Rooftop Photovoltaic Panels Research Articles

Investigation on rooftop PV performance and impact on microclimate in tropical cities ---- A WRF modelling study in Singapore

Building photovoltaic (PV) technologies have been widespread developed. However, the quantitative impact of PV panels on both urban climate and energy balance needs to be investigated. This study adopts the Weather Research and Forecasting model to discuss coupling effects of rooftop PV panels on thermal climate and electricity balance for cooling, and provides installation strategies accordingly. Numerical simulations with five rooftop PV coverage fractions of solar panels for a clear-sky are conducted at a high spatial resolution. Results indicate that rooftop PV panels cool Singapore by a maximum of 1.0 °C during the daytime, and no impact on nocturnal air temperature is observed. PV coverage fractions of 75%–100 % and 75 % are recommended for the North-East area and other four areas to decrease air temperature at least 0.8 °C and 0.4 °C respectively. The electricity offset percentages (EOP) are linearly related to rooftop PV coverage fractions. To maximize the cooling energy offset, 75 % PV coverage is sufficient for most regions of Singapore, while 100 % is recommended for the West and East. By leveraging the dual effects on city cooling and energy balancing, East/West/North-East regions follow the principle of a rooftop PV coverage fraction of 100 %, while 75 % is suggested in the Central/North areas.

Rooftop photovoltaic solar panels warm up and cool down cities

Rooftop photovoltaic solar panels warm up and cool down cities

Behind-the-Meter Load and PV Disaggregation via Deep Spatiotemporal Graph Generative Sparse Coding With Capsule Network.

Nowadays, rooftop photovoltaic (PV) panels are getting enormous attention as clean and sustainable sources of energy due to the increasing energy demand, depreciating physical assets, and global environmental challenges. In residential areas, the large-scale integration of these generation resources influences the customer load profile and introduces uncertainty to the distribution system's net load. Since such resources are typically located behind the meter (BtM), an accurate estimation of BtM load and PV power will be crucial for distribution network operation. This article proposes the spatiotemporal graph sparse coding (SC) capsule network that incorporates SC into deep generative graph modeling and capsule networks for accurate BtM load and PV generation estimation. A set of neighboring residential units are modeled as a dynamic graph in which the edges represent the correlation among their net demands. A generative encoder-decoder model, i.e., spectral graph convolution (SGC) attention peephole long short-term memory (PLSTM), is devised to extract the highly nonlinear spatiotemporal patterns from the formed dynamic graph. Later, to enrich the latent space sparsity, a dictionary is learned in the hidden layer of the proposed encoder-decoder, and the corresponding sparse codes are procured. Such sparse representation is used by a capsule network to estimate the BtM PV generation and the load of the entire residential units. Experimental results on two real-world energy disaggregation (ED) datasets, Pecan Street and Ausgrid, demonstrate more than 9.8% and 6.3% root mean square error (RMSE) improvements in BtM PV and load estimation over the state-of-the-art, respectively.

Shading effect and energy-saving potential of rooftop photovoltaic on the top-floor room

Rooftop photovoltaic panels can serve as external shading devices on buildings, effectively reducing indoor heat gain caused by sunlight. This paper uses a numerical model to analyze rooftop photovoltaic panels' thermal conduction, convection, and radiation in hot summer areas as shading devices. The researcher builds an experimental platform to verify the model, exploring the potential for energy savings of photovoltaic rooftop units in the Wuhan area. The results show that after installing photovoltaic panels, the delay performance of the roof increases by 0.5 h, the roof heat flux is reduced by 41.7%, the peak temperature of the roof is reduced by 22.9 °C, and the daily heat gain is reduced by 74.84%. Finally, this paper discusses the impact of high-reflectance and low-reflectance roofs on the shading effect. The study finds that low-reflectance roofs are more energy-efficient in the hot summer, as high-reflectance roofs lead to a 10.8% increase in indoor heat gain when the photovoltaic panel is installed. Finally, a quantitative method for evaluating the comprehensive potential for energy savings is proposed, considering the electricity generation gain of photovoltaic panels and the comprehensive energy-saving efficiency of photovoltaic roofs, which generates a total potential for energy savings rate of 61.06%. The model presented in this paper provides theoretical guidance for analyzing the comprehensive energy-saving effects of photovoltaic rooftop systems and reveals the potential for energy savings of rooftop photovoltaic panels as external shading devices.

On the local warming potential of urban rooftop photovoltaic solar panels in cities

Understanding and evaluating the implications of photovoltaic solar panels (PVSPs) deployment on urban settings, as well as the pessimistic effects of densely populated areas on PVSPs efficiency, is becoming incredibly valuable. Thus, the deployment of low-efficiency, low-cost, and widely available PVSPs may diminish total solar reflectance, raising the risks of PVSPs-based urban heating, particularly during the summertime heatwaves. This study employs and assesses physical parameterizations that account for the impact of PVSPs on Sydney’s urban environment in the context of the mesoscale model weather research and forecasting (WRF). To account for the impacts of PVSPs, the parameterization presented in this paper assumes that PVSP arrays are parallel, detachable from roofs, and consist of a single layer. Results showed that increasing PVSPs can raise peak summer ambient temperatures by up to 1.4 °C and surface temperatures by up to 2.3°C at city-scale. Temperature variability was found between the city’s eastern and western parts due to the presence of PVSPs. In addition, local warming effects of PVSP were observed at urban district-scale as well. The large-scale deployment of PVSPs at local district-scale of the Sydney during a typical hot day caused air temperature to rise by 1.5 °C during the daytime and decrease by 2.7 °C at nighttime. The patterns of the city’s ambient temperature distribution were found to be strongly dependent on synoptic meteorological conditions and advection flow strength. The maximum increases in sensible heat flux and latent heat flux were 245.5 Wm−2 and 11.5 Wm−2, respectively. Wind speed may be raised by up to 1.2 ms−2 due to regional low effect over city domain. As a result, large-scale deployment of PVSPs promotes advective flow between the city and its environs. Modification of the PVSPs in Sydney results in an increase in planetary boundary layer (PBL) heights of up to 537.9 m above the city and may lower pollutant concentrations at ground level. The advent of sea breeze in the city’s eastern parts, which reduces the temperature of the coastal zone, along with inland westerly winds, which heat the city’s western zones, lessened the intensity of the urban heat island (UHI) phenomenon induced by PVSPs warming. The findings of this study can be used to help policymakers make informed decisions about the use of PVSPs systems. PVSPs with a high solar reflectance in wavelengths that do not convert solar energy to electricity can be considered as an alternative solution to reduce local warming in urban environments.

Rooftop segmentation and optimization of photovoltaic panel layouts in digital surface models

Rooftop photovoltaic panels (RPVs) are being increasingly used in urban areas as a promising means of achieving energy sustainability. Determining proper layouts of RPVs that make the best use of rooftop areas is of importance as they have a considerable impact on the RPVs performance in efficiently producing energy. In this study, a new spatial methodology for automatically determining the proper layouts of RPVs is proposed. It aims to both extract planar rooftop segments and identify feasible layouts with the highest number of RPVs in highly irradiated areas. It leverages digital surface models (DSMs) to consider roof shapes and occlusions in placing RPVs. The innovations of the work are twofold: (a) a new method for plane segmentation, and (b) a new method for optimally placing RPVs based on metaheuristic optimization, which best utilizes the limited rooftop areas. The proposed methodology is evaluated on two test sites that differ in urban morphology, building size, and spatial resolution. The results show that the plane segmentation method can accurately extract planar segments, achieving 88.7% and 99.5% precision in the test sites. In addition, the results indicate that complex rooftops are adequately handled for placing RPVs, and overestimation of solar energy potential is avoided if detailed analysis based on panel placement is employed.

Ceviz üretiminde enerji bilinçli üretim verimliliği

Innovative cultivation and processing facilities are needed in order for the increased production in Türkiye to be efficient, low energy consumption, and high quality. Therefore, an in-depth analysis of the US California-based walnut producer has been made, which can also be an industrial role model to Turkish producers. The orchard of Chandler variety walnut was 70 decares and contains 17-year-old trees. Fuel consumed by agricultural machines during field operation, the rate of meeting the electricity need from solar panels for irrigation and processing, and the data of the hybrid drying (solar wall-natural gas) which is one of the few agricultural applications samples in the world were recorded for sustainable walnut production. In walnut production, spraying (pesticide) had the highest fuel consumption by far compared the all-year applications. The photovoltaic panels in the orchard met 62.8% of the required energy for irrigation engines. To process 42837 kg of walnuts, it was determined that 18.05% of the daily produced energy by roof-top photovoltaic panels was used. It is presenting that walnut production makes a significant direct contribution to the economy as a result of high-quality products with full mechanization and methods of utilizing solar energy technologies in walnut production.

Stochastic optimization of home energy management system using clustered quantile scenario reduction

Recent proliferation of renewable energy has increased the installation of residential energy sources (e.g., roof-top photovoltaic (PV) panel and residential wind turbine) in households. To manage electricity usage incurred by renewable energy and residential load, home energy management systems (HEMSs) provide intelligence to home by real-time monitoring and controlling appliances. In this paper, we propose a novel HEMS framework considering multiple uncertainties from renewable generation and load profiles. First, we generate scenarios of each uncertainty through deep learning. Then, we propose an algorithm called clustered quantile scenario reduction (CQSR) to reduce computation time while preserving the stochastic properties of generated scenarios based on the Wasserstein-1 distance. We prove that solution of CQSR is determined by the number of clustered scenarios. Also, we show provable upper bound of performance degradation incurred by the scenario reduction. Simulation results show that the optimality gap and computation time of the proposed framework is substantially reduced compared to other HEMS algorithms, e.g., by up to 81.4% and 93.7%, respectively. Furthermore, although the original scenarios are generated through different scenario generation algorithms, HEMS using CQSR is less vulnerable to performance degradation incurred by scenario reduction.

Energy storage system impact on the operation of a demand response aggregator

In this paper, we consider a demand response (DR) aggregator responsible for participating in the wholesale electricity market on behalf of the end-users who participated in the DR programs. Thus, the DR aggregator can trade its acquired DR within the short-term electricity markets, i.e., the day-ahead and the balancing (real-time) markets. In the proposed framework, the electricity market prices are considered uncertain, and a robust optimization approach is applied to address the uncertainties to maximize the profit of the DR aggregator. A model for analyzing the impact of the energy storage system (ESS) unit on a DR aggregator's performance is developed to provide more flexibility for the consumers. The direct interactions of a DR aggregator with an ESS are neglected in many models. However, this consideration can lead to improvement in the flexibility of the aggregator and also increase the profit of the entity by trading energy in the short-term markets to charge the ESS during the low-price periods and discharge it to the market while the electricity market prices are high. Hence, it is assumed that the DR aggregator owns an ESS unit and can cover a percentage of its traded power through the ESS. An analysis of the impact of the ESS unit on the DR aggregator's performance is applied to study the most appropriate size of the ESS that can maximize the profit of the aggregator. In addition, renewable energy production is employed for end-users through the installation of rooftop photovoltaic (PV) panels. This demand-side renewable generation can provide more flexibility for the participants in DR programs. Various feasible case studies have been applied to demonstrate the model's effectiveness and usefulness, and conclusions are duly drawn. The numerical results indicate that having an ESS seems necessary when the decision-maker desires to protect its profit from the worst-case scenarios and reduces the negative effect of the uncertain parameter, i.e., the wholesale electricity market prices. Thus, it can be shown that having a greater capacity for the ESS has a significant and direct impact on increasing the profit of the aggregator even in the worst-case scenarios, where the profit rises 20 % when the budget of uncertainty in the robust optimization is equal to 12.

Influence of rooftop mitigation strategies on the thermal environment in a subtropical city

As one of the most promising climate adaptation measures, rooftop mitigation strategies (RMSs) have been studied and practiced in many cities. However, the cooling potential of RMSs may be controversial under different climates. This study establishes city-scale numerical simulations of RMSs, including green roofs (GRs), cool roofs (CRs), rooftop photovoltaic panels (RPVPs), and photovoltaic panels plus green roofs (PVPs+GRs) in Guangzhou, a subtropical city in China, to explore the impact of RMSs on the urban thermal environment during the clear-sky meteorological conditions. The results indicate that RPVPs and PVPs+GRs can cool the city throughout the day, especially from 12 to 17 LST, reaching 0.3–0.7 K, while GRs have the weakest cooling potential, only 0.1 K. The order of cooling space range is PVPs+GRs (the entire city) > RPVPs > CRs (urban and downwind areas) > GRs (urban area). RPVPs and PVPs+GRs mitigate the urban heat island (UHI) effect, reaching 0.5–0.6 K, and increase relative humidity (RH) by 8.5% and 9.6%, respectively. PVPs+GRs and GRs could increase specific humidity (SH) by 0.35 g/kg and 0.23 g/kg, respectively. RPVPs and PVPs+GRs reduce the universal apparent temperature (UAT) throughout the day, especially at night, reaching 0.8 °C (PVPs+GRs) to 0.9 °C (RPVPs), and hence improve human thermal comfort.

AlphaHydrogen: A virtual platform for simulating and evaluating station-based regional hydrogen-electricity networks with distributed renewables, buildings, and fuel-cell vehicles

Building-transportation integrated regional energy networks powered by hydrogen (H2) and electricity has attracted increasing research attention due to its capability to address the urgent challenge of climate change. However, modeling and optimizing H2-electricity networks are difficult due to the inter-disciplinary nature and the lack of convenient and compatible simulation approaches. In this study, an innovative open-sourced virtual platform, AlphaHydrogen, is developed for fast, accurate, and convenient simulation of regional H2-electricity networks constituted by distributed renewable generation, buildings, fuel-cell vehicles, and a H2 station. We first introduced the development of AlphaHydrogen. Then, we demonstrated how to use AlphaHydrogen to simulate a microgrid with 30 houses, rooftop photovoltaic panels, 30 fuel-cell vehicles, and a H2 station with an electrolyzer, an H2 tank, and an onsite fuel cell. We compared the simulation accuracy of AlphaHydrogen with a widely used commercial-licensed software (TRNSYS), and then demonstrated how AlphaHydrogen can be used to optimize the design and operation of a complicated microgrid energy system. Our numerical validation indicates that the simulation differences between AlphaHydrogen and TRNSYS are smaller than 5% in terms of annual energy amounts and grid powers. The optimal sizing of on-site renewable generation and the most suitable rule-based control strategy for the above microgrid system have been discussed using AlphaHydrogen. We believe AlphaHydrogen can promote the application and optimization of H2-electricity hybrid energy system by providing an accurate, open-sourced, compatible, extensible, and easy-to-use virtual testbed.

Dekarbonizacija procesa proizvodnje margarina korišćenjem krovnih fotonaponskih panela i kogeneracije

he paper proposes a methodology for defining the optimal solution for the integrated usage of cogeneration and rooftop photovoltaics. The purpose is to minimize CO₂ emissions in industrial plants. The methodology is founded on the integral use of energy audit techniques (analyzing energy and material flows in a production process and the dependence of electricity and heat consumption on a production volume) and mathematical optimization (linear programming). In accordance with the obtained consumption profiles, technical limitations, and legal regulations, the potential for installing rooftop photovoltaic (PV) panels at appropriate locations and the implementation of appropriate cogeneration technologies were analysed. The proposed methodology was tested on a local margarine production facility. The integrated application of cogeneration (gas engines) and photovoltaics can reduce the current CO₂ emission by 56% if PV panels are installed on the entire accessible rooftop surface, i.e. up to 44.5% if 150 kW PV panels are installed as the new national regulation proposes.

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.

IoT-Based Decentralized Energy Systems

In traditional energy production at large-scale, conventional methods are being used, including fossil fuels. This in turn leads to greenhouse gas emissions (e.g., carbon dioxide or CO2) that cause environmental concerns, but also those traditional methods rely on traditional distribution systems, which are burdened with high transmission losses. This paper focuses on a new concept in the energy sector that undergoes transformation from a traditional centralized system to a decentralized one. In reaching sustainability goals, such as net-zero emissions, the energy sector is incorporating renewable energy sources into the energy system. This requires transformation that combines big conventional energy producers with multiple small- and large-scale energy producers (rooftop photovoltaic panels, wind farms and solar plants) in one system. This enormous transformation is a difficult task, but with recent advancements in information and communication technologies, digitalization, the Industry 4.0 paradigm and Internet of Things technology, it is feasible to achieve. This paper provides a review based on keyword bibliometric analysis, and although it cannot be considered exhaustive or conclusive, it provides a picture of the current international research.

Structural analysis and comparative study of photovoltaic panel mounting systems in Northern Cyprus

Northern Cyprus has made efforts to lessen its reliance on oil products and increase the usage of solar energy and installation of Photovoltaic (PV) panels. The design of lightweight structures, such as PV panel mounting systems, is significantly influenced by the characteristics of wind loads. Inaccurate calculations or a failure to take the wind load into account have recently resulted in substantial financial losses and damage to equipment and structures. In addition, the installation manner has remarkable effects on the output and efficiency of the PV panels. The wind loads on roof-mounted PV panels are examined in this study by considering two different heights for the building and different span lengths based on two loading standards; ASCE 7-16 and TS498, and the results and accuracy of each result are evaluated. Additionally, 64 rooftop PV panel mounting systems were developed to investigate the effects of factors including beam span length, load resisting system, column arrangement, available roof area, and required spacing between arrays. Deflection of the beams, cost of the mounting systems, weight of the mounting systems, and aesthetics of the building after installing PV panels are evaluated in this study.

Thermal and energy benefits of rooftop photovoltaic panels in a semi-arid city during an extreme heatwave event

Rooftop photovoltaic panels (RPVPs) implementation is one of the effective strategies to mitigate urban heat island and relieve urban energy demand with renewable energy resources, which is in need, especially during extreme heatwave events. However, the effects of RPVPs on cooling the urban thermal environment and saving energy have not been fully investigated in terms of RPVPs coverage (CV) and conversion efficiency (CE). To address this question, this study conducted a numerical evaluation of RPVPs on the thermal environment, cooling energy consumption (EC) in buildings, and electricity production (EP) during an extreme heatwave event in a semi-arid Chinese city using the WRF model coupled with building effects parameterization and building energy model. Simulations of twelve scenarios with four CVs and three CEs were conducted. The results indicated that RPVPs implementation could generally lower the 2-m air temperature, due to RPVPs’ higher effective albedo and higher emissivity. An increase in RPVPs CV and CE resulted in stronger cooling effects and larger cooling areas, where the RPVPs with a CV of 100 % and a CE of 0.3 could lower the 2-m air temperature by 0.4 – 0.7 °C, and significantly contributed to a decrease of cooling EC by 14.74 %. The EP could offset and even exceed the cooling EC, with the EP/EC reaching 182.61 %, 135.77 %, and 123.37 % in 100 % coverage (CE = 0.3), 75 % coverage (CE = 0.3) and 100 % coverage (CE = 0.2) scenarios, respectively, indicating that RPVPs could potentially generate enough electricity to compensate cooling EC in semi-arid cities. Our findings have practical implications for the RPVPs implementation and the necessity of improving conversion efficiency for better thermal and energy benefits.

Mind the gap between PV generation and residential load curves: Maximizing the roof-top PV usage for prosumers with an IoT-based adaptive optimization and control module

The necessity to diversification in terms of energy sources, especially for residential consumers, becomes more stringent as the prices of electricity are going up. There are numerous incentives to generate electricity using roof-top Photovoltaic (PV) panels as it reduces the costs, grid dependency and partially replaces gas or other heating source. However, solutions to optimally use the local solar energy for off-grid and on-grid prosumers are incomplete and do not provide an efficient ready-to-use implementation schema. Thus, in this paper, we propose an Adaptive Optimization and Control (AOC) module for residential appliances using Internet of Things (IoT). It consists of forecasting the PV output, optimizing the schedule and controlling the prosumers’ appliances in real-time to maximize the PV usage. This solution provides the layout concept and practical implementation in a real environment. For seasonal simulation, various prosumer profiles (on– and off-grid), with or without storage and different generation capacities are considered. The optimization process brings clear benefits for all types of prosumers, but the real-time monitoring and control algorithm is more valuable as it considers the current PV output and adapts the load in real-time so that to further enhance the Self-Sustainable Ratio (SSR). Furthermore, it protects the battery and prolongs its lifetime by reducing the number of charge/discharge cycles. On average, the SSR increases significantly by 12 or 13%. The SSR goes up to 93%, meaning that the consumption from grid is considerably reduced. More significantly, the impact on the environment and life quality will be consistent as the residential sector represents 27% of the total energy consumption, out of which 62.8% for heating.

Energy Analysis of a NZEB Office Building with Rooftop PV Installation: Exploitation of the Employees’ Electric Vehicles Battery Storage

Near zero energy buildings are increasing worldwide, exploiting low-carbon technologies in heating and electricity self-production. Commercial buildings are increasingly considered as candidates for the installation of smart micro-grids, which may profit from the added storage capacity of the batteries of employees electric vehicles, stationed during daytime in their charging lots. Smart exploitation of the interaction of these electricity sources and sinks may prove essential to address the complex electricity network demand patterns in today’s fast changing energy mixture. The interaction of an efficient office building’s energy system with a big rooftop photovoltaic installation and the aggregate storage capacity of 40 electric cars that are connected in the building’s charging lots is studied by means of transient simulation in TRNSYS environment. The 18-zone building’s heating, ventilation, and air conditioning system, the cars’ batteries, and photovoltaic systems’ interactions are analyzed on a monthly, seasonal, and hourly basis, against the respective demand curves of the Greek network. The results suggest that the specific system’s size may profitably support the operation of a smart micro-grid. The total annual electricity consumption of the building is computed to reach 112,000 kWh, or 20 kWh/m2y. The annual electricity needs of the 40 electric cars, amounting to 101,000 kWh, can be fully met with 30% of the photovoltaic electricity production. Thus, the building becomes a net exporter of electricity to the network, with maximum exported electricity occurring daily between 12:00 and 14:00, which is favorable to meeting the demand curve. Thus, the establishment of smart micro-grids in commercial buildings with large rooftop photovoltaic panels’ capacity and a significant number of electric cars in the employees’ car fleet is quite effective in this direction.

A solar-assisted low-temperature district heating and cooling network coupled with a ground-source heat pump

Towards the development of highly integrated and energy efficient heating and cooling systems, with an energy community perspective, the present paper proposes a novel technical solution for the provision of air-conditioning, domestic hot water and electricity to a small residential district in heating-dominant regions. Three reference climates have been considered: Helsinki, Berlin and Strasbourg. Detailed dynamic models have been created using TRNSYS and NeMo, and long term operations of the energy system, including a new-generation ultra-low temperature district heating and cooling network have been performed. The core of the energy system is the network supplied by a high-efficiency ground source heat pump and used as the source and sink by booster heat pumps installed in the substations. Rooftop photovoltaic thermal panels partially meet the electrical demand of the district, as well as the thermal load for domestic hot water production. Moreover, the panels are cooled by the network, obtaining a reduction in the thermal unbalance to the ground and enhancing their electrical efficiency. This solution allows obtaining high coefficient of performance for the heat pumps in the substations and supply stations, reaching values of 5.4 and 4.0, respectively, for heating provision in the coldest locality. The proposed multi-energy district reaches an electrical self-consumption of 71% in the coldest locality and efficiently combines different renewable energy sources at district level in cold climates.

Towards transactive energy: An analysis of information‐related practical issues

AbstractThe development of distributed energy resources, such as rooftop photovoltaic (PV) panels, batteries, and electric vehicles (EVs), has decentralized the power system operation, where transactive energy markets empower local energy exchanges. Transactive energy contributes to building a low‐carbon energy system by better matching the distributed renewable sources and demand. Effective market mechanisms are a key part of transactive energy market design. Despite fruitful research on related topics, some practical challenges must be addressed. This review surveys three practical issues related to information exchange in transactive energy markets: asynchronous computing, truthful reporting, and privacy preservation. The state‐of‐the‐art results are summarized and relevant multidisciplinary theories are introduced. Based on these findings, several potential research directions are suggested that could provide insights for future studies.