Photovoltaic Owners Research Articles

IRMAC: Interpretable Refined Motifs in Binary Classification for smart grid applications

Modern power systems are experiencing the challenge of high uncertainty with the increasing penetration of renewable energy resources and the electrification of heating systems. In this paradigm shift, understanding electricity users’ demand is of utmost value to the retailers, aggregators, and policymakers. However, behind-the-meter (BTM) equipment and appliances at the household level are unknown to the other stakeholders mainly due to privacy concerns and tight regulations. In this paper, we seek to identify residential consumers based on their BTM equipment, mainly rooftop photovoltaic (PV) systems and electric heating, using imported/purchased energy data from utility meters. To solve this problem with an interpretable, fast, secure, and maintainable solution, we propose an integrated method called Interpretable Refined Motifs And binary Classification (IRMAC). The proposed method comprises a novel shape-based pattern extraction technique, called Refined Motif (RM) discovery, and a single-neuron classifier. The first part extracts a sub-pattern from the long time series considering the frequency of occurrences, average dissimilarity, and time dynamics while emphasising specific times with annotated distances. The second part identifies users’ types with linear complexity while preserving the transparency of the algorithms. With the real data from Australia and Denmark, the proposed method is tested and verified in identifying PV owners and electrical heating system users. The performance of the IRMAC is studied and compared with various state-of-the-art methods. The proposed method reached an accuracy of 96% in identifying rooftop PV users and 94.4% in identifying electric heating users, which is comparable to the best solution based on deep learning, while the speed of the inference model is a thousand times faster. Last but not least, the proposed method is a transparent algorithm, which can tackle the concerns regarding the agnostic decision-making process when policies prohibit some machine learning methods.

Masculine roles and practices in homes with photovoltaic systems

Privately owned, domestic photovoltaic (PV) panels can play an important role in the transition to a low-carbon society by producing renewable energy for the grid and engaging households in energy management. In both aspects, gender is an issue, leading to the development of new theoretical concepts, <em>e.g</em>. the ‘resource man’, the ‘flexibility woman’ and the ‘mother-multiple’. However, a deeper understanding of the role of masculinity may reveal gender complicity and can inform the design of these technologies to be more inclusive. This ethnographic study investigates masculine roles, practices and domesticities for energy management activities in the home. It examines Danish PV owners as domestic prosumers based on in-depth qualitative interviews of 18 participants in 12 households. The use of PV technologies (and their interfaces) reproduces several masculine domesticities that work both productively and counterproductively with feminine domesticities. Technology and energy management are often situated as male hobbies related to masculine self-expression, identity and homemaking. To unlock the full potential of sustainable energy engagement consumption and practices, both masculine and feminine approaches are needed at the conceptual and practical levels. <em><strong>Practice relevance</strong></em> The successful deployment and operation of domestic PV systems depends on aligning the timing of production and consumption of energy in a household. This involves both traditional male and female domains within a household. Significant gaps in understandings, communication and practices were found in many households due to gendered roles. This compromises the potential of domestic generation of renewable energy. Awareness of gender relations can promote the integration and improve practices by providing households with information about technical and less technical aspects of time-shifting practices. Both feminine and masculine understandings of technology, practices and energy consumption are necessary for a more effective transition to the use of smart energy technologies, and with a more equal distribution of these competencies and practices in households with heterosexual couples. In addition, the design and usability of smart energy technology should embrace the needs of both female and male users.

Solar rebound: The unintended consequences of subsidies

Many jurisdictions use net metering to record power exchanges between residential solar photovoltaic (PV) panels and the grid, thus valuing solar power at the electricity retail rate. However, if over the billing period, production exceeds consumption, the surplus remains freely available for consumption. In Wallonia (Belgium), the combination of net metering and generous subsidies for rooftop PV installation encouraged households to set-up large systems, possibly exceeding their consumption needs. We test this potential rebound effect with a large sample of residential PV installations. We observe that a large proportion of households oversize their installation to benefit from the subsidies and, later consume most of their excess production. The effect is econometrically highly significant. There is thus evidence of a strong increase in energy consumption by residential PV owners, which runs counter to the original policy design.

Agent Based Modelling of a Local Energy Market: A Study of the Economic Interactions between Autonomous PV Owners within a Micro-Grid

Urban Photovoltaic (PV) systems can provide large fractions of the residential electric demand at socket parity (i.e., a cost below the household consumer price). This is obtained without necessarily installing electric storage or exploiting tax funded incentives. The benefits of aggregating the electric demand and renewable output of multiple households are known and established; in fact, regulations and pilot energy communities are being implemented worldwide. Financing and managing a shared urban PV system remains an unsolved issue, even when the profitability of the system as a whole is demonstrable. For this reason, an agent-based modelling environment has been developed and is presented in this study. It is assumed that an optimal system (optimized for self-sufficiency) is shared between 48 households in a local grid of a positive energy district. Different scenarios are explored and discussed, each varying in number of owners (agents who own a PV system) and their pricing behaviour. It has been found that a smaller number of investors (i.e., someone refuse to join) provokes an increase of the earnings for the remaining investors (from 8 to 74% of the baseline). Furthermore, the pricing strategy of an agent shows improvement potential without knowledge of the demand of others, and thus it has no privacy violations.

Network Partition-Based Two-Layer Optimal Scheduling for Active Distribution Networks With Multiple Stakeholders

This article proposes a two-layer optimal scheduling strategy to handle the overvoltage problem in high photovoltaic (PV) power-penetrated distribution networks. The voltage regulators can be classified as the power utility and PV owners, which are referred to as stakeholders. The proposed scheduling strategy includes autonomous optimization layer and coordination optimization layer. In the autonomous optimization layer, a min-max robust game model and a mixed-integer second-order cone programming-based model are respectively proposed to minimize the operating costs of PV stakeholders and the power utility stakeholder. A parallel optimization is employed to solve the two models in the autonomous optimization layer. In the coordination optimization layer, a noncooperative game-based model is presented to coordinate scheduling solutions of each stakeholder. Finally, an actual 10 kV, 106-bus feeder in Zhejiang Province, China, and a modified IEEE 123-bus distribution system are employed to verify the feasibility and effectiveness of the proposed approach.

The potential for community financed electric vehicle charging infrastructure

Charging infrastructure is one of the key ingredients necessary to further increase the market uptake of plug-in electric vehicles (PEVs). Most public charging infrastructure in Europe is not profitable, and 42% of European households reside in dwellings with no possibility to install a private charger, which are key limitations to the build-up of charging infrastructure. This work presents and analyzes an alternative business model for ‘community-owned charging points’ that combines many of the convenience advantages of private charging with the cost savings of a joint purchase. Survey data from 3,131 households in Austria is used to identify barriers to PEV charging and charging infrastructure usage, and investigate consumers’ preferences with respect to community-owned charging points. Four key target groups of customers for such a business model are identified based on their socio-economic characteristics, purchase history, and expressed level of environmental concerns, including current PEV owners, high income households, photovoltaic (PV) owners, and environmentalists. Identified distinct preferences for the configuration of a collective charging station amongst these groups include lower importance attached to flexibility to charge the vehicles at any time by current PEV owners, higher sensitivity to cost per charge of PV owners, and strong preferences for comfort and flexibility demonstrated by high income households.

Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model

Inherent variability in photovoltaic (PV) and associated impacts on power systems is a challenging problem for both the PV owners and the grid operators. Existing statistical and machine learning algorithms typically work well for weather conditions similar to historical data. However, uncertain weather conditions pose a great challenge to the estimation accuracy of the estimation models. With the enhanced integration of intelligent electronic devices and the realization of associated automation in the power grid, renewable energy data are becoming more accessible, which can be utilized by deep learning models and improve the PV power generation estimation accuracy. In this article, a hybrid deep learning model driven by external weather data is proposed to do day-ahead PV output forecasting at 15-min interval. The proposed model is motivated by the recent advancement of long-short-term-memory networks and autoencoder, which estimates uncertainties in sequence while making the prediction for complex weather conditions. Meanwhile, the persistence model is used to predict continuous sunny weather conditions. The forecasting result is validated with data from multiple locations.

Danish PV Prosumers’ Time-Shifting of Energy-Consuming Everyday Practices

Consumer engagement in the energy system is necessary to ensure a low-carbon transition. However, this has proved difficult because consumers are engaged in pursuing everyday practices rather than focusing on abstract questions of energy. Recent studies have suggested that being a prosumer can make a difference. This paper builds on survey data from a representative sample of 2505 photovoltaic (PV) owners in Denmark combined with 12 qualitative in-depth interviews. The results indicate that PV owners consider that they have become more concerned about energy consumption and adjust the timing of their everyday practices to their production. Thus, 67% of the households ‘often’ or ‘always’ time-shift the use of washing machines to their production. The extent to which households time-shift is strongly related to their net-metering scheme. Thus, 75% of the households on hourly metering stated that they ‘to some’ or to ‘a great extent’ adjust their consumption, compared to only 26% of the households on annual metering. This financial effect is interpreted in an everyday life context where financial gain transfers meanings of self-sufficiency and sustainability, rather than primarily being viewed as rational economic behaviour. The conclusion discusses the policy implications of methods to engage the consumer.

A Novel Peer-to-Peer Local Electricity Market for Joint Trading of Energy and Uncertainty

In the future power system, an increasing number of distributed energy resources will be integrated including intermittent generation like photovoltaic (PV) and flexible demand like electric vehicles (EVs). It has been long thought to utilize the flexible demand to absorb the PV output locally with the technical solution proposed while an effective commercial arrangement is yet to be developed due to the significant uncertainty associated with local generation. This paper proposes a peer-to-peer (P2P) local electricity market model incorporating both energy trading and uncertainty trading simultaneously. The novelty is to match the forecast power with demand having time flexibility and the uncertain power with demand having power flexibility. This market enables more PV uncertainty to be balanced locally rather than propagating to the upper layer system. In the test case, 55.3% of PV forecast error can be balanced locally in the proposed joint market. In comparison, 43.6% of PV forecast error is balanced locally when the forecast power and uncertain power are traded separately in a day-ahead market and a real-time market. The proposed P2P market can also motivate PV owners to improve forecast accuracy.

Bi‐level optimisation dispatch method for photovoltaic hosting capacity enhancement of distribution buses

Distributed photovoltaic (PV) systems are being gradually integrated into distribution systems with the advantage of economy, environmental protection, and flexibility. Hosting capacity (HC) of a distribution system limited the integration of the PVs. To identify the economic efficiency of HC, two boundaries of HC for distribution buses are proposed considering the technology and economy. Then, new evaluation metrics that can identify the HC region for the PV buses are presented. A bi-level optimisation dispatch method based on iterative particle swarm optimisation for PVHC enhancement is proposed considering the benefits of both distribution network operators and PV owners. At the upper level, a linear power flow-based model is used to determine the economic PVHC and technological PVHC. At the lower level, the objective function of the maximisation of benefits for PV owners is presented. The punishment coefficient of the PVHC region is incorporated into the optimisation dispatch model. Finally, the demonstration is performed on modified PG&E 69-bus and IEEE 123-bus distribution systems. The simulation results show that the proposed bi-level optimisation dispatch method can effectively increase the PVHC margin of distribution buses, and the presented evaluation metrics are of great practical significance in a deregulated environment.

Performance of roof-top PV systems in Germany from 2012 to 2018

The photovoltaic (PV) sector is a central pillar for the global energy transition process which aims to reach the climate change mitigation goals. Roof-mounted systems in particular can make a significant contribution to greenhouse gas-free energy generation. By the end of 2018, more than 1.5 million rooftop systems have been installed in Germany. The yield data measured by monitoring systems and provided by web-based online systems from 2012 to 2018, as well as the configuration information of 23,944 PV systems, are evaluated in this publication. Long-term indicators are derived from the spatial and temporal distribution of the yield data. The yield analysis shows a typical south-north gradient in Germany. 2018 was the year with the highest yield, 2013 the year with the lowest. The annual specific yield varies between 816 kWh/kWp in 2017 at the Baltic Sea and 1049 kWh/kWp in 2018 in Bavaria. If analysis of data evaluation is narrowed to one to two years, a deviation of five percent and more from the long-term yield can be observed. In order to obtain detailed information on the quality of PV systems, at least five years of data are required. The yield data evaluation can be used by PV system operators and owners to identify an undersupply of their PV system and to initiate countermeasures.

Reactive Power Cost From PV Inverters Considering Inverter Lifetime Assessment

Photovoltaic (PV) system inverters have been frequently utilized for reactive power support in the literature. Although the benefits of PV reactive power for the grid have been quantified, the costs incurred by the PV owner have largely been ignored. This paper combines the findings from power electronics research and power system economics to formulate the cost of reactive power from PV inverters, considering the inverter degradation due to the reactive power provision. One-year simulations with real weather and load data for the case of Singapore for different levels of PV penetration have been conducted. Subsequently, the benefits of local reactive power provision to the system and its effect on the lifetime of PV inverters were analyzed. It was found that the cost of inverter lifetime reduction is a significant part of the reactive power cost (more than 50% at lower PV penetration), but decreases at higher PV penetration when the reactive power support is distributed over more PV systems. Through comprehensive analysis, the feasible range for monetary incentives for PV reactive power support has been identified. The results from this paper can, therefore, be used as a foundation for future research, formulating regulations, and for market analysis.

Defining Customer Export Limits in PV-Rich Low Voltage Networks

The growing adoption of residential photovoltaic (PV) systems around the world is presenting distribution network operators (DNOs) with technical challenges, particularly on low voltage (LV) networks. The need to mitigate these issues with simple yet effective measures in countries with high PV penetrations is likely to drive the adoption of limits on the very exports that affect this infrastructure. Defining the most adequate limit, however, requires understanding the tradeoffs between the technical benefits and the effects on PV owners. This paper proposes two methodologies: an optimal power flow (OPF) based technique to define the export limit that solves technical problems with minimal curtailment, and a Monte Carlo based analysis to investigate the spectrum of such tradeoffs considering different PV penetrations and export limits. A real U.K. residential LV network with 180 customers is analyzed using realistic 1-min resolution daily load and PV generation profiles across seasons. Results demonstrate that, for DNOs, the OPF-based approach is effective in determining the most technically adequate export limit. However, for policy makers, the spectrum of tradeoffs provided by the Monte Carlo approach can help defining export limits that reduce curtailment at the expense of partially mitigating technical issues.

Analysis of Rooftop Photovoltaics Diffusion in Energy Community Buildings by a Novel GIS- and Agent-Based Modeling Co-Simulation Platform

The present work introduces an empirically ground agent-based modeling (ABM) framework to assess the spatial and temporal diffusion of rooftop photovoltaic (PV) systems on existing buildings of a city district. The overall ABM framework takes into account social, technical, environmental, and economic aspects to evaluate the diffusion of PV technology in the urban context. A city district that includes 18 720 households distributed over 1 290 building blocks and a surface area of 2.47 km2 is used to test the proposed ABM framework. Results show how the underlying regulatory framework (i.e., the rules of the internal electricity market) influences the pattern and intensity of adoption, thus realizing different shares of the available potential. Policies that support the establishment of `prosumers' within Condominiums (i.e., energy community buildings), and not in single-family houses only, is key to yield high diffusion rates. The installed capacity increases by 80% by switching from the one-to-one configuration to the one-to-many paradigm, i.e., from 5.90 MW of rooftop PV installed on single-family households and/or single PV owners to 10.64 MW in energy community buildings. Moreover, the possibility to spread the auto-generated solar electricity over the load profile of the entire population of Condominium results in self-consumption rates greater than 50% and self-sufficiency ratios above 20% for the majority of the simulated buildings.

Improved Model for Forecasting Global Solar Irradiance During Sunny and Cloudy Days

Abstract A precise estimation of the electrical energy produced daily by photovoltaic (PV) systems is important both for PV owners and for electrical grid operators. It can be achieved if the received solar irradiance can be accurately estimated during any type of daily solar profile (clear, cloudy, mixed sky), not only average solar profile for larger periods of time, e.g. one month or season, as used in PV system design. The paper firstly describes an existing mathematical model, based on the Meliss approach, which uses mean monthly coefficients for estimating average direct and diffuse solar irradiance. This model is satisfactory for monthly/annual intervals but is not useful for daily estimations. Therefore in the second part of the paper an algorithm which allows to generate daily variations of the model’s coefficients for clear and cloudy sky conditions is proposed. The improved model with variable coefficients was tested during several representative days and can be used for estimating the effect which different meteorological conditions as fog/dew/frost have on the quantity and quality of the solar irradiance received by a PV convertor.

A New Remote Tap Position Estimation Approach for Open-Delta Step-Voltage Regulator in a Photovoltaic Integrated Distribution Network

With the rapid development of photovoltaic (PV) technology, more utility-scale PV systems are being integrated into rural areas where abundant and low-cost land is available. In these areas, step-voltage regulators (SVRs) with an open-delta configuration are widely used for voltage regulation. However, since SVRs were initially designed for tackling slow and smooth load variations, frequent PV output fluctuations can cause excessive tap changes. Moreover, rural communication facilities are normally expensive and underdeveloped, so only limited parameters are recorded with low resolution and reliability. Thus, much needed tap information cannot be properly monitored in a cost-effective way. In contrast, a downstream load center or utility-scale PV site is commonly well equipped with modern sensors and data acquisition systems, which provide the possibility for tap evaluation. Therefore, this paper proposes a new remote tap position estimation approach to accurately assess the upstream tap positions of open-delta SVRs from downstream measurements. Furthermore, the proposed approach is firmly validated through field testing under the support of the local utility and PV plant owner. This novel method not only enables reliable tap position monitoring with high precision for utilities to research PV-SVR interaction, but also provides valuable information to PV owners for correctly analyzing downstream voltage performance.

Real-Time Coordinated Voltage Control of PV Inverters and Energy Storage for Weak Networks With High PV Penetration

There are more large-scale photovoltaic (PV) plants being established in rural areas due to availability of low-priced land. However, distribution grids in such areas traditionally have feeders with low X/R ratios, which makes the independent reactive power compensation method less effective on voltage regulation. Consequently, upstream step voltage regulator (SVR) may suffer from excessive tap operations with PV-induced fast voltage fluctuations. Although a battery energy storage system (BESS) can successfully smooth PV generation, frequent charge/discharge will substantially affect its cost effectiveness. In this paper, a real-time method is designed to coordinate PV inverters and BESS for voltage regulation. To keep up with fast fluctuations of PV power, this method will be executed in each 5 s control cycle. In addition, charging/discharging power of BESS is adaptively retuned by an active adjustment method in order to avoid BESS premature energy exhaustion in a long run. Finally, through a voltage margin control scheme, the upstream SVR and downstream PV inverters and BESS are coordinated for voltage regulation without any communication. This research is validated via a real-time digital simulator MatLab cosimulation platform, and it will provide valuable insights and applicable strategies to both utilities and PV owners for large-scale PV farm integration into rural networks.

Optimal battery storage operation for PV systems with tariff incentives

Many efforts are recently being dedicated to developing models that seek to provide insights into the techno-economic benefits of battery storage coupled to photovoltaic (PV) generation system. However, not all models consider the operation of the PV – battery storage system with a feed-in tariff (FiT) incentive, different electricity rates and battery storage unit cost. An electricity customer whose electricity demand is supplied by a grid connected PV generation system benefiting from a FiT incentive is simulated in this paper. The system is simulated with the PV modelled as an existing system and the PV modelled as a new system. For a better understanding of the existing PV system with battery storage operation, an optimisation problem was formulated which resulted in a mixed integer linear programming (MILP) problem. The optimisation model was developed to solve the MILP problem and to analyse the benefits considering different electricity tariffs and battery storage in maximising FiT revenue streams for the existing PV generating system. Real data from a typical residential solar PV owner is used to study the benefit of the battery storage system using half-hourly dataset for a complete year. A sensitivity analysis of the MILP optimisation model was simulated to evaluate the impact of battery storage capacity (kWh) on the objective function. In the second case study, the electricity demand data, solar irradiance, tariff and battery unit cost were used to analyse the effect of battery storage unit cost on the adoption of electricity storage in maximising FiT revenue. In this case, the PV is simulated as a new system using Distributed Energy Resources Customer Adoption Model (DER-CAM) software tool while modifying the optimisation formulation to include the PV onsite generation and export tariff incentive. The results provide insights on the benefit of battery storage for existing and new PV system benefiting from FiT incentives and under time-varying electricity tariffs.

Control algorithm for a residential photovoltaic system with storage

High penetration of photovoltaic (PV) electricity could affect the stability of the low-voltage grid due to over-voltage and transformer overloading at times of peak production. Residential battery storage can smooth out those peaks and hence contribute to grid stability. A feed-in limit allows for the easy setting of a maximum power injection cap and motivates PV owners to increase their self-consumption. A simple control strategy for a residential battery system coupled with a PV system that maximizes self-consumption and minimizes curtailment losses due to a feed-in limit is presented. The algorithm used in this strategy does not require a forecast of insulation conditions. The performance of this algorithm is compared to a second algorithm—a control strategy based on linear optimization using a forecast. Assuming an exact forecast, this second algorithm is very close to the maximum self-consumption and minimum curtailment losses achievable and can be used to benchmark the simple strategy. The results show that the simple strategy performs as well as the second algorithm with exact forecasts and performs significantly better than the second algorithm using real forecasts. Moreover, it is shown that this result is valid for a large range of storage capacities and PV sizes. Furthermore, it is shown that with a time resolution of 15min for the input data (the resolution of most PV production and load data) self-consumption is overestimated by about 3% and curtailment losses are underestimated by the same amount. Load sensitivity simulations show that different load curve shapes do not fundamentally change the results. Finally, to assess the effect of load aggregation, the case where the strategy is applied separately to 44 households with storage is compared to the case where it is applied to a centralized storage system of the same size as the total storage of the 44 households. The reduction of the curtailment losses with the number of aggregated houses is showed.

Quantification and Mitigation of Unfairness in Active Power Curtailment of Rooftop Photovoltaic Systems Using Sensitivity Based Coordinated Control

With increasing photovoltaic (PV) penetration in low voltage networks (LVNs), voltage regulation is a challenge. Active power curtailment (APC) is one possible solution for mitigating over voltages resulting from active power injection in LVNs. There is an inherent unfairness in the APC scheme. When generation is high and consumption is low, the voltages at the end of the feeder tend to be the highest. This results in high curtailment of active power output of the inverters located at the end of the feeder and low or even no curtailment for the inverts located closer to the transformer. A secondary voltage controller has been implemented to mitigate this unfairness in APC based voltage support schemes. The focus of this work is to quantify this unfairness and develop methods that enable residential PV owners serviced by the same feeder to participate equally in voltage regulation in the LVN.