborhood consisting of ~250 residential buildings in Austin, TX with a mix of prosumers and consumers and detailed data on decentralized DERs. By accounting for the uncertainties in energy consumption patterns across households, different levels of PV and energy storage integration, and different modalities of user adaptation, various scenarios of operations were simulated. The analysis showed that with PV integration of more than 75%, energy exchange could result in self-sufficiency for the entire community during peak generation hours from 11 a.m. to 3 p.m. However, there are limited opportunities for energy exchange during later times with PV-standalone systems. As a potential solution, leveraging building-level storage or user adaptation for load shedding/shifting during the 2-h low-generation timeframe (i.e., 5–7 p.m.) was shown to increase community self-sufficiency during generation hours by 17% and 5–10%, respectively, to 83% and 71–76%.

Integration of PV Distributed Generator in Electrical Distribution System with Electric Vehicles Charging Stations Considering Uncertainties using Chaotic SSA Algorithm

The penetration of renewable energy resource units in the Electrical Distribution System (EDS) has gradually increased. In addition to that, the interest in the electrification of the transport sector has brought about increasingly significant incentives for the integration of Electric Vehicles Charging Station (EVCS). In this regard, the planning of the installation of PV source-based Distributed Generation (DG) units in EDS considering EVCS should be carefully considered to avoid stressing the EDS. This paper applied various Chaotic Salp Swarm Algorithm (CSSA) based various chaotic maps methods with the multi-objective functions that are considered minimizing simultaneous the Active Power Loss (APL), the Annual Losses Cost (ALC), and the Total Voltage Deviation (TVV) in EDS. The proposed algorithms are tested on a standard IEEE 69-bus system that is used to demonstrate the feasibility of the CSSA algorithm in allocating the DG units by considering the uncertainty of the power delivered by the DG as well as the variation of load demand and EVCS in 24 hours. Furthermore, the overall EDS performances are also enhanced with simultaneous placement of both devices.

Data centre day-ahead energy demand prediction and energy dispatch with solar PV integration

This paper presents a novel Marine Predators Algorithm for both training an Artificial Neural Network model used for predicting the energy demand and for solving a dynamic combined economic and emission dispatch of a data centre. The MPA is proposed for first finding the optimal weights and biases of the neural network based on a Mean Squared Error and Mean Absolute Error minimization objective function. Real life dataset obtained from an anonymous data centre operator in Cape Town, South Africa was used for the model implementation. The dataset was made up of a total of 564 samples and was split into training and testing set using an 80:20 ratio. The input variables contained in the dataset are the data centre’s ambient temperature, ambient relative humidity, chiller output temperature and Computer Room Air Conditioning supply temperature while the energy demand is the target variable. The optimal weights of the neural network model were analysed using a weights-based approach to determine the level of influence each input parameter of the model has on the data centre’s energy demand. Then based on the predicted energy demand of the data centre, a dynamic economic and emission dispatch problem is solved for the building while considering thermal and solar photovoltaic generations. A spinning reserve is also incorporated in the energy dispatch model to cater for any shortfall that may exist between the predicted and actual energy demand of the data centre due to possible inaccuracies in the energy demand prediction model. Results for the energy demand prediction task showed that the proposed method outperformed the state-of-the-art by producing the least Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error and highest prediction accuracy for the training and testing sets. Further analyses also highlighted that the data centre’s ambient temperature has the highest influence of about 37.63% on its energy demand pattern. For the energy dispatch task, the proposed method also identified solar photovoltaic as the preferred energy source over conventional thermal generators in fulfilling the objective function, depending on its availability. Overall, the findings presented in this study emphasize the robustness of the proposed method in solving the problems considered and its potential application towards solving even more complex problems.

A real-time automated scheduling algorithm with PV integration for smart home prosumers

This paper proposes an IoT-based automated home energy management system (HEMS) focusing on cloud computing infrastructure for real-time demand response (DR). The stochastic real-time scheduling algorithm has been developed for solving the multi-objective optimization problem of minimizing electricity cost while preserving comfort. First, an IoT-enabled smart plug has been created to store load profiles of home appliances and control them through the cloud platform. Next, scheduling optimization with PV integration and various types of constraints has been discussed. Finally, an autonomous, efficient, and real-time energy scheduling scheme utilizing forecasted PV power for different pricing scenarios has been developed. The priority-driven algorithm schedules appliances every 5-min based on a trade-off between the current price and the consumer's comfort. The appliance-level dispatch with smart plugs, and the flexibility analysis could help the HEMS to achieve better energy efficiency without causing major discomfort to the consumer. The performance of the proposed algorithm has been demonstrated under different request patterns, PV generation, and pricing scenarios. On average, the potential cost saving achieved under different pricing schemes using RTP, ToU, and CPP pricing is 40%, 45%, 48%, respectively.

Evaluation of model fidelity for solar analysis in the context of distributed PV integration at urban scale

Evaluation of model fidelity for solar analysis in the context of distributed PV integration at urban scale

A novel and cost-efficient energy management system for plug-in electric bus charging depot owners

The integration of renewable Distributed Generations (DGs) has been a serious concern over reliable and satisfactory operation of the distribution system due to the intermittent nature of DGs. One of the solutions being proposed is the utilization of an Energy Storage System (ESS). However, without an appropriate energy management system, the integration of PV and ESS in a Plug-In Electric Bus Depot Charging (PEBDC) ecosystem can be harmful to the LV feeder. Therefore, this paper proposed a cost-efficient energy management system that is based on a double-sided auction mechanism that provides a platform to perform energy trading among the selling and buying agents in the PEBDC ecosystem. A simulation has been performed for the energy management of the PEBDC ecosystem considering the middle months of all four seasons Summer, Autumn, Winter, and Spring corresponding to January, April, July, and October, respectively. A mixed-integer linear programming (MILP) model has been formulated for Bus Depot Owner (BDO) profit maximization and analysed using IBM ILOG studio with CPLEX solver.