Residential PV-battery Systems Research Articles

A Study of Grid-Connected Residential PV-Battery Systems in Mongolia

For national energy capacity improvement and CO2 emission reductions, Mongolia has focused its attention on grid-connected residential PV systems. Due to the feed-in tariff (FIT), the aggregated residential PV systems are expected to increase with the PV penetration level. Currently, there is no power injection limitation in Mongolia. A new policy for the PV penetration level of residential PV systems needs to be developed. This study analyzed the techno-economic performances of distributed PV-battery systems, considering PV generation, the historical load demand, and the tariff structure. We studied the performances of 40 combinations of PV sizes (2 kW–9 kW) and battery capacities (4.4 kWh, 6.6 kWh, 10 kWh, 12 kWh, and 15 kWh) to find feasible system sizes. The aggregated PV-battery systems in a low-voltage (LV) distribution system located in Ulaanbaatar, Mongolia, are also discussed. The results show that six combinations satisfied the technical and economic requirements. The maximum profit was determined to be an NPV of 1650 USD with a 9-year payback period using combination 3 (6 kW PV and 6.6 kWh battery capacity). Combination 6 (8 kW PV and 15 kWh battery capacity) shows that the energy management strategy for residential houses with battery storage has the potential to increase the installed capacity of PV systems without voltage violence in the LV network. For the distributed PV-battery storage system (BSS), the environmental analysis indicates that CO2 and SO2 emissions were reduced by 3929 t/year and 49 t/year, respectively. The findings obtained from this analysis will be used for power system planning.

Levelized profits for residential PV-battery systems and the role of regulatory and fiscal aspects in Germany

BackgroundThe levelized cost of electricity (LCOE), expressing the price per unit that a technology must receive over its lifetime to break even, is a useful indicator, but insufficient for a comprehensive investment appraisal of PV-battery (PV-BES) projects. For household PV-BES systems, aimed at prosumers in the German context, our paper seeks to look at the revenue side in addition to the costs side of the investment decision. We extend the LROE (levelized revenue of electricity) to consider the impact of different fiscal options available to households seeking to install PV-BES systems. From this, we calculate the levelized profit of electricity (LPOE), linking the cost-focused and revenue-focused perspectives of prosumers facing investment options. We calculate the LPOE for six different fiscal options available to prosumers, four household types with different socio-economic characteristics and for different sizes of PV-BES systems.ResultsIn terms of preliminary results, we identify the most advantageous fiscal option and the drivers of LPOE in relation to PV-BES systems in Germany. We find that the switching from the standard to small business tax setup is the optimal fiscal option for all households and all technology combinations, but the optimal income tax decision depends on the present value of revenues compared to tax deductibles. The LPOE is particularly sensitive to changes in CAPEX and retail electricity prices, with the FiT rate, VAT rate and Income Tax rate being somewhat influential. From an LPOE perspective, self-consumption is incentivized through lower FiT rates and higher electricity prices, whereas the abolition of the EEG contribution and low FiT rate adversely affect the LPOE of different options. Stand-alone PV remains the most attractive option, with bundles with storage showing weaker profitability performance.ConclusionsLPOE complements the LCOE and LROE indicators and offers a comprehensive investment analysis, integrating fiscal considerations. Moreover, it offers greater guidance as to the relative attractiveness of different technology configurations and technology sizing. Mechanisms could be implemented to enhance the profitability of residential PV-BES systems in line with energy policy objectives.

Preface

China is currently the world’s largest producer of wind and solar energy. China has made great efforts in promoting decarbonization in energy and reducing greenhouse gas emissions. In the future, China will accelerate the development of low-carbon and zero-carbon technologies to help promote green development. China has a clear pathway to build a more sustainable, secure and inclusive energy future.Following this concept, the 2022 International Conference on Renewable Energy and Electrical Technology (ICREET 2022) took place held during December 16th-18th, 2022 in Xi’an, China (virtual event). The conference provided a platform of international standards where all the participants discussed and shared persuasive key advances in renewable energy and electrical technology. It also offered a unique venue for renewing professional relationships and for remaining up-to-date variations in the challenging and expanding fields.This volume contains the papers presented at the 2022 International Conference on Renewable Energy and Electrical Technology. The papers covering various topics including Renewable Energy Power Generation, Natural Gas Hydrate Development Technology, Energy Internet Technology, Electric Motors and Drives, Power System Analysis, etc. have been through rigorously reviewed by experts and meet the requirements of publication.The online conference attracted about 60 individuals, and three sophisticated professors performed their keynote speeches during the conference. Among them, Prof. Pierluigi Siano from University of Salerno, Italy, one of our keynote speakers, shared with us his research on Energy Communities in Smart Grids. A novel scalable and privacy-preserving distributed parallel optimization that allows the participation of large-scale aggregation of prosumers with residential PV-battery systems in the market for the ancillary service (ASM) was proposed in this study. To consider both reserve capacity and reserve energy, day-ahead and real-time stages in the ASM were considered. A method, based on hybrid Variable Neighborhood Search (VNS) and distributed parallel optimization was designed for the day ahead and real-time optimization. All the keynote speakers made brilliant speeches combing their own research domains and experiences, and shared unique insights, creating a great platform for academic communication.On behalf of the Conference Organizing Committee, we would like to extend our sincere acknowledgement to all the keynote speakers, peer reviewers, and all the participants. Particularly, our heartfelt thanks and appreciation go to the Editors and Managers, Journal of Physics: Conference Series for their help and kind cooperation during the preparation of the proceedings. Hopefully, all participants and other interested readers can benefit significantly from the proceedings and find it rewarding.The Committee of ICREET 2022List of Committee member is available in this Pdf.

Prosumer-centric demand side management for minimizing electricity bills in a DC residential PV-battery system: An Australian household case study

Technological advancements lead to the development of a home energy management system (HEMS) to perform demand side management (DSM) in residential houses. The current study proposes a DSM strategy to minimize electricity bills in a DC residential house considering the predictions of electrical load consumption and solar photovoltaic (PV) power based on long short-term memory networks. A simulation study compares the proposed DSM strategy against four alternate scenarios, the result shows that the proposed DSM strategy has the lowest annual energy cost increment (5.81%) compared to the benchmark scenario where it is assumed to have the ideal predictions of the load consumption and solar PV power. It also shows that the predictions of the load consumption and solar PV power are essential for economically sensible DSM, however further analysis reveals that the prediction errors do not correlate with the differences in daily energy costs (or daily peak powers) resulting from the proposed DSM strategy and benchmark scenario. Finally, the proposed DSM strategy is experimentally validated on a small-scale hardware-in-the-loop test platform to demonstrate the feasibility of its practical implementation in a HEMS.

Techno-Economic Analysis of Grid-Connected PV Battery Solutions for Holiday Homes in Sweden

Grid-connected PV battery systems for private homes are becoming increasingly popular in many countries, including Sweden. This study aimed to evaluate the techno-economic feasibility of such distributed, grid-connected PV battery systems for single homes at a Swedish holiday location. It was especially of interest to investigate the impact of demand charges, as they are frequently introduced by utilities in Sweden and are also common in popular winter sport regions. Grid-connected PV battery systems were sized and optimized based on their net present cost. Load patterns, incentives, demand tariff structures and electricity price variation were used to study the sensitivity of the obtained results. Grid-connected residential PV battery systems were found to be equally profitable compared to grid-connected PV systems without batteries when demand charges were applied. When the load profiles had peak loads throughout the whole year and the batteries were large enough sized to shave many peaks, grid-connected PV battery systems had slightly higher profitability than grid-connected PV systems without batteries. The total savings also depended on the actual rate of demand charge. The good profitability we found greatly depends on the current state incentives for these systems in the form of tax credits for surplus electricity and investment costs. Removing the tax credit for surplus electricity would reduce the savings generated by a grid-connected PV system without batteries significantly more than for grid-connected PV systems with batteries.

Let the Sun do the Work

PV-Battery systems offer a viable technological alternative to intermittent, stand-alone PV systems when deployed at the residential level. PV-Battery systems allow households to significantly reduce their reliance on the electric grid by storing excess electricity generated from theu solar panels for self-consumption. This study investigates the extent to which residential PV-Battery systems offer economic and environmental benefits to Massachusetts homeowners throughout their operational lifetime. Using spreadsheet models, I represent the energy flows, cash flows, and greenhouse gas emissions associated with residential PV-Battery systems in Massachusetts under different economic and technological assumptions. Results indicate that these systems are a viable means for homeowners to save money and reduce their carbon footprint. Economic performance of residential PV-Battery systems is deeply impacted by federal and state subsidies, as well as net metering offerings and electricity rate structures. System size also offers certain tradeoffs between economic and environmental performance.

Economic Control for a Residential Photovoltaic-Battery System by Combining Stochastic Model Predictive Control and Improved Correction Strategy

Abstract Photovoltaic (PV) power generation can help reduce households’ electricity from the power grid and thus reduce electricity bills. However, due to the intermittence and time-varying nature of PV power generation, part of the clean energy will be wasted. Especially in some places where PV power is allowed to be sold to the power grid, the PV power that exceeds the feed-in limit will be curtailed to reduce the pressure on the infrastructure of the power grid. Battery energy storage systems (BESSs) as energy buffers have attracted increasing attention to help improve the penetration of PV power to households. This paper presents an adaptive energy management method to minimize the energy cost of residential PV-battery systems. First, the uncertainty of the predictive electricity demand and PV power supply is modeled. Then a stochastic model predictive control (SMPC) strategy is used to determine the optimal power flow of the system. Due to the deviation between the predictive input values and the actual ones, the power flow from SMPC is adjusted based on the improved correction strategy (ICS) proposed in this paper. By comparing with the other two methods (one considers the uncertainty and the other does not), the proposed method can increase the economic benefits of the system by 18% and 63%, respectively. The wasted PV power that exceeds the feed-in limit can also be reduced by 24% and 31%. This verifies the effectiveness of the proposed method to improve the system's economic benefits and self-consumption of clean energy.

An integrated smart home energy management model based on a pyramid taxonomy for residential houses with photovoltaic-battery systems

Smart home energy management (SHEM) with residential photovoltaic (PV)-battery systems is a complicated issue with different facets. An integrated SHEM model covering the essential functions is missing. Meanwhile, residential PV-battery systems' optimal operations with renewable energy exchanges and imperfect forecasts are still open challenges. In this study, the research activities in SHEM are firstly organized by a pyramid with four functional layers: (i) Monitoring; (ii) Analyzing and forecasting; (iii) Scheduling; and (iv) Coordinating, which can serve as a standard pathway for developing SHEM. Second, guided by the pyramid taxonomy, an integrated SHEM model is developed for residential houses with PV-battery systems. Assuming a perfect Monitoring layer, we obtain the probabilistic load/PV forecasts and user preference vectors of shiftable appliances based on historical data. Then, we develop a two-stage stochastic programming model for optimal scheduling of single houses with a grid-connected PV-battery system, incorporating the probabilistic forecasts and user preference vectors. A retail electricity market with day-ahead (DA) and real-time (RT) markets is employed for leveraging imperfect forecasts. Finally, we design a distributed coordinating algorithm - Asynchronous Scheduling and Iterative Pricing for PV power-sharing among multiple prosumers based on the single-house scheduling model. Numerical simulations based on realistic loads and PV generation data validated the two-stage stochastic programming model's economic superiority and the distributed PV power-sharing approach compared with the rule-based dispatching and selfish scheduling strategies. We concluded that 1) the modeling of load/PV forecast uncertainties is valuable than averaging or ignoring them, 2) the two-stage stochastic programming model and the DA-RT retail electricity market are beneficial for utilizing imperfect forecasts, and 3) coordinating multiple prosumers could benefit each household by sharing PV and battery investments for revenue or trading with local small prosumers for cost reductions.

On the model granularity and temporal resolution of residential PV-battery system simulation

This paper investigates the impact of model granularity and temporal resolution on simulated energy flow items, self-sufficiency and self-consumption of grid-connected residential PV-battery systems. For such a purpose, three models with increasing levels of granularity are implemented for both PV modules and batteries. In addition, three temporal resolutions (i.e., 1 s, 1 min, and 1 h) of weather data and building electrical loads are considered. The simulation results for a PV-battery system in Lindenberg, Germany show that temporal resolutions have negligible impact on self-consumption and self-sufficiency, but cause noticeable differences of most power profiles observed in the PV-battery system. As for the impact of model granularity, the self-consumption is approximately 44% for the coarsest models, 48% for the models with the intermediate level of granularity, and 52% for the most refined models; the self-sufficiency is 83%, 78%, and 80%, respectively, for the three models.

A multi-year two-stage stochastic programming model for optimal design and operation of residential photovoltaic-battery systems

The residential PV-battery system is becoming economically viable due to technological advances. Selecting optimum investment portfolios and operation strategies are still the challenges faced by homeowners. This study proposed an integrated stochastic sizing-operating framework for optimal design and operation of grid-connected residential photovoltaic (PV)-battery systems. The two-stage stochastic programming method is adopted to address the uncertainties from load and PV productions, where 1) the first stage optimizes the investment portfolio of PV and battery, and 2) the second stage optimizes the energy system operation cost. A multi-year financial model is used to calculate the cash flows during the analysis period. The discounted net present values of expected net benefit (NB) are used as the maximization objective function to make optimal decisions for PV-battery investment and operation. Annual battery degradation factors estimate the aging battery effect. To separately evaluate the PV/battery profitability, we are the first to define the added value/cost metrics of PV/battery. Simulations based on realistic load/PV output profiles and estimated technical–economic parameters validated the effectiveness of the proposed framework and the added value/cost metrics of PV/battery components. The impacts of critical factors, including feed-in tariffs (FiT), tariff profiles and levels, and unit costs, were investigated to provide key findings to stakeholders. This framework can guide the homeowners to cost-effectively invest in and operate the distributed residential PV-battery system to facilitate the transition to a reliable, affordable, and clean building energy system.

System contributions of residential battery systems: New perspectives on PV self-consumption

France aims to boost the share of solar power generation in its electricity mix by 2030. When PV self-consumption systems become economically competitive, end-users will be willing to switch to PV self-consumption instead of using power from the grid. However, high penetration of distributed solar PV provokes a significant impact on the stability of electricity. The major systemic issues concern seasonal back-up power system associated with variable PV integration. Policymakers thus need to work on systemic solutions (e.g. load management, peak shaving) to support the large-scale integration of variable solar power. In this regards, this study aims to propose an innovative load management model based on the secondary application of residential batteries already installed for PV self-consumption. We performed a prospective economic analysis to identify potential system contribution of French residential PV-battery systems in 2030. The aim is to reduce systemic impact of distributed solar PV system integration thanks to collective use of distributed residential batteries for load management when they are not in use in winter. A sensitivity analysis has been conducted based on different projections of residential PV self-consumption in 2030 (RTE, Enedis). Our study then concludes with key messages and policy recommendations.

Optimally sizing of battery energy storage capacity by operational optimization of residential PV-Battery systems: An Australian household case study

Abstract Residential battery energy storage system (BESS) adoption is hindered with its expensive price in current market. Optimally sized BESS can excel the fiscal benefits and thus can be economically sensible. An optimization problem, which targets to minimize the total annual cost including both energy and battery degradation-based costs, is formulated to investigate the added benefits of operational optimization of a BESS over the baseline operation of BESS based on self-consumption maximization (SCM). A case study is conducted considering a Victorian electricity customer in Australia. Results revealed that installation of residential BESSs are fiscally beneficial at current market prices only if adequate rebates are available. A fiscally beneficial upper limit of BESS capacity that can be installed with the prevailing government incentives depending on the PV system capacity is also found. For a given installed cost of BESS (AU$/kWh), optimal BESS capacity is larger when operational optimization of BESS is considered. However, with a time-of-use (TOU) tariff scheme, BESSs based on operational optimization outperforms BESSs based on SCM in terms of the minimum total annual cost, return on investment and PV curtailment avoidance which excels the fiscal benefits of a BESS and results in indirect greenhouse gas emission reduction.

A Scalable Privacy Preserving Distributed Parallel Optimization for a Large-Scale Aggregation of Prosumers With Residential PV-Battery Systems

A novel scalable and privacy-preserving distributed parallel optimization that allows the participation of large-scale aggregation of prosumers with residential PV-battery systems in the market for the ancillary service (ASM) is proposed in this paper. To consider both reserve capacity and energy, day-ahead and real-time stages in the ASM are considered. A method based on hybrid Variable Neighborhood Search (VNS) and distributed parallel optimization is designed for the day ahead and real-time optimization. Different distributed optimization methods are compared and designed and a new distributed optimization method based on Linear Programming (LP) is proposed that outperforms previous methods based on integer and Quadratic programming (QP). The proposed LP-based optimization can be easily coded up and implemented on microcontrollers and connected to a designed Internet of Things (IoT) based architecture. As confirmed by simulation results, carried out considering different realistic case studies, both day-ahead and real-time proposed optimization methods, by allocating the computational effort among local resources, are highly scalable and fulfil the privacy of prosumers.

A MILP optimization model for assessing the participation of distributed residential PV-battery systems in the ancillary services market

A novel non-linear stochastic method based on a Mixed-Integer Linear Programming (MILP) optimization model is proposed to optimally manage a high number of photovoltaic (PV)-battery systems for the provision of up and down regulation in the ancillary services market. This method, considers both the technical constraints of the power system, and those of the equipment used by all the prosumers. This allows an aggregator of many residential prosumers endowed with photovoltaic (PV)- battery systems to evaluate the baseline of the aggregate by minimizing the costs related to the electrical energy absorbed from the grid and then to assess the up and down flexibility curves with relative offer prices. As confirmed by simulation results carried out considering different realistic case studies, the method can effectively be used by an aggregator to evaluate the economic impact of its participation in the ancillary services market, both for the aggregator and for its prosumers.

Multi-stage compound real options valuation in residential PV-Battery investment

Strategic valuation of efficient and well-timed network investments under uncertain electricity market environment has become increasingly challenging, because there generally exist multiple interacting options in these investments, and failing to systematically consider these options can lead to decisions that undervalue the investment. In our work, a real options valuation (ROV) framework is proposed to determine the optimal strategy for executing multiple interacting options within a distribution network investment, to mitigate the risk of financial losses in the presence of future uncertainties. To demonstrate the characteristics of the proposed framework, we determine the optimal strategy to economically justify the investment in residential PV-battery systems for additional grid supply during peak demand periods. The options to defer, and then expand, are considered as multi-stage compound options, since the option to expand is a subsequent option of the former. These options are valued via the least squares Monte Carlo method, incorporating uncertainty over growing power demand, varying diesel fuel price, and the declining cost of PV-battery technology as random variables. Finally, a sensitivity analysis is performed to demonstrate how the proposed framework responds to uncertain events. The proposed framework shows that executing the interacting options at the optimal timing increases the investment value.

Power to the people: Evolutionary market pressures from residential PV battery investments in Australia

Falling costs of solar PV and battery technologies are continuously changing the customer relationship with their electricity network. By managing their own self-generation, customers are able to place natural selection pressure on utilities to evolve. The devised techno-economic simulation model projects residential PV and battery investment decisions over 20 years in Perth, Australia to quantify the potential market impacts from policy and customer investment conditions. Using real-world demand and insolation profiles from 261 households, this research evaluates how cumulative customer PV and battery investments changes the network and market operating conditions, while under the influence of various feed-in tariff values. The results indicate that high feed-in tariff policy costs in the short-term, make it economically challenging to prevent or restrain significant residential PV-battery adoption in the longer-term. Moreover, continuous increases in residential PV-battery system installations eventually lead to annual net-exports substantially exceeding net-imports on the distribution network. This significant shift in network operation provides an opportunity for policymakers to utilise behind-the-meter PV-battery investments and decentralised energy markets to meet wider renewable energy and decarbonisation goals.

Modeling coordination between renewables and grid: Policies to mitigate distribution grid constraints using residential PV-battery systems

Distributed photo-voltaic (PV) generation is one of the pillars of energy transitions around the world, but its deployment in the distribution grid requires costly reinforcements and expansions. Prosumage – consisting of a household-level PV unit coupled with a battery storage system – has been proposed as an effective means to facilitate the integration of renewable energy sources and reduce distribution grid stress. However, tapping its full potential requires regulatory interventions; otherwise, system costs could rise despite increasing flexibility. We analyze the effectiveness of different policy schemes to mitigate the need for distribution capacity expansion by incentivizing beneficial storage operation. Our novel top-down modeling approach allows analyzing effects on market prices, storage dispatch, induced distribution grid requirements, system costs, and distributional implications. Based on German power system data, numerical results indicate that distribution grid requirements can be reduced through simple feed-in policies. A uniform limit on maximum grid feed-in can leave distribution system operators better off, even if they fully compensate prosumage households for foregone revenue. Policies imposing more differentiated limits at the regional level result in only marginal efficiency improvements. Complete self-sufficiency (autarky) is socially undesirable, as it confines important balancing potential and can increase system costs despite adding storage.

Customer economics of residential PV–battery systems in Thailand

The currently high upfront costs of batteries and the low retail electricity prices of households make investments in PV–battery systems not yet economically feasible. However, the experiences/learning curves of renewable generation technologies lead to the assumption that battery prices will rapidly decline with increasing diffusion. Furthermore, projected retail electricity rates are expected to increase with rising electricity demand. This study investigates the returns to residential customers using PV–battery systems under decreasing battery prices in Thailand. The impacts of four additional parameters have been included. The analysis is based mainly on net present values (NPV) and levelized costs of electricity (LCOE). The results show that battery size and its cost, and retail rate design have significant impacts on the returns, whereas buyback incentives for excess electricity have the lowest impact. In addition, to increase the power system flexibility by using PV–battery systems, the Thai government should provide the appropriate financial support, by which the savings incurred by the grid extension investments compensate for the costs.

Extensive analysis of photovoltaic battery self-consumption: Evaluation through an innovative district case-study

Energy storage is one of the key elements within the actual stage of the energy transition, as it is probably one of the most important factors to allow high penetration of fluctuating renewable energies, such as wind or solar, in the existing power systems. Intensive research is being conducted to assess the economic aspects and technical performance of renewable energy-based systems supported by batteries by evaluating different services that batteries can provide to the electric grid or to the end-consumers. In Germany, where the majority of the currently installed 43 GW of PV capacity corresponds to small residential, commercial, or industrial facilities, an interesting market for batteries to enhance local self-consumption and autarky is already booming, with more than 80 000 storage system installations in 2017. In this context, this study presents a comprehensive analysis of the photovoltaic battery model by analyzing the technical and economic consequences that variations on the most relevant system parameters induce. The presented results are based on high resolution data obtained from a representative residential district with an autarky of above 95%. The employed battery model is based on the results obtained through an extensive test campaign and includes electrical and thermal sub-models. The analysis predicts that grid parity of residential PV battery systems can be reached in the upcoming years, with especially great potential of the retrofitting market for those PV installations which run out of the feed-in tariff policy.

Solar PV-Battery-Electric Grid-Based Energy System for Residential Applications: System Configuration and Viability.

Distributed renewable energy share increase in electricity generation is creating challenges for the whole power system, due to its intermittent and nonprogrammable nature. Energy storage has the potential to solve those issues although its technical, economic, and environmental impact is up for debate. The paper presents a study about a PV-battery energy storage system installed in a grid-connected residential apartment in the Green Energy Laboratory at Shanghai Jiao Tong University, China. Daily experimental results show how the presence of energy storage reduces the midday feed-in of excess PV power and the evening peak demand, providing benefits to the distribution network in terms of reduced voltage swings and peak load. Considering the Chinese context, an economic analysis is carried out to assess the profitability of residential PV-battery systems, using the net present value as the economic indicator of an 18-year investment in which the battery pack is replaced twice (6 life years). The analysis shows that such system is not economically viable due to a combination of low electricity prices, valuable PV incentives, and high technology costs. However, considering a future scenario of doubled electricity tariff, halved export tariff, and falling technology costs (-66% battery and -17% PV and inverter), PV-battery investment becomes profitable and shows more resilience to future scenarios than PV-only investment.