Photovoltaic Generation Profiles Research Articles

Comparative Study of Performance Metrics for Smart Inverter Control in Solar PV-Based EV Charging Stations

Photovoltaic (PV) panels harness sunlight as a clean and renewable energy source, providing a sustainable alternative to fossil fuels. In India, PV technology is gaining traction due to its environmental benefits and governmental support. This study analyzes typical PV generation profiles, identifying daily and seasonal variations influenced by factors like cloud cover. Additionally, it investigates the effectiveness of three EV charging strategies—uncontrolled, smart, and vehicle-to-home (V2H)—using rule-based and Model Predictive Control (MPC) algorithms. Simulations reveal that MPC consistently outperforms rule-based control in reducing household costs, with V2H charging showing potential for cost savings during peak electricity pricing. However, the efficacy of control strategies varies across seasons, suggesting the need for adaptive optimization approaches.

Probabilistic photovoltaic generation and load demand uncertainties modelling for active distribution networks hosting capacity calculations

With the increasing integration of photovoltaic (PV) systems in active distribution networks (ADNs), accurate modelling of PV power generation and the network demand has become essential, especially for system operators (SO). However, existing studies have focused on deterministic representations of hourly profiles for PV generation and load consumption, which cannot thoroughly evaluate the existing uncertainties of PV power output and load demands. In this study, uncertain parameters load demand and PV power output profile will be modelled with forecasted values, and their profile will be obtained over probability density functions (PDFs). Firstly, a vast quantity of realistic load and PV generation profiles are produced over a day with 15-minute resolution, with a scenario generation method using the Monte Carlo methodology. Afterward, the generated scenarios are reduced to a set of scenarios to represent the span of all generated scenarios. A fully local reactive power regulation strategy is used in this study to evaluate the hosting capacity of the ADN. This proposed method is tested on modified 33-bus and 69-bus distribution test systems by using practical solar generation and load data. The proposed methodology results in the hosting capacity improvement by 20% besides the existing Q-Voltage and PF-Power local voltage control methods, where it has the flexibility to be implemented to any distribution feeder.

Cost-Benefit Analysis of Multiple Shunt Compensators Allocation in PV-Integrated Distribution Networks for Profit Maximization

This article presents the cost-benefit analysis of multiple shunt compensator (SHC) allocations in photovoltaic (PV)-integrated distribution networks for profit maximization. This article strategically places multiple SHCs in a PV-integrated distribution network to minimize energy losses (EL) and maximize profits by providing reactive power compensation. The study evaluates the EL reduction achieved by installing SHCs during a 20-year planning period. It considers the impact of PV integration, load growth, seasonal load, and PV generation profiles on overall system performance. Particle Swarm Optimization (PSO) is employed to identify optimal locations for SHCs to maximize the profit obtained from EL reduction in the network. The results demonstrate that SHCs significantly reduce EL and enhance system performance. The cost-benefit analysis shows that the profit obtained from EL reduction justifies the investment required for SHC installation. Furthermore, the overall profit is higher for scenarios with more SHCs. These findings highlight the importance of carefully balancing PV integration and SHC placement to achieve maximum benefits in PV-integrated distribution networks. This article also evaluates the effectiveness of the PSO algorithm on different distribution networks through a comprehensive statistical analysis.

The Energy Storage System Integration Into Photovoltaic Systems: A Case Study of Energy Management at UTFPR

Energy storage system integration can reduce electricity costs and provide desirable flexibility and reliability for photovoltaic (PV) systems, decreasing renewable energy fluctuations and technical constraints. In this sense, this study aimed to propose energy management strategies through this integration, aiming to improve the demand profile of a university commercial consumer for compensation during peak hours. To achieve the objectives of this research, a pilot project was installed which included a 10 kWp grid-tie PV system (GTPVS), bidirectional inverters, and battery bank, as part of a research and development (R&D) project implemented in the Federal University of Technology–Paraná (UTFPR) Curitiba campus at Neoville headquarters. The methodology consisted of the analysis of variables that interfere with the operation of this system, such as a detailed university profile of demand and consumption, local energy pricing, PV generation profile as well as the interaction between PV generator and the energy storage system, and the variation in power fluctuations throughout the year. Additionally, a technical feasibility study was carried out aimed to determine this consumer’s optimal size through the Homer Grid software. The detailed demand profile allowed us to know in depth the peak values were above 80 kW, for university working days. Furthermore, the daily analysis of the PV generation profile confirmed that although the PV source is quite variable, the scheduled dispatch operation is not interfered by the variability and intermittency of the solar source, as the system was designed and programmed to operate in parallel with the electrical network. Regarding the energy consumed at the university, very significant values were obtained, in which the month of July presented the highest consumption for the base year 2019, with a total monthly value of 48,670 kWh. The established operating strategy for the battery bank with a 10% discharge depth resulted in a life cycle of 2,500 cycles which can extend its useful life by approximately 9.5 years. The results obtained by the simulation show different levels of energy contribution with percentages of 60.0, 33.6, and 2.7%, respectively.

Voltage control in low voltage grids with independent operation of on-load tap changer and distributed photovoltaic inverters

This paper aims to find the optimal setups of voltage control devices in different configurations of Low Voltage (LV) grids with strong PhotoVoltaic (PV) diffusion by performing dedicated simulations. Distributed PV inverters and On-Load Tap Changer (OLTC) are simulated without considering their coordination, to avoid large investments in new communication infrastructures. Thus, each device independently works to decrease voltage deviations in the respective grid connection point. PV generation and consumption profiles are measured and used in two simulated LV grids, connected to the Medium Voltage (MV) grid by a MV/LV transformer with rated powers of 400 and 250 kVA, respectively. The calculation of the optimal devices setups is addressed as a multi-objective problem, considering objectives of voltage quality, grid losses, and OLTC lifespan increase. Multiple simulations are performed by varying the setup of the voltage controls, and considering different positioning and sizes of the generators. In the hardest case, the ratio between the maximum PV power generation and the maximum load in the whole grid is ≈70%. Pareto analysis is carried out to find the non-dominated solutions and TOPSIS is applied to rank the solutions. Finally, a sensitivity analysis is performed by changing the weights attributed to each objective function.

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

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

Comprehensive Analytical Expressions for Assessing and Maximizing Technical Benefits of Photovoltaics to Distribution Systems

The proliferation of photovoltaic (PV) can cause several operational problems in distribution systems. In this paper, comprehensive analytical expressions (CAEs) are proposed for maximizing the technical benefits of multiple PV units to distribution systems considering the uncertainty of PV generation and load profiles. Specifically, the proposed CAEs quantify and optimize the following five vital indices with multiple PV units: 1) active energy losses, 2) reactive losses, 3) voltage deviations, 4) line congestion margin, and 5) voltage stability index. The smart functions of the PV inverter (i.e., reactive power support and active power curtailment) are also incorporated in the CAEs, complying with the revised IEEE 1547:2018 standard. Further, various PV tracking options are considered, including fixed, one-axis, and two-axis trackers. Unlike existing approaches, the CAEs can simultaneously solve the optimal allocation problem of multiple PV units in a direct manner without needing optimization algorithms, iterative processes, or simplifying procedures. The calculated results reveal the high performance of the CAEs in terms of accuracy, flexibility and computational speed while providing further PV planning options. Moreover, CAEs are effectively utilized for two other applications with promising computational performance, i.e., rapid assessment of PV impacts with annual datasets and optimal centralized/decentralized inverter control.

Disaggregating Customer-Level Behind-the-Meter PV Generation Using Smart Meter Data and Solar Exemplars

Customer-level rooftop photovoltaic (PV) has been widely integrated into distribution systems. In most cases, PVs are installed behind-the-meter (BTM), and only the net demand is recorded. Therefore, the native demand and PV generation are unknown to utilities. Separating native demand and solar generation from net demand is critical for improving grid-edge observability. In this paper, a novel approach is proposed for disaggregating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">customer-level</i> BTM PV generation using low-resolution but widely available hourly smart meter data. The proposed approach exploits the strong correlation between monthly nocturnal and diurnal native demands and the high similarity among PV generation profiles. First, a joint probability density function (PDF) of monthly nocturnal and diurnal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">native</i> demands is constructed for customers <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> PVs, using Gaussian mixture modeling (GMM). Deviation from the constructed PDF is utilized to probabilistically assess the monthly solar generation of customers <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">with</i> PVs. Then, to identify <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hourly</i> BTM solar generation for these customers, their estimated monthly solar generation is decomposed into an hourly timescale; to do this, we have proposed a maximum likelihood estimation (MLE)-based technique that utilizes hourly typical solar exemplars. Leveraging the strong monthly native demand correlation and high PV generation similarity enhances our approach's robustness against the volatility of customers’ hourly load and enables highly-accurate disaggregation. The proposed approach has been verified using real native demand and PV generation data.

Analysis of a Multi-Timescale Framework for the Voltage Control of Active Distribution Grids

The development of strategies for distribution network management is an essential element for increasing network performance and reducing the upgrade of physical assets. This paper analyzes a multi-timescale framework to control the voltage of distribution grids characterized by a high penetration of renewables. The multi-timescale solution is based on three levels that coordinate Distributed Generation (DG) and Energy Storage Systems (ESSs), but differs in terms of the timescales and objectives of the control levels. Realistic load and photovoltaic generation profiles were created for cloudy and clean sky conditions to evaluate the performance features of the multi-timescale framework. The proposed solution was also compared with different frameworks featuring two of the three levels, to highlight the contribution of the combination of the three levels in achieving the best performance.

Photovoltaic Self-Consumption in Industrial Cooling and Refrigeration

The industrial sector has a great opportunity to reduce its energy costs through distributed generation. In this sense, the potential of photovoltaic self-consumption systems in the industrial cooling and refrigeration sector is shown. Two industries with photovoltaic self-consumption installations are shown and the electricity consumption profile of this type of industry which has a remarkable basal electricity consumption during daytime is analyzed. The matching between consumption and photovoltaic generation profiles is provided through the self-consumption and self-sufficiency curves considering different reporting periods (monthly and annual). Moreover, a new index is presented: self-sufficiency index for sunshine hours, φSS,SH. This index evaluates the performance of the photovoltaic self-consumption system when facing the consumption only during sunshine hours. This index may complement the self-sufficiency index and may improve the analysis of this type of systems in the industrial sector. Self-consumption indices of 90% may be provided. Moreover, self-sufficiency indices for total (24 h) and for sunshine hours of 25% and 50%, respectively, for industry A, and 26% and 45% for industry B have been obtained. During daytime, half the load consumption in this type of industry may be covered by photovoltaics while achieving high levels of use of the photovoltaic energy generated.

GENERACIÓN DISTRIBUIDA Y AUTOCONSUMO FOTOVOLTAICO. POTENCIAL ENERGÉTICO PARA LAS INDUSTRIAS DE LAS ALMAZARAS EN ESPAÑA

The industrial sector faces a new paradigm of energy offshoring, where distributed generation can play a leading role in reducing energy costs in industries, as well as in its C02 emissions. This work shows the potential that photovoltaic self-consumption systems can present to face part of the consumption in the industries of the agri-food sector, specifically the oil mills. The electrical consumption of this type of industry for an oil mill is analyzed, as well as the level of coupling between the actual consumption profiles and the estimated photovoltaic generation profiles for a given range of powers of the photovoltaic generator. The analysis method is easily extrapolated to any mill located in Spain. Likewise, and given that this type of industry has a very characteristic consumption profile, the results obtained are easily transferable to other oil mills. For the mill analyzed, and from an annual perspective, a level of use of the generated photovoltaic energy of 75% with a self-sufficiency index of 20% has been estimated, highlighting the great potential of this technology, as an energy option in this type of industry, as well as in any other that presents a consumption with little variability.

Assessment and Mitigation of Electric Vehicle Charging Demand Impact to Transformer Aging for an Apartment Complex

Due to the increasing use of Electric Vehicles (EVs), the effect of the EV charging power demand on the reliability of the power system infrastructure needs to be addressed. In apartment complexes, which have emerged as a common residential type in metropolitan areas and highly populated districts, high charging demand could result in substantial stress to distribution networks. In this work, the effect of EV charging power demand in an apartment complex on the aging of the Distribution Transformer (DT) is studied. A methodology based on the stochastic characterization of vehicle usage profiles and user charging patterns is developed to obtain realistic EV charging demand profiles. Based on the modeled EV charging profile and the transformer thermal model, the effect of different EV penetration ratios on DT aging for an apartment complex in the Republic of Korea is studied. Results for an EV penetration ratio of up to 30% indicated that DT aging could be accelerated by up to 40%, compared to the case without EV charging. To mitigate this accelerated DT aging caused by EV charging, the effectiveness of two integration approaches of Photovoltaic (PV) sources was studied. Based on a case study that included a realistic PV generation profile, it was demonstrated that a significant contribution to DT reliability could be achieved via the operation of PV sources. A more apparent contribution of PV integration was observed with an energy storage installation at higher EV penetration ratios. At an EV penetration ratio of 30%, a maximum decrease of 41.8% in the loss-of-life probability of the DT was achieved. The effects of different PV integration approaches and power management details on DT aging were also studied. The results demonstrate that the EV charging demand could introduce a significant level of stress to DTs and that this impact can be effectively mitigated by installing PV sources. These observations are expected to contribute toward the effective planning of power system infrastructures that support the design of sustainable cities with the widespread use of EVs.

The Design and Analysis of Large Solar PV Farm Configurations With DC-Connected Battery Systems

Typically, solar inverters curtail or “clip” the available power from the photovoltaic (PV) system when it exceeds the maximum ac capacity. This article discusses a battery system connected to the dc link of an inverter to recuperate this PV energy. Contrary to conventional approaches, which employ two dc–dc converters, one each for the battery and solar PV system, the proposed configuration utilizes a single dc–dc converter capable of simultaneously operating as a charge controller and a maximum power point tracking (MPPT) device. In addition to improving the overall system capacity factor, increasing the conversion efficiencies, and ensuring MPPT stability, the proposed configuration offers a simple solution for adding energy storage to existing PV installations. With this configuration, the excess power that will otherwise be curtailed due to inverter rating limitations is stored in the battery and supplied to the grid during periods of reduced irradiance. Moreover, a systematic methodology for sizing a dc-bus connected battery to minimize total PV energy curtailed was developed using an annual PV generation profile at the Louisville Gas and Electric and Kentucky Utilities E. W. Brown solar facility at Kentucky. The detailed behavior of the proposed system and its power electronics controls and operations were validated with case studies developed in PSCAD/EMTDC software for variable power generation and PV output power smoothing.

A Coordinated Voltage Control for Overvoltage Mitigation in LV Distribution Grids

The design of intelligent strategies for grid management is a cost-effective solution to increase the hosting capacity of distribution grids without investing in the reinforcement of the grid assets. This paper presents a distributed voltage control algorithm to coordinate Energy Storage Systems (ESSs) and Distributed Generation (DG) in a scenario of high renewable penetration. The proposed control algorithm relies on a dual decomposition approach and aims at mitigating possible voltage rise events occurring in the Low Voltage (LV) grid by solving an optimization problem of power minimization. Instead of using local control strategies, in the proposed solution, the voltage control burden is distributed among all the available resources in the grid, which cooperate to resolve the existing voltage violations. The performance of the developed voltage control has been tested under realistic distribution grid scenarios, using stochastic load profiles together with photovoltaic generation profiles obtained in the presence of both clear sky and cloudy sky conditions. The algorithm is also compared to a strategy that considers only DG management, highlighting the benefits associated to the proposed coordination of DG and Energy Storage Systems (ESSs).

Evaluation of Load Matching Indicators in Residential PV Systems-the Case of Cyprus

Three load matching indicators (self-consumption rate, self-sufficiency rate, loss of load probability) and the CO2 emissions were evaluated for 55 Cypriot households with 3 kWp rooftop photovoltaic (PV) generators. The calculations were performed using 30-minute generation and consumption data from a large scale smart meter project in Cyprus. To investigate the effects of recent advances in local legislation, an analysis for higher PV capacities (5 kWp and 10 kWp) was also performed. The PV generation profiles for 5 kWp and 10 kWp PVs were obtained by scaling the 3 kWp PV generation profiles. The results showed that the self-consumption of the analyzed households varied seasonally, as it was related to their heating and cooling demand. More interestingly, the ratio between the households’ annual electricity generation and demand, formally defined here as generation-to-demand ratio (GTDR), was found to be related to the value ranges of the studied load matching indicators. Hence, on average, households with 3 kWp PV generators annually self-consumed 48.17% and exported 2,415.10 kWh of their PV generation. On the other hand, households with larger PV generators were characterized by a higher GTDR, but lower load matching capabilities. For the cases of 5 kWp and 10 kWp PV generators, the average self-consumption fell to 34.05% and 19.31%, while the exported PV generation was equal to 5,122.47 kWh, and 12,534.90 kWh, respectively. Along with lower load matching capabilities, households that generated more than they consumed were also found to have a lower potential for CO2 emissions reduction per installed kWp within the boundaries of the building. In this context, the GTDR could be used by stakeholders to characterize buildings, infer possible value ranges of more complex indicators and make evidence based decisions on policy and legislation.

Optimal Scheduling of EV Charging at a Solar Power-Based Charging Station

The transition to electric vehicles (EVs) has prodigious plausibility in reducing green house gas (GHG). But EVs acceptance is, however, hindered by several challenges; among them is their avidity for quicker charging at lower price. This article considers a photovoltaic (PV)-powered station equipped with an energy storage system (ESS), which is assumed to be capable of assigning variable charging rates to different EVs to fulfill their demands inside their declared deadlines at minimum price. To ensure fairness, a charging rate-dependent pricing mechanism is proposed to assure a higher price for enjoying a higher charging rate. The PV generation profile and future load request are forecasted at each time slot, to handle the respective uncertainty. An integer linear programming (ILP)-based centralized system is first proposed to minimize the charging price per EV. Due to the larger computational time, we subsequently present two game theoretic algorithms, i.e., game 1 and game 2. In game 1, players are oblivious of upcoming charging requests, whereas in game 2, players consider the future anticipated load to select their charging strategies. The games are shown to converge to a Nash equilibrium. The average unit price of game 2 is shown to be the same as the one of the optimal solution and takes considerably less computation time than the centralized method.

Impacts of Array Orientation and Tilt Angles for Photovoltaic Self-Sufficiency and Self-Consumption Indices in Olive Mills in Spain

Olive mills are extensive in the Mediterranean Basin, and Spain constitutes approximately 45% of global production. The industrial sector faces a new energetic paradigm where distributed generation provided by small renewable energy sources may reduce the dependence from fossil energy sources as well as avoid energy distribution losses. Photovoltaic self-consumption systems can play an important role in confronting this challenge due to their modularity and their decreasing cost. Most of self-sufficiency energy studies are focused on building sector and discussions about the idiosyncrasy of industrial load profiles, and their matching capability with photovoltaic generation profiles can be scarcely found. This work analyzes the potential of photovoltaic self-consumption systems as a function of the array power, array tilt, and orientation angles to face the electric consumption in olive mills. Different recording intervals and reporting periods are considered. Results show that a self-sufficiency index of 40% may be achieved on olive harvest basis. Moreover, due to the load profile particularities, percentage error lower than 1.6% has been found when considering a recording interval of 60 min when matching the olive load consumption and photovoltaic generation profiles. Chosen array tilt and orientation angles may be key parameters to maximize the self-sufficiency index.

Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems

This work proposes a fault detection and imputation scheme for a fleet of small-scale photovoltaic (PV) systems, where the captured data includes unlabeled faults. On-site meteorological information, such as solar irradiance, is helpful for monitoring PV systems. However, collecting this type of weather data at every station is not feasible for a fleet owing to the limitation of installation costs. In this study, to monitor a PV fleet efficiently, neighboring PV generation profiles were utilized for fault detection and imputation, as well as solar irradiance. For fault detection from unlabeled raw PV data, K-means clustering was employed to detect abnormal patterns based on customized input features, which were extracted from the fleet PVs and weather data. When a profile was determined to have an abnormal pattern, imputation for the corresponding data was implemented using the subset of neighboring PV data clustered as normal. For evaluation, the effectiveness of neighboring PV information was investigated using the actual rooftop PV power generation data measured at several locations in the Gwangju Institute of Science and Technology (GIST) campus. The results indicate that neighboring PV profiles improve the fault detection capability and the imputation accuracy. For fault detection, clustering-based schemes provided error rates of 0.0126 and 0.0223, respectively, with and without neighboring PV data, whereas the conventional prediction-based approach showed an error rate of 0.0753. For imputation, estimation accuracy was significantly improved by leveraging the labels of fault detection in the proposed scheme, as much as 18.32% reduction in normalized root mean square error (NRMSE) compared with the conventional scheme without fault consideration.

A regularized state estimation scheme for a robust monitoring of the distribution grid

In this paper, we propose a regularized state estimation (SE) scheme for the distribution grid. The ultimate goal is to track accurately the system state at a faster time scale according to the requirements of the new operational environment. The SE algorithm operates at two different time scales in which the set of available measurements are different. At the main time instants (every 15 min) the set of observations comprises SCADA measurements, pseudomeasurements and micro Phasor Measurement Units (μPMUs). In this case, we resort to a Regularized version of the Normal Equations based SE (R-NESE). In the intermediate time instants, only a reduced number of μPMU measurements is available. To circumvent observability issues, we exploit the fact that the voltage drop in adjacent buses is limited and, on that basis, a regularized weighted total variation estimation (WTVSE) problem is formulated. Then, the impact of in-line voltage regulators (IVLRs) is explicitly taken into consideration and that, forces us to decompose and solve the original SE problem for a number of smaller regions (D-WTVSE). The latter can be iteratively solved by resorting to the Alternating Direction Method of Multipliers (ADMM). Complementarily, we also present a μPMU placement method (μPP) in order to improve the conditioning of the R-NESE problem. This problem can be posed as a mixed integer semidefinite programming model (MISDP) and, thus, can be efficiently solved. The performance of the proposed scheme is numerically assessed on (mostly) a 95-bus distribution system for a number of realistic conditions of noise, load and photovoltaic generation profiles. A number of benchmarks are provided, as well.

Comparative analysis of auction mechanisms and bidding strategies for P2P solar transactive energy markets

The advent of blockchain technology and the increasing penetration of rooftop photovoltaic (PV) systems have presented a new opportunity for peer-to-peer (P2P) energy trading. In such transactive markets, communities may enjoy cheaper electricity prices while supporting locally produced green energy. However, there exists a considerable knowledge gap between market mechanisms and energy exchanges. Challenges arise in the auction process to ensure individual rationality, incentive compatibility, budget balance, and economic efficiency. This paper offers insights for building the foundation of a P2P energy trading market and presents a comparative analysis of auction mechanisms and bidding strategies for P2P solar electricity exchanges in terms of market demand and supply metrics. Auction mechanisms considered in this paper are Discriminatory and Uniform k-Double Auction (k-DA). Impacts of different bidding strategies, including game theoretic approaches, on the economic efficiencies of the P2P transactive energy market are also studied. A simulation case study of 100 participants in a microgrid at various PV penetration levels is presented using typical residential load and solar PV generation profiles.