Optimal PV Research Articles

Optimal PV and Battery Investment of Market-Participating Industry Facilities

Introducing flexible consumers to electricity markets is beneficial to the power system and offers them potential economic savings. Industrial consumers are pioneer candidates due to their high energy demand and interest in reducing energy costs. This paper addresses the battery storage and photovoltaics investment problem, which includes five revenue streams for industrial consumers, such as participation in the day-ahead and intraday energy markets as well as the primary control reserve market peak shaving and optimized self-consumption. The uncertainty is considered using correlated scenarios of the local load, primary reserve market and day-ahead market prices, as well as generation from photovoltaics. The uncertainty of the pay-as-bid intraday market with continuous trading is modeled using robust optimization. Credibility and applicability of the model is achieved by using market settings and prices from three European countries (Germany, Denmark and Croatia) and comparing their suitability for encompassing the end-user flexibility. Results shed light on national energy-political and climatic differences, highlighting opportunities for active market participation through individual or aggregated industrial plants.

Reducing conventional generation dependency with solar Pv power: A case of Delhi (India)

ABSTRACT In several Indian cities, the governments is planning to reduce excessive air pollution due to fossil fuel used by traffic and local industries. This is done by encouraging greater use of renewable energy resources. One such city that has come under this observation is the Metropolis of Delhi, the capital of India. The Government of Delhi NCR has initiated corrective policy moves that promote the use of solar photovoltaic energy for minimizing dependency on fossil-fueled generation. However, it is well known that renewable energy has its limitations in relation to steady availability and desired quantity for bulk applications. Thus, at best, renewable energy can partially replace conventional fossil-fueled energy resources. The extent to which solar photovoltaic (SPV) energy can replace conventional-fueled generation is broadly referred to as capacity credit or capacity value of the concerned SPV. This paper is an attempt to compute the sizing of the optimum solar PV installation for a satisfactory level of capacity credit. The analysis employs the state sampling approach. The system adequacy is evaluated in terms of the reliability indices Loss of Energy Expectation (LOEE) and Loss of Load Expectation (LOLE). The Monte Carlo simulation technique is applied for the reliability evaluation, on account of its simplicity and better relative accuracy as compared with other available techniques. It is observed that for a solar penetration level of 30%, the increment in capacity contribution shows dissimilar trends with the increment in installation capacity. The optimum installation is witnessed for the 200 W and 400 W for the reliability criteria of LOLE and LOEE indices, respectively.

Economic analysis of a novel design of microtracked concentrating photovoltaic modules

AbstractA new design for microtracked concentrating photovoltaics (mCPVs) published by one of us in Nature Energy has the same form factor as PV. This paper analyzes the cost below which it would compete with PV in behind‐the‐meter applications for commercial electricity customers in four cities in southern United States, assuming that the only significant differences between mCPV and PV are mCPV's higher efficiency and mCPV's inability to utilize sunlight that is either diffuse or has a high angle of incidence. The analysis first optimizes PV and mCPV microgrids and then selects the battery capacity that maximizes the internal rate of return (IRR) and selects a target mCPV installed cost for which the optimal mCPV IRR matches the optimal PV IRR. The result for projects starting in 2020 is $1.2/W in Lancaster, CA and Las Vegas, cities with similar ratios of direct to total irradiance. Modesto, CA and Kansas City have target mCPV installed costs of $1.07/W and $0.94/W, respectively. The electricity tariff and battery costs impact both PV and mCPV and have a minimal effect on the mCPV cost at which the two technologies compete. mCPV occupies 33.2% less area per watt than PV reducing some components of installed cost. In 2020, target mCPV module costs are $0.33/W in Lancaster and Las Vegas, $0.25/W in Modesto, and $0.17/W in Kansas City. These target costs decline by on average 9.6% per year for projects starting 2021–2025, similar to the 9.3% rate of decline in projected PV module costs over this period.

Decarbonising heat with optimal PV and storage investments: A detailed sector coupling modelling framework with flexible heat pump operation

This paper analyses optimal electricity investments (PV and battery storage) to decarbonise heat supply in residential buildings under different heat pump and energy retrofitting scenarios in a detailed representation of the Swiss power and heating system. The sensitivity of PV and storage deployment, including lithium-ion (LiB) and vanadium redox flow batteries (VRFB), with respect to distribution network capacity is also investigated. We propose an open-source dispatch sector coupling model (GRIMSEL-AH) to minimise energy system costs (social planner perspective) for heating and electricity supply in Switzerland with hourly and daily time resolution for electricity and heating respectively. Moreover, our representation of the Swiss energy system includes various types of consumers and urban settings which are represented with monitored electricity demand data for each sector and simulated heat demand data at the building level for the residential sector. We find that under a “business as usual” heat pump deployment and retrofitting rate, the optimal electricity investments correspond to 27.8 GWp of PV combined with 16.9 GW (33.8 GWh) for LiB and 1.9 GW (7.6 GWh) for VRFB. For this case, 57% (13.3 TWth/year) of the residential heat demand is covered by heat pumps with a total installed capacity of 19.7 GWth by 2050 (capacity exogenously set with its operation optimised). With increasing heat pump deployment, retrofitting rates are found to have a large impact on the investment in storage and a 100% heat pump scenario for the residential sector appears to be feasible. Our results show that heat pumps do not only decarbonise heat but also provide extra flexibility to the power system, since they increase local PV self-consumption, resulting in higher PV deployment. The model and the methodology presented in this study can be applied to other countries.

Towards a sustainable and cleaner environment in China: Dynamic analysis of vehicle-to-grid, batteries and hydro storage for optimal RE integration

Over the last 20 years, development in China hasfollowed a positive trend. However, the high energy consumption has impacted the environment negatively. About 239 cities fell short of the national air quality standard in 2017 and the groundwater fell from quality 31.9% to 10.9% in 2018. In this study, the use of solar PV and onshore wind turbines for electricity generation in a particular area in China is analyzed. The integration of hydrogen production and Electric Vehicles (EVs) into the energy mix is dynamically modeled.Three storage mechanisms namely; vehicle-to-grid (V2G), V2G + batteries, and V2G + pumped hydro storage wasconsidered. EnergyPLAN computer program is used to perform a dynamic simulation in this research to analyze the potential carbon emission reduction of the proposed methodology. The optimal PV and on-shore wind turbine capacity required for a 1 TWh/yr electricity demand and 0.02 TW/yr EVs charge demand is 400 MW and 350 MW respectively. The storage capacities are 30 GWh of V2G, 55 GWh of V2G + Batteries, and 60 GWh of V2G + pumped hydro storage with various charge/discharge hourly profiles. The proposed methodology in this research will reduce the yearly coal consumption by 495.3 kton and yearly carbon emission by 368.2 kton.

Determination of PV Power and Battery Capacity Size for a Leisure Solar Powered Boat at Kalimas River, Surabaya, Indonesia

As a green city, Surabaya had been developing a program of an environmentally friendly concept in almost all sectors. One of the developed sectors with this concept is the tourism area around the Kalimas river. Tour by riding a small passenger leisure boat is the most favorite recreational option. The leisure boat designs with environmentally friendly solar-powered concepts were developed, but regarding PV power and battery capacity determination, almost all designs are not optimal. This research is aimed to obtain the optimal PV power and battery capacity by calculating the number of PV panels and batteries with minimum cost. A Linear programming approach by Simplex method is applied in the optimization calculation. The results show that the number of the battery of the previous design can be reduced from 4 (four) units (20 kWh) becomes 3 (three) units (15 kWh) and the number of PV panels are still the same number with the previous one, i.e., 7 (seven) units (2,24 kW). The optimum system cost is around 264 million rupiahs, which means that the cost is reducing around 81 million rupiahs or 24%.

Decentralized T-S Fuzzy Control for Solar PV Powered Water Pumping System Driving by Induction Motor

This paper presents a decentralized Takagi Sugeno (T-S) control scheme for a PV powered water pumping system, which is composed of a photovoltaic generator (PVG) supplying via a DC-DC boost converter, a DC-AC inverter, an Induction Motor coupled to a centrifugal pump. A T-S fuzzy controller is developed for MPPT (Maximum Power Point Tracking) to control the DC-DC boost converter, under variable solar irradiation and ambient temperature. An observer-based T-S fuzzy controller is dedicated to control the IM to guarantee the field-oriented control performances. From the optimal PV power provided in the MPP conditions, the optimal speed is calculated and delivered to control the IM, so that the proposed PV pumping system operates in optimal conditions and thus, maximizes the quantity of water pumped daily. Finally, simulation results are presented for both transient and steady state operation while taking into account all changes in climatic conditions, in order to validate the efficiency of the developed decentralized controller.

Loss analysis and optimization of PV module components and design to achieve higher energy yield and longer service life in desert regions

The global share of photovoltaic plants in desert locations increases continuously due to inexpensive land and higher yield due to higher irradiation levels. However, PV modules suffer from harsh environmental conditions that influence their lifetime and, consequently, the levelized cost of electricity. Environmental factors such as high temperature differences between nights and days, high ultraviolet doses, high ambient temperatures, and high airborne dust lead to durability and performance issues such as delamination, discoloration, fatigue of interconnection, breakage of solar cells, hot-spots, and power loss due to the soiling. In this work, different bills of materials and module designs are evaluated, targeting optimum PV output power while increasing the service life and performance of the PV modules in desert climates. A stepwise optimization of module components (solar cells, glass coating and polymers/encapsulation) and module design (full vs. half cells, tab widths) are performed by simulation and experimental approaches. Simulations results analyzes the loss mechanisms and electricity production of PV modules by considering the impact of module material and design Experimentally, ultraviolet stress tests and thermal cycling tests are performed for polymer durability and interconnection fatigue analysis. The soiling reduction potential of a newly developed glass coating is investigated by outdoor exposure tests in Saudi-Arabia. It is shown by proper choice of materials and optimized interconnection design, the efficiency of the module is increased by 9.58%rel. relative to the reference module. Furthermore, the choice of encapsulant and module design strongly affect the expected service-life, and soiling losses could be reduced up to 35%.

New CUK–SEPIC converter based photovoltaic power system with hybrid GSA–PSO algorithm employing MPPT for water pumping applications

This work proposes a CUK–SEPIC converter for photovoltaic water pumping application with maximum power point tracking (MPPT). For this a new hybrid gravitational search algorithm (GSA)–particle swarm optimisation (PSO) based MPPT method is proposed. The recent power electronics based CUK–SEPIC converter provides identical output with positive and negative ground reference. The novel converter minimises the ripple present in the supply current and provides optimum PV power extraction from solar module by integrating input and output magnetic cores of inductor. The proposed model is practically tested under changing solar insolation by employing switch reluctance motor for water pumping applications.

Assessing uncertainty in the timing of energy use during cost-optimal distributed energy technology selection and sizing

This paper empirically derives uncertainty ranges in cost-optimal solar PV and storage sizing by comparing results from the REopt Lite optimization platform from metered data and a set of simulated Department of Energy Commercial Reference Building (CRB) profiles at 65 sites. We find load profile shape alone does not explain a site’s optimal configurations (i.e., PV, Storage, PV and Storage, No System). Still, load profile shape does introduce uncertainty to optimal PV and storage capacities. Across all cases where PV is part of an optimal configuration, we find the average ratio of power capacities derived from metered loads to capacities derived from CRB profiles to be 0.97 (and as high as 1463), where 1 would be a perfect match in system size. For storage, the ratio is 1.6 (and as high as 42). We also assess how, in the absence of complete metered data, a CRB profile can be selected that would be expected to yield the most similar solar PV and storage capacities. From those metrics that can be available from billing data (i.e., peak demand, monthly load totals), we find that uncertainty is most reduced by selecting the CRB’s with an annual peak occurring at the most similar time, or those with the lowest average root mean square error (RMSE) among monthly peak loads. This research can help improve the implementation and interpretation of results derived from simulated load profiles and is an important next step in advancing smart grid solutions.

Photovoltaic capacity optimization of small and medium-sized hydro-photovoltaic hybrid energy systems considering multiple uncertainties

The integrated operation of PV power plants and hydropower plants is regarded as an efficient and promising approach for large-scale PV power accommodation. This study mainly focuses on studying the optimal sizing of small and medium-sized hydro-PV systems, which are abundant in the northwest and southwest of China but are rarely considered by the existing researches. In this study, a scenario analysis technique, which includes typical scenario classification, the analysis of the inflow and PV power output under each scenario and scenario combination, is proposed to deal with the uncertainties of the inflow of the hydropower plant and the PV power output. On that basis, an optimization model is developed to determine the optimal size of a PV plant complementarily operating with a hydropower plant, where the hydro-PV hybrid system provides the baseload of local power grid and renewable energy curtailment is not allowed. A two-layer nested algorithm is then proposed to solve the model in a hierarchical structure. In the inner layer, an iterative trial calculation method for coupled dynamic programming is used to optimize the PV capacity for the given scenario. In the outer layer, the traversal search algorithm is employed to search for the optimal PV capacity in each scenario, and a confidence level accumulation method is proposed to determine the PV capacity for different confidence levels. The developed model and method are applied to the Naijili hydro-PV hybrid system which is located in Qinghai Province, China. Case studies demonstrate that the optimization of the PV capacity can significantly promote the consumption of renewable energy and effectively mitigate the adverse impact of PV output fluctuations on the power grids. The optimal PV capacities for different confidence levels are obtained, which can make it easy for planners to determine the installed capacity of the PV plant based on their own appetite for risk. Moreover, it is observed that, for small and medium-sized hydro-PV hybrid systems, the impact of capacity expansion of the hydropower plant on the solar energy consumption and the total power generation of the system is not significant.

Scalable Optimization Approach of Optimal PV Sizing Problem for Minimizing Network Loss and Inverter Loss

Scalable Optimization Approach of Optimal PV Sizing Problem for Minimizing Network Loss and Inverter Loss

Study of the application potential of photovoltaic direct-driven air conditioners in different climate zones

The existing calculation and evaluation methods for photovoltaic directly driven air conditioners (PVAC) are often based on a long timescale without considering the short-term rapid fluctuations of environmental parameters. This study proposes a comprehensive method for evaluating real-time zero energy potentials of PVACs. Firstly, an optimal determinationmethod for PV size from the timely energy matching perspective was discussed. Then, the energy generation and consumption based on different time scales in different climate regions were simulated and analyzed. Finally, the zero-energy potentials of PVACs with the optimal PV size in different climate regions were investigated. The potentials were evaluated based on the indices of hourly self-sufficiency, hourly self-consumption, zero energy probability, and PV generation utilization. Besides these indicators, a graphical approach was also provided to enable the visualization of the real-time energy matching performance of PVACs. The results showed that different climatic conditions had significant effects on energy matching performance. Under the premise that the total PV generation can fully meet the electricity consumption of air conditioner, the number of hours that PVAC, without assistance, can realize real-time zero energy consumption only accounts for 26.40% of the whole year in Shenyang and 37.17% in Shanghai. To summarize, the method and indicators for optimizing PV capacity and evaluating dynamic energy matching for different climate zones were proposed from the perspective of achieving real-time zero energy.

Analysis of performance criteria for an optimal PV system configuration

The main objective of this study is to offer an approach to analyze the performance of different three-phase four-wire residential rooftop photovoltaic system topologies. Among different photovoltaic system topologies, this work focuses on the two most used. These two photovoltaic topologies have different design regarding the neutral wire. The first topology includes a DC-link neutral point and the second one incorporates a Delta/Star grounded transformer. Authors conduct a comparative study of both topologies based on the following criteria and constraints: (1) the battery and the DC link capacitors lifetimes; (2) the impact of non linear load on the neutral current; (3) the power quality; (4) the cost of the photovoltaic system topology and (5) voltage and current total harmonic distortion. Analyses are supported by simulation results performed under PSIM software.

Solar PV integration in commercial buildings for self-consumption based on life-cycle economic/environmental multi-objective optimization

In this study, a new multi-objective optimization approach is introduced to optimize economically and environmentally the implementation of solar photovoltaic systems for self-consumption under the scenario of zero export injection. Such a scenario is encountered in several developing countries, but up to the author’s knowledge, no previous study dealt with its detailed assessment and optimization. The problem formulation considers using the levelized cost of electricity (LCOE) and the cumulative environmental benefit (CEB) as two life-cycle conflicting objective functions. First, a multiple linear regression technique was developed to predict accurately the PV power generation under a time resolution of 15 min as recommended for PV-self consumption studies. A new mathematical expression was as well derived to calculate more accurately the CEB considering both the degradation of the PV productivity over the project lifetime and the dynamic evolution of the electricity mix of the country with respect to time. A Genetic algorithm (GA) is used to identify Pareto frontiers and Technique for Order Preference by Similarity to Ideal Solution “TOPSIS” decision-making method is employed to select the final optimum. The followed approach was applied for a large Moroccan commercial structure with an annual electric load of 2.932 GWh. It was found that the recommended optimum PV installed capacity is 847.4 kWp. This optimized design leads to the best compromise between economic and environmental aspects and the LCOE is 15.12 c$/kWh while the CEB is 7.89 tonnes CO2. The corresponding self-sufficiency and self-consumption ratios were estimated at 41.41% and 79.14%, respectively.

Experimental and numerical investigation on thermal performance of PV-driven aluminium honeycomb solar air collector

An experimental analysis and a numerical modelling of a newly designed solar air collector was conducted. The honeycomb structure with large surface area was introduced to the collector to form a blackbody-like heat absorbing core. PV panels were used to drive an electric fan that provide mechanical air circulation without additional energy supply. The effect of irradiance and PV panel coverage ratio on the overall thermal efficiency of the collector were investigated. The system was experimentally tested under irradiance of 200 W/m2 to 600 W/m2 with PV coverage ratio of 15%, 30%, 45%, 60%, 75% and 90%. The thermal behaviour of the system was analysed. It was observed that the honeycomb solar air collector integrated with well-designed PV configuration would be able to achieve suitable thermal efficiency. Over the range of experimental conditions, the maximum instantaneous efficiency reached 64% when PV coverage ratio was 45%. A numerical model was developed and verified, further exploring the thermal efficiency dependency on PV coverage ratio and radiation conditions. The investigation confirmed that there is a theoretical optimum PV coverage ratio that can maximize overall efficiency and the numerical model offered a methodology to predict thermal behaviour of the system under practical conditions.

Optimum PV module interconnection layout and mounting orientation to reduce inhomogeneous soiling losses in desert environments

Photovoltaic modules in desert areas benefit from high irradiation levels but suffer, among other parameters, from high soiling ratios, which influence the output power of the modules. Power loss due to soiling can reach more than 20% per month if not cleaned. Besides, inhomogeneous soiling by accumulation of dust on the corners and bottom of the modules may lead to total power loss of the modules. Modification in cell sizes, electrical interconnection, and mounting orientation of the modules can reduce the losses from inhomogeneous soiling. In this paper, we evaluate the performance of three module designs with full- and half-sized solar cells with different interconnection designs for different inhomogeneous soiling conditions. The modules are investigated in two mounting orientation of portrait and landscape installation with two scenarios of corner and edge soiling. The simulation results supported by the experimental data indicate that the PV modules in favorable mounting positions show up to 65% higher output power depending on the module design and soiling patterns. It is shown that our proposed optimized design mounted in favorable orientation is less sensitive to the inhomogeneous soiling and shows less current losses compared to the nominal operating current. Consequently, the module shows less current mismatch with the other modules in an array.

A visual-inertial system to determine accurate solar insolation and optimal PV panel orientation at a point and over an area

To provide the best return on investments it is often required to assess the suitability of a site for installation of solar photovoltaic panel and quantify shading and atmospheric losses. The shading analysis is generally done using light detection and ranging or 3D geographic information system-based approaches which are cost-effective only for large-scale analysis. In several cases, particularly in developing countries, LiDAR data or 3D GIS are not available. In this study, a terrestrial image-based system is developed to accurately estimate solar insolation at a place. The positions of obstructions obtained using captured images are integrated with sun position model to provide solar insolation and optimal solar panel orientation. To further refine the result, the effect of sky conditions on the obtained solar insolation is also considered at monthly and yearly scale. About 40% reduction in solar insolation is observed due to shading which rose to 51% when the atmospheric conditions were included in the analysis of the selected sites. Further, an approach to estimate solar insolation over an area using some discrete point location is also presented and demonstrated. Results from 30 sites show that the obtained error in insolation estimate over an area is within 4%.

Fast photovoltaic IncCond-MPPT and backstepping control, using DC-DC boost converter

In this paper, we present our contribution in photovoltaic energy optimization subject. In this research work, the goal is to determinate fastly the optimal PV Module working point, allowing maximum power extraction. In this work we use DC-DC Boost converter to control the working point, by adjusting PV voltage trough duty cycle. In order to achieve our goal, we use the combination of incremental conductance MPPT technique and DC-DC Boost converter backstepping control. The validation of this control is made by Matlab simulation; the obtained results prove its effectiveness and its good maximum power tracking dynamics for different irradiance and temperature profiles.

Application of QOCGWO-RFA for maximum power point tracking (MPPT) and power flow management of solar PV generation system

A hybrid technique for solar PV array (SPV) generating system for maximizing the power to load is proposed in this dissertation. The proposed hybrid technique is the joint execution of both the Quasi Oppositional Chaotic Grey Wolf Optimizer (QOCGWO) with Random Forest Algorithm (RFA) and hence it is named as QOCGWO-RFA technique. Here, QOCGWO optimizes the exact duty cycles required for the DC-DC converter of SPV based on the voltage and current parameters. RFA predicts the control signals of the voltage source inverter (VSI) based on the active and reactive power variations in the load side. With this control technique, the system parameter variations and external disturbances are reduced and the load demands are satisfied optimally. The proposed strategy is implemented in MATLAB/Simulink working platform with three different case studies and compared with existing techniques. With these case studies, the proposed technique generates the optimal PV power of 2.1 kW.