Rooftop Photovoltaic Generation Research Articles

Application of Internet of Things in Residential Distribution Systems

Enabling an internet of things (IoT) application in residential distribution systems by integrating houses with IoT windows and occupant behavior can provide numerous advantages to the power grid, including, but not limited to, demand diminution, congestion reduction, and capacity deferral. This paper presents a new framework that mathematically enables an IoT application in residential distribution systems by integrating IoT windows and occupant behavior with houses for load management and energy conservation. With the proposed framework, we model residential loads considering the IoT concept, and then develop a mathematical optimization model that facilitates the integration of IoT-based houses into the residential distribution system. Different case studies considering a 33-bus distribution network are presented and discussed to demonstrate the effectiveness of penetrating IoT-based houses on distribution system operations and household profitability. It is observed that the profit of the local distribution company decreases when houses are transformed to IoT-based houses due to the fact that less energy is sold to the households. On the other hand, the operation cost of the IoT-based house is lower than that of the conventional house because of the better-managed house energy use, thereby resulting in saving money. It is found that 10% and 20% penetrations of IoT-based houses help reduce the maximum power imported through the distribution substation by 30 kW and 60 kW, respectively. It is also found that the load of IoT-based houses and power availability of a rooftop photovoltaic generation are not compatible, and hence, without an action from the customer and/or utility to coordinate them through a demand response program, IoT-based houses would not contribute to increasing the connectivity of PV-distributed generation in the smart grid.

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

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

Hosting Capacity Assessment Strategies and Reinforcement Learning Methods for Coordinated Voltage Control in Electricity Distribution Networks: A Review

Increasing connection rates of rooftop photovoltaic (PV) systems to electricity distribution networks has become a major concern for the distribution network service providers (DNSPs) due to the inability of existing network infrastructure to accommodate high levels of PV penetration while maintaining voltage regulation and other operational requirements. The solution to this dilemma is to undertake a hosting capacity (HC) study to identify the maximum penetration limit of rooftop PV generation and take necessary actions to enhance the HC of the network. This paper presents a comprehensive review of two topics: HC assessment strategies and reinforcement learning (RL)-based coordinated voltage control schemes. In this paper, the RL-based coordinated voltage control schemes are identified as a means to enhance the HC of electricity distribution networks. RL-based algorithms have been widely used in many power system applications in recent years due to their precise, efficient and model-free decision-making capabilities. A large portion of this paper is dedicated to reviewing RL concepts and recently published literature on RL-based coordinated voltage control schemes. A non-exhaustive classification of RL algorithms for voltage control is presented and key RL parameters for the voltage control problem are identified. Furthermore, critical challenges and risk factors of adopting RL-based methods for coordinated voltage control are discussed.

Investigation of the Effect of Albedo in Photovoltaic Systems for Urban Applications: Case Study for Spain

Rooftop photovoltaic generation can help cities become key players in the transition to clean energy. The optimal solar photovoltaic production on rooftops depends on two angles: tilt angle and azimuth angle. It is accepted in all studies that the ideal orientation of photovoltaic modules is toward the south (north) in the northern hemisphere (south). In contrast, the determination of the optimum tilt angle is more complex, and there are different equations for its calculation. Most of these equations do not take albedo into account. In this work, 47 Spanish province capitals representing the most populated cities have been studied with different equations for the calculation of the optimum annual tilt angle (Technical report by the Spanish Institute for the Diversification and Saving of Energy (IDAE), Lorenzo’s and Jacobson’s equation) and different types of albedo. Accounting for the geographical and the meteorological conditions of the cities, we analyzed the optimum tilt angle through a Mathematica© optimization code. The influence that different variables have on optimum tilt angle has been quantified by means of the term relative energy harvested. The use of the equations as a function of latitude increases the annual relative energy harvested by increasing the albedo. When the albedo is 0.2, the annual relative energy harvested is very similar in all equations. Comparing to the method that maximizes the total irradiation incident on a tilted surface, the minimum and maximum value of the percentage of relative energy harvested per year were 0.01 and 2.50% for the IDAE guideline, 0.00 and 2.38% for Lorenzo’s equation, 0.00 and 2.46% for Jacobson’s equation. A simplified polynomial regression model to estimate optimum tilt angle as a function of latitude, altitude and albedo has been proposed as well.

An estimation framework of regional rooftop photovoltaic potential based on satellite remote sensing images

This paper proposes a comprehensive framework for estimating the regional rooftop photovoltaic (PV) potential. The required rooftop information is extracted from Gao Fen-7 satellite images. In particular, the rooftop area is obtained using a semantic segmentation network. The azimuth and inclination angles are calculated based on the digital surface model. In addition, to improve the accuracy of the economic evaluation, buildings are divided into commercial and industrial buildings and residential buildings. Based on the difference in the roof inclination, the rooftops can be divided into flat roofs, on which the PV panels are installed with the optimal inclination angle, and sloped rooftops, on which the PV panels are installed in a lay-flat manner. The solar irradiation on the plane-of-array is calculated using the isotropic sky translocation model. Then, the available installed capacity and generation potential of the rooftop PV is obtained. Finally, the net present value, dynamic payback period, and internal rate of return are used to evaluate the economic efficiency of the rooftop PV project. The proposed framework is applied in the Da Xing district of Beijing, China, with a total area of 546.84 km2. The results show that the rooftop area and available installed capacity of PV are 25.63 km2 and 1487.45 MWp, respectively. The annual rooftop PV generation potential is 2832.23 GWh, with significant economic returns.

Economic Viability of Rooftop Photovoltaic Systems and Energy Storage Systems in Qatar

Renewable energy sources and sustainability have been attracting increased focus and development worldwide. Qatar is no exception, as it has ambitious plans to deploy renewable energy sources on a mass scale. Qatar may also investigate initiating and permitting the deployment of rooftop photovoltaic (PV) systems for residential households. Therefore, a research gap has been introduced regarding the system design, grid compatibility, economic viability, and energy consumption produced from household rooftop PV systems. Additionally, the lack of supporting policies and a feed-in tariff creates further research and development topics. Therefore, using collected data regarding household power consumption and rooftop PV generation, the purposes of this research study are as follows: (1) determining the economic aspects and practicality of using energy storage systems for self-consumption values; and (2) evaluating the economic viability of rooftop PV systems under different policies and electricity rate schemes. The insights of the results of this study can serve as a stepping stone for decisions and policymakers regarding the application of rooftop PV systems in Qatar. This study utilizes empirical evidence and an economic model to evaluate rooftop PV systems in Qatar and can also be applicable in the middle east region. A few studies in the region produce complementary results, which further supports our findings; however, what makes this paper unique is the use of different economic tools and real collected data while investigating multiple economic and energy policy scenarios.

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

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

Toward smart distributed renewable generation via multi-uncertainty featured non-intrusive interactive energy monitoring

Quantities of advanced technologies have been utilized to realize the carbon neutrality, among which the smart implementations at energy user side are highlighted due to the extensive coverage. Supported by multiple energy management technologies, the integration of distributed renewable energy is prominent, but the compatibility problem of diverse technologies still exists especially considering the newly-developed features. In this paper, an interactive energy monitoring via non-intrusive way is allocated to analyze the roof-top photovoltaic generation and individual appliance consumption from integral energy measurements, and a complete solution is proposed to tackle the distributed generation intelligence with smart voltage maintenance. At first stage, aiming at the domestic reactive power management of photovoltaic inverters, the framework and strategy of coordination between smart meter and intelligent photovoltaic generation are proposed, and then the unified energy signature formulation of photovoltaic generation is presented. Afterwards, the energy disaggregation model is formularized by modified dictionary learning scheme with hybrid validation strategy. Lastly, a sparse coding and auxiliary optimization-based solution is proposed to solve the problem, achieving the robustness under real time architecture. Both the specific low voltage network simulator platform and the practical field measurement scenarios are utilized to verify the effectiveness of the proposed study, and results demonstrate that the proposed study provides an efficient monitoring solution to the intelligent photovoltaic generation, which helps to move toward the smart energy user implementation.

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

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

Assessment of Rooftop Solar Power Generation to Meet Residential Loads in the City of Neom, Saudi Arabia

The economic and social development of the Kingdom of Saudi Arabia (KSA) has led to a rapid increase in the consumption of electricity, with the residential sector consuming approximately 50% of total electricity production. The KSA depends largely on non-renewable energy resources, and the government has produced Saudi Vision 2030. This plan aims to lessen the country’s reliance on fossil fuels and reduce associated problems such as air pollution. Saudi Vision 2030 combines renewable energy and new building designs so that, for example, the planned city of Neom will be net zero energy. This study addresses how best to reduce Neom’s reliance on the national grid through rooftop photovoltaic generation in residential buildings. The study develops a techno-economic model of rooftop PV with battery storage suitable for existing residential building types likely to be built in Neom city (villas, traditional houses, and apartments), and assesses the optimal PV size, battery storage capacity, and optimal orientation of the PV panels. The study used HOMER Pro to compute the Net Present Cost, Levelized Cost of Energy, orientation of PV panels, and optimum PV system size. The optimal size of PV system is 14.0 kW for the villa, 11.1 kW for the traditional dwelling, and 10.3 kW for the apartment, each with a single battery of capacity 12 kWh.

Impact Assessment of High-Frequency Distortions Produced by PV Inverters

Utility engineers, professionals from regulatory agencies, manufacturers and researchers have discussed if high-frequency distortions produced by rooftop photovoltaic generators are a general power quality concern for distribution systems. In this context, this paper presents the results of an extensive research to investigate the emission and propagation characteristics of such high-frequency distortions. Analytical and theoretical analyses, computer simulations, laboratory experiments and field measurements revealed that high-frequency distortions emitted by rooftop photovoltaic inverters are produced with a voltage source characteristic. As a result, the equivalent grid impedance limits and attenuates significantly the high-frequency distortions injection and propagation into the distribution systems. Moreover, the front-end, low-pass filters found in many home appliances also contribute with such attenuation by sinking part of the high-frequency distortions. In summary, the results indicate that these distortions are unlikely to become a generalized power quality concern, so that the front-end filters commonly available for such devices, when properly designed, are enough to deal with this issue.

Data-Driven Game-Based Pricing for Sharing Rooftop Photovoltaic Generation and Energy Storage in the Residential Building Cluster Under Uncertainties

In this article, a novel machine learning based data-driven pricing method is proposed for sharing rooftop photovoltaic (PV) generation and energy storage in an electrically interconnected residential building cluster (RBC). In the studied problem, the energy sharing process is modeled by the leader-follower Stackelberg game where the owner of the rooftop PV system is responsible for pricing self-generated PV energy and operating ES devices. Meanwhile, local electricity consumers in the RBC choose their energy consumption with the given internal electricity prices. To track the stochastic rooftop PV panel outputs, the long short-term memory network based rolling-horizon prediction function is developed to dynamically predict future trends of PV generation. With system information, the predicted information is fed into a Q-learning based decision-making process to find near-optimal pricing strategies. The simulation results verify the effectiveness of the proposed approach in solving energy sharing problems with partial or uncertain information.

System Unbalance Analyses and Improvement for Rooftop Photovoltaic Generation Systems in Distribution Networks

This paper studies the system unbalance caused by rooftop Photovoltaic Generation Systems (PVGSs) in distribution networks and proposes an improved method. The voltage and current unbalance studies for three extreme cases considering all rooftop PVGSs being connected to one single phase and then the stochastic analyses for the integration cases of rooftop PVGSs are considered. These three extreme cases lead to severe system unbalance problems. An improving method that combines the On Load Tap Changer (OLTC) and the optimal phase arrangement of distribution transformers is proposed in this paper to mitigate the system unbalance. The OLTC-based improving method is applied first. If the system unbalance is still out of range, the optimal phase arrangement of distribution transformers is further used to mitigate the system unbalance problem. The objective function of optimal phase arrangement is to minimize the system unbalance with the constraints of voltage limits, line flow limit, etc. Test results show that the proposed method can improve the system unbalance significantly even under very extreme cases.

A comparison of arrangements for increasing self-consumption and maximising the value of distributed photovoltaics on apartment buildings

As subsidised feed-in-tariffs for distributed photovoltaic (PV) generation are reduced or abolished in many jurisdictions, there is growing interest in increasing self-consumption to realise greater value from rooftop PV generation. However, deployment of PV on apartment buildings lags behind other residential deployment despite the potential advantages of load aggregation. We present a study of electricity and financial flows in ten ‘virtual’ Australian apartment buildings under a range of technical implementations and financial arrangements, using real load profiles and simulated generation profiles. Aggregation of diverse household and shared loads, either through an embedded network or ‘behind the meter’ of individual dwellings, can increase self-sufficiency and self-consumption of on-site generation compared to separate systems supplying common property or individual apartments. While embedded networks can enable access to more beneficial retail arrangements, behind the meter solutions may allow residents to avoid regulatory complexities and additional costs. The relative benefits of each arrangement are dependent on building characteristics and financial settings.

Energy Storage Systems Architecture Optimization for Grid Resilience With High Penetration of Distributed Photovoltaic Generation

Renewable generation on the electric power grid is expected to increase in prevalence, but once this technology reaches a certain level of penetration, the grid will not be able to handle the variability and intermittency of the generation without the inclusion of energy storage systems. Simultaneous with this increase in renewables with energy storage, the incidence of disturbance events such as extreme weather that cause significant faults and failures on the power grid is also expected to increase. This research optimizes the architecture of energy storage systems on the electrical power grid for resilience to faults caused by extreme disturbance events under a high penetration scenario for rooftop photovoltaic generation. A grid fault model was applied to simulated generation and demand data to determine the application of storage on the grid that maximizes grid resilience to faults. Failures were simulated at multiple locations on the grid model and combinations of storage architectures were applied to determine the application strategy of energy storage that maximized the resilience of the grid.

Using Bayesian Deep Learning to Capture Uncertainty for Residential Net Load Forecasting

Decarbonization of electricity systems drives significant and continued investments in distributed energy sources to support the cost-effective transition to low-carbon energy systems. However, the rapid integration of distributed photovoltaic (PV) generation presents great challenges in obtaining reliable and secure grid operations because of its limited visibility and intermittent nature. Under this reality, net load forecasting is facing unprecedented difficulty in answering the following question: How can we accurately predict the net load while capturing the massive uncertainties arising from distributed PV generation and load, especially in the context of high PV penetration? This paper proposes a novel probabilistic day-ahead net load forecasting method to capture both epistemic uncertainty and aleatoric uncertainty using Bayesian deep learning, which is a new field that combines Bayesian probability theory and deep learning. The proposed methodological framework employs clustering in subprofiles and considers residential rooftop PV outputs as input features to enhance the performance of aggregated net load forecasting. Numerical experiments have been carried out based on fine-grained smart meter data from the Australian grid with separately recorded measurements of rooftop PV generation and loads. The results demonstrate the superior performance of the proposed scheme compared with a series of state-of-the-art methods and indicate the importance and effectiveness of subprofile clustering and high PV visibility.

Influence of rooftop PV generation on net demand, losses and network congestions: A case study

This paper presents a practicable methodology allowing the impact of rooftop photovoltaic (PV) panels on distribution networks to be thoroughly assessed. Based on granular information (both in space and time) about the network and the demand, and a minimum but realistic set of assumptions concerning the available rooftop surface, hourly scenarios of PV production and consumption are created for increasing PV penetration levels. A load flow solution provides, at the feeder level, the voltage profile, the net power flowing through each feeder section and the total losses. The methodology is then applied to a real case study comprising over 80,000 customers from urban and rural areas, with an annual consumption of 460 GWh and served by over 3600 km of MV&LV lines/cables, large enough to safely extrapolate the attained conclusions to the general case. In a nutshell, network losses increase with respect to the base case scenario, and network congestions begin to show up, when the PV penetration exceeds 30% of the maximum installable power, which is roughly equivalent to 45% of the PV power that yields zero net demand all year round.

Deploying residential rooftop PV units for office building use: a case study in Shanghai

The utilization of distributed power generation system plays an essential role in the design of resilient cities. With the ever increasing of efficiency and decreasing of production cost, Photovoltaic (PV) system is becoming a globally welcomed city component. During the design of PV systems, the matching of demand and supply power curve is critical. This paper evaluates the PV usage potential in different neighborhoods of Shanghai through matching the rooftop PV generation profile with the electricity demands of office buildings. The typical electricity use pattern of an office building is calculated by clustering method with data from a sub-metering platform, and PV generation curve is generated through simulation approach. Case studies were done in 140 neighborhoods of Shanghai City, and theoretical self-utilization ratio and PV utilization of each neighborhood were calculated.

Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration

Proper installation of rooftop photovoltaic generation in distribution networks can improve voltage profile, reduce energy losses, and enhance the reliability. But, on the other hand, some problems regarding harmonic distortion, voltage magnitude, reverse power flow, and energy losses can arise when photovoltaic penetration is increased in low voltage distribution network. Local battery energy storage system can mitigate these disadvantages and as a result, improve the system operation. For this purpose, battery energy storage system is charged when production of photovoltaic is more than consumers’ demands and discharged when consumers’ demands are increased. Since the price of battery energy storage system is high, economic, environmental, and technical objectives should be considered together for its placement and sizing. In this paper, optimal placement, sizing, and daily (24 h) charge/discharge of battery energy storage system are performed based on a cost function that includes energy arbitrage, environmental emission, energy losses, transmission access fee, as well as capital and maintenance costs of battery energy storage system. All simulations are carried out in DIgSILENT and MATLAB linked together. Results show that by using the proposed approach, overvoltage and energy losses are decreased, reverse power flow is prevented, environmental emission is reduced, and economic profit is maximized.

A multi-scale framework for simultaneous optimization of the design and operating strategy of residential CHP systems

The expected increase in energy demand in the United States has led to the pursuit of more efficient methods to generate thermal and electrical energy for the residential sector. One possible approach that could both increase generation efficiency and reduce CO2 emissions is Combined Heat and Power (CHP). CHP plants, powered by natural gas, can act as an integrated residential utility supplier by producing the thermal and electrical energy needed to meet the heating, cooling, and electricity demands of a (future) residential neighborhood. However, a CHP plant operating in island (i.e., grid-disconnected) mode must be optimally sized to maximize efficiency and to lower the capital and marginal costs. In this paper, we describe a novel simultaneous optimization of design and operating strategies for a CHP plant as a utility producer for a residential neighborhood. The plant, operating in island mode, integrates distributed residential photovoltaic solar power generation, and is optimized to meet a time-dependent energy demand profile characteristic of residential energy use. To accurately capture the variability (hourly and seasonal) and uncertainty in residential energy demand and rooftop photovoltaic generation, a vast amount of energy data were incorporated in the problem formulation. The multi-scale optimization problem was solved using a temporal Lagrangean decomposition method, generating the design of a CHP plant that can efficiently meet all residential utility demands, taking into consideration the long-term (design) and short-term (operational) costs.