Photovoltaic Self-consumption Research Articles

Design and Energy Analysis of Photovoltaic-Battery Prototype Considering Different Voltage Levels

Photovoltaic self-consumption systems are effective at reducing energy consumption from fossil fuels and carbon emissions. Incorporating energy storage into these systems enables improved energy management and the optimization of their operation. However, to date, few studies have evaluated and compared the energy performance of PV systems with battery storage. In this context, with the current development of High Voltage batteries, research is needed on energy storage at different voltage levels incorporated into PV systems for self-consumption. In this way, the design and operation of an experimental prototype are described, consisting of two photovoltaic systems for self-consumption with energy storage using batteries operating at different voltages. One of them operates at low voltage (Low Voltage Installation, LVI) and the other at high voltage (High Voltage Installation, HVI). Through experimentation, it was demonstrated which installation is more efficient. During commissioning, the results showed the importance of considering the voltage level parameter in a renewable energy production system for self-consumption, since the energy yield of the HVI inverter-battery set was higher than in the case of the LVI on almost all of the days of the month studied. In addition, both systems showed a strong dependence on weather conditions, causing higher energy losses in their components during days with lower solar energy production. Therefore, the need for further analysis of energy efficiency to optimize the integration of these systems into the building sector was demonstrated.

Experimental tests on the performance of an economic model predictive control system in a lightweight building

• Economic MPC was tested in a lightweight laboratory. • Effect of HVAC system models accuracy was discussed. • MPC improved PV self-consumption and operating costs compared to thermostat. • Oversimplified models led to higher thermal discomfort and increased consumption. European policies are fostering the electrification of energy use, including space heating and cooling systems, in order to decarbonise the building stock. The significant penetration of electrical loads and domestic photovoltaic (PV) plants has therefore become an important topic for researchers and engineers working in the building sector. In this context, this paper presents a recently constructed laboratory for testing efficient management strategies in all-electric houses. The article describes the laboratory and the Model Predictive Control (MPC) strategy developed to minimize economic costs for space heating while ensuring thermal comfort in the indoor environment with a simulated rooftop PV system. The proposed controller leverages prior knowledge about the physical and geometric properties of the building and the optimization problem is formulated using mixed-integer quadratic programming. This article reports the results of calibration and optimization performed in open loop, as well as two closed loop tests where the MPC controls the HVAC system using receding horizon. The predictive controller was able to substantially increase PV self-consumption in both tests compared to a conventional thermostat, thus cutting electricity costs for heat pump by 10–17%. Such improvement was obtained at the price of a higher thermal comfort violations, mainly due to oversimplified HVAC system models. In light of these findings, the article analyses the effect of such simplifications and suggests possible alternative modelling approaches.

Experimental performance analysis of an installed microgrid-based PV/battery/EV grid-connected system

Abstract Global energy demand, which is largely based on fossil fuels, is expected to increase rapidly. An effort must be made to mitigate carbon emissions and climate change to ensure sustainable and clean development. In recent years, the increasing share of renewable energy and energy-storage systems, the development of electric vehicles (EVs), promotion of energy efficiency and demand-side management (DSM) have become today’s solution technologies. The microgrid (MG), which involves the interconnection of several generation and storage units capable of operating locally with or without connection to the power grid, is also a very useful emerging technology. This study allowed the experimental operation and performance analysis of a grid-connected photovoltaic (PV)/battery/EV MG hybrid system, which was used for maximizing PV self-consumption and DSM objectives. The entire MG consisting of two subsystems (polycrystalline PV array of 2.16 kWp for Subsystem 1, monocrystalline PV system of 2.4 kWp for Subsystem 2, EV with lithium-ion battery capacity of 6.1 kWh) was installed under outdoor conditions at the University Institute of Technology in Mulhouse, France in August 2018. The operation and behaviour of the system components, including the inverter, batteries and power grid, were analysed in both scenarios with and without EV connection. The results shows that the total cumulative energy injected into the grid during the entire system operating cycle is estimated to be 3466.82 and 5836.58 kWh for Subsystems 1 and 2, respectively. In addition, the energy produced by Subsystem 2 during its lifetime and the emissions emitted are respectively estimated at 5597.65 kWh and 4.17 tons. The other results in terms of PV output power, energy yield, feed-in power and self-consumed energy were quantified and analysed in detail.

Democratization of PV Micro-Generation System Monitoring Based on Narrowband-IoT.

Power system configuration and performance are changing very quickly. Under the new paradigm of prosumers and energy communities, grids are increasingly influenced by microgeneration systems connected in both low and medium voltage. In addition, these facilities provide little or no information to distribution and/or transmission system operators, increasing power system management problems. Actually, information is a great asset to manage this new situation. The arrival of affordable and open Internet of Things (IoT) technologies is a remarkable opportunity to overcome these inconveniences allowing for the exchange of information about these plants. In this paper, we propose a monitoring solution applicable to photovoltaic self-consumption or any other microgeneration installation, covering the installations of the so-called ’prosumers’ and aiming to provide a tool for local self-consumption monitoring. A detailed description of the proposed system at the hardware level is provided, and extended information on the communication characteristics and data packets is also included. Results of different field test campaigns carried out in real PV self-consumption installations connected to the grid are described and analyzed. It can be affirmed that the proposed solution provides outstanding results in reliability and accuracy, being a popular solution for those who cannot afford professional monitoring platforms.

Net-metering and net-billing in photovoltaic self-consumption: The cases of Ecuador and Spain

Solar Photovoltaics has become one of the cheapest sources of electricity, with the potential to expand further through distributed generation. PV self-consumption can empower electricity customers, transforming them into prosumers, but its success relies on appropriate regulations, especially in the treatment of surplus electricity, being net-metering and net-billing the most common remuneration mechanisms. As the number of such PV systems increases, regulation needs to evolve and may affect profitability.The purpose of this research is to take advantage of the Spanish experience, establishing key points that can improve the regulation in Ecuador and other countries. The operation of these remuneration schemes is studied through an economic analysis for a wide range of residential customers, highlighting the low profitability of small PV installations, which are not profitable for the average consumer in Ecuador. Grid parity is reached in Spain over 3 kW and in Ecuador only for the highest electricity consumers. Although the net-billing model is more effective in promoting PV self-consumption in the medium and long term, it requires additional conditions, such as those in Spain. Some recommendations are proposed, e.g. additional support with progressive tax reductions, simplication of permits and the introduction of collective self-consumption.

Analysis of photovoltaic self-consumption systems for hospitals in southwestern Europe

A proposal is made for the energy modernization of a group of hospitals in south-western Europe, through the installation of photovoltaic self-consumption systems based on their electricity consumption patterns. For this purpose, the energy demand of these hospitals was examined in detail, self-consumption photovoltaic systems were designed with simulation software and the energy and environmental results were studied. The average annual consumption curve of the hospitals favoured the optimal exploitation of the photovoltaic systems, obtaining very high self-consumption rates. Furthermore, by using 30–50% of the roof surface, an average of 25–30% of the annual electricity demand could be covered and potentially reduce CO2 emissions. Thus, the incorporation of photovoltaic systems for self-consumption in hospitals located in a Mediterranean climate is a very interesting solution.

An approach to implement photovoltaic self-consumption and ramp-rate control algorithm with a vanadium redox flow battery day-to-day forecast charging

The variability of the solar resource is mainly caused by cloud passing, causing rapid power fluctuations on the output of photovoltaic (PV) systems. The fluctuations can negatively impact the electric grid, and smoothing techniques can be used as attempts to correct it. However, the integration of a PV+VRFB to deal with the extreme power ramps at a building scale is underexplored in the literature, as well as its effectiveness in combination with other energy management strategies (EMSs). This work is focused on using a VRFB to control the power output of the PV installation, maintaining the ramp rate within a non-violation limit and within a battery state of charge (SOC) range, appropriate to perform the ramp rate management. Based on the model simulation, energy key-performance indicators (KPI) are studied, and validation in real-time is carried. Three EMSs are simulated: a self-consumption maximization (SCM), and SCM with ramp rate control (SCM+RR), and this last strategy includes a night battery charging based on a day ahead weather forecast (SCM+RR+WF). Results show a battery SOC management control is essential to apply these EMSs on VRFB, and the online weather forecast proves to be efficient in real-time application. SCM+RR+WF is a robust approach to manage PV+VRFB systems in wintertime (studied application), and high PV penetration building areas make it a feasible approach. Over the studied week, the strategy successfully controlled 100% of the violating power ramps, also obtaining a self-consumption ratio (SCR) of 59% and a grid-relief factor (GRF) of 61%.

Can phase change materials in building insulation improve self-consumption of residential rooftop solar? An Australian case study

This work investigates the extent to which phase change material (PCM) in the building's envelope can be used as an alternative to battery storage systems to increase self-consumption of rooftop solar photovoltaic (PV) generation. In particular, we explore the electricity cost-savings and increase in PV self-consumption that can be achieved by using PCMs and the operation of the heating, ventilation, and air conditioning (HVAC) system optimised by a home energy management system (HEMS). In more detail, we consider a HEMS with an HVAC system, rooftop PV, and a PCM layer integrated into the building envelope. The objective of the HEMS optimisation is to minimise electricity costs while maximising PV self-consumption and maintaining the indoor building temperature in a preferred comfort range. Solving this problem is challenging due to PCM's nonlinear characteristics, and using methods that can deal with the resulting non-convexity of the optimisation problem, like dynamic programming is computationally expensive. Therefore, we use multi-timescale approximate dynamic programming (MADP) that we developed in our earlier work to explore a number of Australian PCM scenarios. Specifically, we analyse a large number of residential buildings across five Australian capital cities. We find that using PCM can reduce annual electricity costs by between 10.6% in Brisbane and 19% in Adelaide. However, somewhat surprisingly, using PCM reduces PV self-consumption by between 1.5% in Brisbane and 2.7% in Perth.

Energy efficiency measures and technical-economic study of a photovoltaic self-consumption installation at ENSA Kenitra, Morocco

Abstract The increase in energy demand, increasingly expensive and part of a sustainable development approach, has highlighted the importance of energy efficiency. Indeed, opting for energy efficiency allows to fight against the dissipation of energy and its costs by means of an optimization in the use of energy sources. It is in this sense, that several countries including Morocco, and in order to reduce its energy bill, has opted for a national strategy of energy efficiency which aims to achieve an energy saving of about 20% by 2030 through a better use of energy in all areas of economic and social activity. The integration of energy efficiency and renewable energy techniques in the construction sector is one of the levers that will enable the country to meet its energy challenges and achieve its objectives in the fight against climate change. In this context, Ibn Tofail University has started to exploit the interesting potential of this energy efficiency campus wide. Our addresses the problem of increasing electricity bills at the National School of Applied Sciences of Kenitra (ENSAK) while seeking to develop long-term solutions for the resolution of this problem as well as opening up promising prospects that will better meet its needs. In order to minimize energy consumption at the National School of Applied Sciences, we studied the different types photovoltaic installations and elaborated a study of technical feasibility, financial profitability and environmental impact of potential self-consumption photovoltaic installations.

Investigating the impact of smart energy management system on the residential electricity consumption in Austria

This paper addresses the following question: How can smart energy management system (SEMS) influence the residential electricity consumption at both individual household and national level? First, we developed an hourly optimization model for individual households. The energy cost of an individual household is minimized under given assumptions on outside temperature, radiation, (dynamic) electricity price, and feed-in tariff. By comparing the optimization to the reference scenario, we show the impact of SEMS on grid-electricity consumption and photovoltaic (PV) self-consumption at the individual household level. Second, to we aggregate the results to the national level, we constructed a detailed building stock taking Austria as an example. By aggregating the results of 2112 representative households, we investigate the impact of SEMS in the residential building stock on the national electricity system. As a result, we found that for individual single-family-houses (SFHs) with PV (no battery) and heat pump adoption, SEMS can significantly reduce the grid-electricity consumption up to 40.7% for a well-insulated building. At the national level we found that, for the buildings with 5 kWp PV but without hot water tank or battery storage, SEMS can still reduce the grid-electricity consumption by 7.4% by using the building mass asthermal storage.

Investigation on Individual and Collective PV Self-Consumption for a Fifth Generation District Heating Network

Renewable Energy Communities have been recently introduced in European legislation to promote distributed generation from renewable energy sources. In fact, they allow to produce and consume energy from shared local power plants. Low temperature district heating and cooling networks with distributed heat pumps have demonstrated their capability to exploit renewable and waste heat sources in the urban environment. Therefore, they are considered a promising infrastructure to help decarbonize the building sector. As their main operating cost is the electricity purchased by the utility for heat pumps and circulation pumps, this work investigates whether a Renewable Energy Community could help mitigate such cost by sharing electricity produced by local photovoltaic (PV) systems. The research relies on computer simulations performed with both physical and statistical models for the evaluation of electrical load profiles at the district level. Results show that due to the different seasonality between heating demand and PV production, the increase in self-consumption due to the distributed heat pumps is lower than 10%. The use of batteries does not seem convenient for the same reason. The environmental benefit of the proposed system is evident, with CO2 emissions reduced by 72–80% compared to the current situation depending on PV power installed. It also emerged that PV sharing significantly improves the self-consumption at the district level, in particular when the installed PV power is limited (+45%). In conclusion, results suggest that current incentives on PV-sharing make Renewable Energy Communities a viable option to improve the techno-economic performance of fifth-generation district heating and cooling networks.

PV self-consumption prediction methods using supervised machine learning

The increased prevalence of photovoltaic (PV) self-consumption policies across Europe and the world place an increased importance on accurate predictions for life-cycle costing during the planning phase. This study presents several machine learning and regression models for predicting self-consumption, trained on a variety of datasets from Sweden. The results show that advanced ML models have an improved performance over simpler regressions, where the highest performing model, Random Forest, has a mean average error of 1.5 percentage points and an R2 of 0.977. Training models using widely available typical meteorological year (TMY) climate data is also shown to introduce small, acceptable errors when tested against spatially and temporally matched climate and load data. The ability to train the ML models with TMY climate data makes their adoption easier and builds on previous work by demonstrating the robustness of the methodology as a self-consumption prediction tool. The low error and high R2 are a notable improvement over previous estimation models and the minimal input data requirements make them easy to adopt and apply in a wide array of applications.

Net-Metering and Net-Billing in Photovoltaic Self-Consumption: The Cases of Ecuador and Spain

Net-Metering and Net-Billing in Photovoltaic Self-Consumption: The Cases of Ecuador and Spain

Revenue opportunities by integrating combined vehicle-to-home and vehicle-to-grid applications in smart homes

A smart integration of electric vehicles (EVs) in the future energy system will be crucial in decarbonizing the energy sector. Bidirectional EVs can provide flexibility for the system and generate revenues for the user through multiple use cases. We model both exclusive photovoltaic (PV) self-consumption optimization and the combined usage of PV self-consumption optimization and arbitrage trading for a household with an unmanaged, smart, and bidirectional charging EV in a linear (LP) and mixed-integer linear programming (MILP). Since power flows in a typical household are low, varying non-linear charging and discharging efficiencies of the bidirectional EV in the MILP result in more realistic revenues that are 30% lower than in the LP with fixed efficiencies. For a typical German household using a bidirectional EV for optimizing PV self-consumption, these revenues are about 310 €/a, mostly generated during the summer. Arbitrage trading well complements this vehicle-to-home use case in the winter months, resulting in revenues up to 530 €/a. These significant revenue potentials can lead to more profitable and interactive EVs incentivizing users to change from internal combustion vehicles to electric mobility.

Optimized design of a distributed photovoltaic system in a building with phase change materials

Building decarbonization is one of the most important ways to stop or reverse climate change. Phase change materials (PCMs) were embedded into a building wall to reduce air conditioning energy consumption and related carbon emissions in this article. Moreover, a distributed photovoltaic (PV) system was designed to provide electricity for the building. The thermal and electrical performance of the building with four different amounts of PCMs and a PV system was simulated and compared with a building using PV system only. To validate the simulated results, an experimental apparatus for buildings with PCMs and a PV system was designed and constructed. Results show that the peak demand load is reduced by maximum of 47% using 5.2 vol% PCMs and shifted by one hour in summer. However, the demand load is 1.3% higher during daytime in winter. Three different optimizations were conducted with the aim of highest building self-balance and PV self-consumption as well as minimum interactions between buildings and grid. The economic benefits for the building with PCMs are higher because of the reduction of peak demand load. However, the payback period is 0.5 year longer for the building with PCMs than the reference building. The carbon emissions are reduced by 11.58 kg more using 5.2 vol% PCMs in a building with an area of 6.25 m2, compared to the reference building.

Transition towards solar Photovoltaic Self-Consumption policies with Batteries: From the perspective of distribution networks

The transition towards low-carbon energy systems requires increasing the contribution of residential Photovoltaic (PV) in the energy consumption needs (i.e., PV self-consumption). For this purpose, the adoption of PV self-consumption policies as alternatives to the current net-metering policy may support harnessing batteries to improve PV self-consumption. However, the technical impacts of PV policies on distribution networks have to be adequately assessed and mitigated. To do so, a two-stage planning framework is proposed. The first stage is an optimization approach that determines the best sizes of PV and batteries based on the adopted PV policy. The second stage assesses the impacts of the resulting sizes on distribution networks using Monte-Carlo simulations to cope with uncertainties in demand and generation. The framework is applied on real medium and low voltage distribution networks from the south of Jordan. For the net-metering, the results show that the uptake of residential PV penetration above 40% will result in voltage issues. It is also found that the adoption of batteries for the benefits of customers (i.e., reduce electricity bills) will not mitigate the PV impacts for PV penetration above 60%. Further, the results demonstrate the important role of distribution network operators to manage the uptake of batteries for the benefits of customers and distribution networks. Network operators can support customers to adopt larger sizes of batteries to achieve the desired PV self-consumption in return of controlling the batteries to solve network issues. This facilitates the uptake of 100% PV penetration and improves PV self-consumption to 50%.

Optimal battery capacity of grid-connected PV-battery systems considering battery degradation

The optimal capacity of a battery energy storage system (BESS) is significant to the economy of energy systems and photovoltaic (PV) self-consumption. In this study, considering the long-term battery degradation, a mixed-integer nonlinear programming (MINLP) model was proposed for the PV-battery systems which aim to minimize the life cycle cost (LCC) and was solved by a novel two-layer optimization method. This study compared optimal battery capacities, LCCs, and self-consumption ratios (SCR) under two types of tariffs with and without considering battery degradation. The effects of feed-in tariffs, feed-in limits and PV degradation on the optimization results are discussed. The results showed that the battery degradation could cause an increase in operation cost. The cases considering battery degradation have larger battery capacity and more life cycle cost than the cases without considering battery degradation. Compared with the feed-in tariff profile of flat rate, the cases with feed-in tariff profile of time-of-use have smaller optimal battery capacity, higher SCR, and lower LCC. The feed-in tariff, feed-in limit and PV degradation have an important impact on optimal battery capacity and total life cycle cost. This study provides an optimization method and theoretical analysis for the capacity and operation optimization of PV-battery systems.

Comparative study of photovoltaic self-consumption alternatives considering the Spanish legal framework

This work presents a comparative study focusing on the alternatives that a small photovoltaic self-generating group of consumers face within the new legal framework in Spain. Several alternatives are presented, simulated and discussed for a representative photovoltaic generating plant serving a group of consumers that could be associated. Conclusions are duly drawn, showing which options are most favourable for the consumers. Also, a brief description of the most important features of the new legislation is presented.

A novel power management algorithm for a residential grid-connected PV system with battery-supercapacitor storage for increased self-consumption and self-sufficiency

The increasing penetration of renewable energy technologies causes major problems in the power network, as their generation cannot be totally predicted. Along with fluctuations in the generation of renewables due to weather uncertainties, storage is very important for mitigating several problems that may arise, affecting the stability and reliability of the grid. In particular, in recent years there has been an emphasis on residential storage applications (behind-the-meter storage), with the aim of increasing the energy self-consumption and therefore reducing electricity bills. The proposed model consists of a 3 kWp rooftop solar photovoltaic (PV) system connected to the grid through converters and a battery-supercapacitor hybrid energy storage system. The model is developed and simulated in the MATLAB/Simulink software environment, based on mathematical analysis and average modeling. The supercapacitor handles rapid changes that occur within 0.2 s, and this can relieve the battery stress and extend the battery lifetime. The building’s electricity demand is satisfied through the PV, hybrid energy storage and/or grid. A new filtration-based power management algorithm (PMA) is proposed here, prioritizing the utilization of the PV and battery-supercapacitor instead of the grid, thus achieving a reduced power exchange between the building and the grid and increasing the PV self-consumption and self-sufficiency of the building. The dynamic performance of the proposed model is verified through several simulations over short time periods (10–30 s) for different scenarios that could occur. The obtained results show that the model works properly and responds extremely fast during the different mode transitions, exhibiting a very fast DC-bus voltage regulation with a very small ripple voltage of up to 5 V (a maximum of ± 0.625%). Additionally, both battery and supercapacitor remain between their minimum and maximum limits. Finally, an effective power sharing is achieved between the PV, the battery-supercapacitor storage, the building load and the grid.

Analytical approach for maximizing self-consumption of nearly zero energy buildings- case study: Baltic region

Deployment of photovoltaic (PV) systems in nearly zero energy buildings is rapidly increasing, negatively affecting grid stability and power quality. Therefore, many utilities favour policies that limit the power injection to the grid and increase PV self-consumption. A battery energy storage system (BESS) could constitute a possible solution. However, BESS optimal scheduling to increase PV self-consumption and minimize potential purchased energy from the grid in the situation of non-supported demand by PV is a non-linear multi-variable decision-making problem. Previous works have used optimization algorithms to overcome this problem. Due to these algorithms' nature, instead of global optimum scheduling, just near-optimal results are achieved and the consistency of the results, which is essential especially for sensitivity analysis is not guaranteed. The main goal of this paper is to present an analytical approach to maximize the PV self-consumption and optimize BESS scheduling to minimize electricity costs for a two-level electricity tariff energy system. The effectiveness of the proposed analytical scheduling of the BESS-PV energy system is demonstrated through simulation on real data of a Nearly Zero Energy Building (nZEB) in the Baltic region, Estonia, as the case study. Compared with alternative optimization algorithms, the proposed approach leads to a significant reduction in the purchased energy from the grid, while maximizing PV self-consumption and also noticeably reducing computation complexity and time for reaching the global solution.