High Solar Penetration Research Articles

Integrating solar electricity into a fossil fueled system

The power sector is a major contributor to carbon emissions in China, accounting for nearly half of all emissions, and therefore emission reductions from this sector will be critical, if China is to meet its 2060 targets. Deploying renewable energy sources is the most promising approach to decarbonizing the power sector. However, the intermittency and non-dispatchable nature of wind and solar power pose significant challenges to grid stability, particularly when these sources reach high penetration rates. During China's ongoing energy transition, coal-fired power units will be used to meet peak loads when solar and wind generation is not available. The economic and environmental implications of such a scenario are unclear. In light of this uncertainty, this study applies a unit commitment model to investigate the economic and environmental performance of load shaving strategies across different scenarios. Results show that, in the absence of energy storage systems, a solar penetration rate of 40 % will create an economic bottleneck, and high solar penetration would even increase the total carbon emissions of the whole power system. Moreover, the power system would choose to cycle the large thermal units first to accommodate the solar power, and start-stop strategy is subordinate.

Oxygen Storage Incorporated Into Net Power and the Allam–Fetvedt Oxy-Fuel sCO2 Power Cycle—Techno-Economic Analysis

Abstract With the planned future reliance on variable renewable energy, the ability to store energy for prolonged time periods will be required to reduce the disruption of market fluctuations. This paper presents a method to analyze a hybrid liquid-oxygen (LOx) storage/direct-fired supercritical carbon dioxide (sCO2) power cycle and optimize the economic performance over a diverse range of scenarios. The system utilizes a modified version of the NET Power process to produce energy when energy demand exceeds the supply while displacing much of the cost of the air separation unit (ASU) energy requirements through cryogenic storage of oxygen. The model uses marginal cost of energy data to determine the optimal times to charge and discharge the system over a given scenario. The model then applies ramp rates and other time-dependent factors to generate an economic model for the system without storage considerations. The size of the storage system is then applied to create a realistic model of the plant operation. From the real plant operation model, the amount of energy charged and discharged, the capital expenditures (CAPEX) of each system, energy costs and revenue and other parameters can be calculated. The economic parameters are then combined to calculate the net present value (NPV) of the system for the given scenario. The model was then run through the SMPSO genetic algorithm in Python for a variety of geographic regions and large-scale scenarios (high solar penetration) to maximize the NPV based on multiple parameters for each subsystem. The LOx storage requirements will also be discussed.

Estimation of Solar Power Generation Through the Use of Effective Forecasting Algorithms

The unpredictable nature of solar power generation poses significant challenges to energy management, particularly in power grids with high solar penetration, leading to potential imbalances between supply and demand. This has increased interest in developing accurate forecasting methods for photovoltaic power generation. This study focuses on short-term forecasting of solar power generation using MATLAB, based on historical generation trends through three different hybrid models. It uses a multilayer perceptron neural network as the forecasting tool, which is optimized through various algorithms to fine-tune the network's hyperparameters, such as the weights and biases of the input and hidden layers, for optimal performance. The Root Mean Square Error is used as the objective metric to minimize the discrepancy between actual and predicted values. The models use Particle Swarm Optimization (PSO), Imperialistic Competitive Algorithm, and Genetic Algorithm for network optimization. This research analyzes PV generation data from Belgium in July and August 2022, taken at 15-minute intervals, and evaluates model performance using six different error metrics, including Mean Absolute Error. The results show the accuracy and reliability in forecasting solar power generation time series.

Photovoltaic-penetrated power distribution networks’ resiliency-oriented day-ahead scheduling equipped with power-to-hydrogen systems: A risk-driven decision framework

Hydrogen energy and the pioneering power-to-hydrogen (P-2-H) systems have garnered significant interest due to their potential to revolutionize power systems, offering exceptional long-term energy storage capabilities and augmenting the resilience and flexibility of the grid. This paper presents a novel approach for optimizing the day-ahead proactive scheduling of power distribution networks with high solar penetration. The approach utilizes power-to-hydrogen (P-2-H) technology to enhance resiliency and mitigate risks. Uncertainties in solar irradiation, wind speed, electricity demands, and day-ahead market prices are modeled using a scenario-based probabilistic procedure. The optimization problem is formulated as a mixed-integer linear program that minimizes the network's expected normal and resiliency costs while incorporating downside risk constraints. The approach is applied to an IEEE 33-bus power distribution network, demonstrating the effectiveness of P-2-H facilities and risk assessment in improving resiliency during normal and power outage scenarios. Implementing P-2-H facilities in circumstances with and without risk results in a reduction of total operation costs by 40.89% and 40.32%, respectively, according to the simulation findings. Furthermore, the numerical analysis results show that the network's overall operation cost increase for achieving zero risk is only 3.93%, which reduces to 2.32% with the use of P-2-H facilities.

The cost-competitiveness of concentrated solar power with thermal energy storage in power systems with high solar penetration levels

In this paper, we show that concentrated solar power (CSP) with thermal storage is an economically attractive technology to achieve high solar penetration levels. To this end, we utilize an alternative framework of net levelized cost of electricity (net-LCOE), which captures the projected curtailment rate, to economically compare PV with batteries to CSP in power systems with high levels of solar penetration. We also explore potential benefits enabled by hybrid PV-CSP configuration, which are unattainable by either technology independently. We show the existence of a turning point in power systems with 20%–30% solar penetration levels, after which it is cheaper to supply electricity by CSP with thermal storage by up to 16%. It is demonstrated that storing excess PV electricity in low-cost thermal storage is valuable, enabling CSP configuration with solar multiple as low as 0.5 to operate with a high capacity factor. Furthermore, we show that converting green hydrogen to electricity using CSP power block is cost-effective when seasonal storage is required, thus enabling deep decarbonization based on solar electricity. In these high solar penetration levels, using CSP resulted in a reduction of up to 65% in the net-LCOE. The results may enable researchers and policymakers to evaluate CSP with thermal energy storage as a cost-effective solution for achieving high penetration levels of solar electricity.

Quantifying the value of probabilistic forecasting for power system operation planning

A recent key research area in renewable energy integration is the development of tools and methods to capture and accommodate the uncertainty associated with the forecast errors. While the research community has proposed numerous methods to improve the accuracy of probabilistic forecasts, their application to operational planning is still an open question. This work applies dynamic reserve determination methods to solar probabilistic forecasts and then feed them to a commercial production cost model simulator to assess the value of capturing the uncertainty endogenously in the reserve determination process. Testing is carried out on a calibrated real-size system representing the Southern Company for medium, and high solar penetration levels. Numerical results demonstrate the benefits that can be attained by explicitly modeling probabilistic uncertainty in terms of operating cost, and enhanced system reliability which is measured as the quantity of balancing and reserve violations. Additionally, these methods and results can pave the way for system operators to adopt probabilistic forecasting to draw the operating plans of the system, and this allowing the successful integration of variable renewable energy sources.

The cost of day-ahead solar forecasting errors in the United States

As solar energy contributes an increasing share of total electricity generation, solar forecasting errors become important relative to overall load uncertainty and can add costs to electricity systems. We investigated the costs of day-ahead solar forecast errors across 667 existing solar power plants in the United States (years 2012 through 2019). Our analysis was based on hourly real-time and day-ahead nodal prices. We analyzed two types of solar forecasts: persistence forecasts, a simple approach to forecasting, and a numerical weather prediction forecast, the North American Mesoscale Model (NAM), an improvement over persistence forecasts based on public data and modelling software. We modeled hourly energy forecasts using meteorological forecasts and plant specific characteristics. Hourly plant generation was modeled and debiased with multiple sources of generation records. NAM forecast errors had relatively low costs on average, at no more than $1/MWh in all years except 2016, when costs rose to $1.5/MWh. Even after these error costs, the value of solar was marginally higher when simulating solar participation in day-ahead markets versus participation only in real-time markets. On average, the premium for participating in the day-ahead market, based on NAM forecasts, ranged from −0.5 to 5.2 $/MWh across years. Average error costs were higher in regions with higher solar penetration (i.e., California and New England) compared to regions with low solar penetration. However, California and New England had similar error costs despite higher solar penetration in California, indicating that error costs to date have been only loosely correlated with solar penetration levels.

Operational performance and grid-support assessment of distributed flexibility practices among residential prosumers under high PV penetration

This study explores the impacts of increased photovoltaic (PV) penetration on a real-world power grid. Detailed operational scenarios of existing distributed energy technologies, including grid-tied PV, heat pump, micro-cogeneration, and battery systems among smart residential prosumers are examined using smart meter data. Then, to quantify the grid-support interactions of the decentralized electricity generation and consumption under high grid PV penetration, we introduce grid-support indicators to recognize their induced month-to-month variations based on the dynamic grid spot trading price and carbon emission density profiles. The results show that increased electricity consumption is attractive when the flexible consumption is shifted to a low grid spot trading price and carbon emission intensity period. The grid-support benefits of distributed generators are improved when their output enables a reduction in imported electricity during the grid residual load peak period. The direct simple integration of distributed PV generation makes it difficult to achieve grid-support operation at high solar penetration levels. The analysis results provide insight into the favorable scheduling of the heat pump consumption for time periods corresponding to a large quantity of available on-site PV generation to improve the grid-support performance. The results would help policymakers govern grid-support assets more equitably from a system-level perspective.

Generated Value of Electricity Versus Incurred Cost for Solar Arrays under Conditions of High Solar Penetration

This work considers an emerging value-based paradigm for solar generation under high penetration, in light of its conflicting cycles of supply and demand. The resulting swings in electricity prices, in locales such as California, call into question the aim of optimizing solar arrays solely in terms of accumulated electrical power. Thus, this work studies solar arrays in terms of value, as an accumulated product of electrical power and price, where solar arrays that generate greater electrical power over more profitable early- and late-day hours yield improved value. Experimental, theoretical, and economic analyses are given to characterize the industry-standard angled-panel, an alternative V-groove, and a new U-groove array over a 5-year study. The trends and projections suggest that the industry-standard angled-panel array realizes the best value-based performance at present, although it will likely be outperformed in the foreseeable future by the V-groove array.

BESS as a UPS to Power Systems With High Solar Penetration

This study analyzes the primary and secondary frequency controls of the power system. Further, the article discusses the high-level understanding of keeping a selected feeder’s power supply during an upstream fault condition. The Sri Lankan power system is taken as a case study. Due to the high penetration of wind and solar injection to the Sri Lankan power system, the inertia of the system has been reduced drastically. Simulations have been carried out on a single feeder as it is impracticable to install a large battery energy storage system (BESS). The problems of dynamic stability of the current system due to high solar power penetration are analyzed. The repercussions of these problems to the power system are then discussed related to the stability of the system where there is severe machine tripping. Remedial action needed to be taken to increase the power quality of the power system, namely, BESS and supercapacitors are presented. A comparison of the inertia of the current system and the system when conventional power replaces solar power is also analyzed. The article addresses on how fast-active power injection could be used as an uninterrupted power supply to the selected feeder. It also states the fact that in the case of underfrequency load shedding, the feeder will not experience a power outage due to the BESS. However, BESS limitation and cost analysis are not discussed extensively. The results of analysis show that the selected feeder has uninterrupted power even though the grid was disconnected. Further, an approach is also taken to establish the required reactive power to the feeder as well. Method of combining virtual inertia with inverters is discussed here. All simulations conducted were based on real feeders and data.

Evaluating ammonia as green fuel for power generation: A thermo-chemical perspective

Energy storage will be necessary for a future power system with high penetration of renewable sources, mainly, wind and solar, to ensure the stability of the grid. In this context, power-to-chemicals is a promising concept for a medium/long-term storage horizon and a wide range of capacities. Within this alternative, ammonia rises as one of the fuels with the highest potential in a scenario targeting decarbonization. The first step is the production of ammonia using renewable energy sources, followed by its transformation into energy. This second area requires a deeper analysis at process scale in order to introduce this technology into the future power system. In this work, an assessment of an ammonia-based power plant is presented, focusing on the thermo-chemical route. A combined cycle is evaluated, considering different gas clean-up technologies to recover valuable components and comply with environmental restrictions. As a result, the total efficiency of the power facility reaches about 40%, limited by the maximum temperature allowed in the gas turbine. The influence of the price of ammonia is also evaluated due to the paramount importance of this parameter. The production cost ranges from 0.2 to 0.6€/kWh, with the lowest level corresponding to a scenario in which there is a significant reduction in the cost of renewable power generation and electrolysis technology. Therefore, the feasibility of the use of ammonia as an energy storage alternative is demonstrated, providing a powerful platform for the implementation of a power grid with high penetration of fluctuating sources.

Compounding climate change impacts during high stress periods for a high wind and solar power system in Texas

Power system planning aims at ensuring that sufficient supply- and demand-side assets exist to meet electricity demand at all times. For a Texas electric power system with high wind and solar penetrations, we quantify how climate change will affect supply and demand during three types of high stress periods for the power grid: high demand hours, high net demand hours, and high system ramp hours. We specifically quantify effects on demand, reductions in available thermal capacity (i.e. thermal deratings), wind and solar generation, and net demand. We estimate each using meteorological variables from five climate change projections (2041–2050) assuming Representative Concentration Pathway 8.5 and from a reference period (1996–2005). All five projections indicate that climate change will increase demand by up to 2 GWh during high demand hours (4% of demand in the reference period) and increase net demand by up to 3 GWh during high net demand periods (6% of net demand in the reference period). All five projections also indicate thermal deratings will increase during high demand and net demand periods by up to 2 GWh and high net demand ramps will increase by up to 2 GW. Overall, our results indicate compounding effects of climate change in Texas will necessitate greater investment in peak and flexible capacity.

Investigation of Daytime Peak Loads to Improve the Power Generation Costs of Solar-Integrated Power Systems

Improving daytime loads can mitigate some of the challenges posed by solar variations in solar-integrated power systems. Thus, this simulation study investigated the different levels of daytime peak loads under varying solar penetration conditions in solar-integrated power systems to improve power generation cost performance based on different load profiles and to mitigate the challenges encountered due to solar variation. The daytime peak loads during solar photovoltaic generation hours were determined by measuring the solar load correlation coefficients between each load profile and the solar irradiation, and the generation costs were determined using a dynamic economic dispatch method with particle swarm optimization in a MATLAB environment. The results revealed that the lowest generation costs were generally associated with load profiles that had low solar load correlation coefficients. Conversely, the load profile with the highest positive solar load correlation coefficient exhibited the highest generation costs, which were mainly associated with violations of the supply-demand balance requirement. However, this profile also exhibited the lowest generation costs at high levels of solar penetration. This result indicates that improving daytime load management could improve generation costs under high solar penetration conditions. However, if the generation system lacks sufficient ramping capability, this technique could pose operational challenges that adversely impact power generation costs.

Shadow-camera based solar nowcasting system for shortest-term forecasts

The rapid growth of solar power generation and the variable nature of the solar resource pose challenges for our electricity grids. Forecasting future changes in the irradiance might help to cost-efficiently manage this variability both for photovoltaic and concentration solar plants as well as grids with high solar penetrations. So far, for shortest-term forecasts with lead times of a few minutes, all-sky imager based nowcasting systems are used. However, due to the complexity of dynamically changing 3d cloud shapes as well as certain geometrical effects such as self-occlusion or near-horizon saturation, all-sky imager based nowcasting systems exhibit inherent weaknesses. Here, we present a novel system to generate shortest-term solar forecasts, which is located at Plataforma Solar de Almeria in southern Spain. This approach is based on downward-facing cameras (shadow cameras), taking images of the ground. From these images, spatially resolved irradiance maps are derived. By tracking cloud shadows, future irradiances are predicted. A demonstration system is achieved, which provides shortest-term forecasts for the next 2min. To the best of our knowledge, this is the first time such a system is developed. We benchmark several possible algorithmic approaches on 16 days and compare the deviations to a state-of-the-art all-sky imager based nowcasting system on 22 days. The root-mean-squared deviation (RMSD) of this shadow camera based nowcasting system for direct normal irradiance (DNI) and 1-min temporal averages is 15.6% for lead times of 2min (MAD, DNI: 9.6 %). In comparison to an all-sky imager system, this is an improvement as the all-sky imager system only reaches 22.0% RMSD and 14.8% MAD (both DNI). This demonstrates the feasibility and attractiveness in terms of accuracy of the proposed concept.

Shaping photovoltaic array output to align with changing wholesale electricity price profiles

Large-scale deployment of solar photovoltaics (PV) contributes to the occurrence of depressed—and sometimes negative—electricity prices during daylight hours as PV displaces higher-cost generators in the merit-order dispatch stack. These changes in electricity price provide an opportunity to increase the wholesale energy revenue of PV generators through temporal shaping of PV output. Here, we explore the impact of three output-shaping strategies on PV wholesale energy revenue and capacity factor: utilization of 1-axis tracking, curtailment during negative-price hours, and modification of fixed-tilt array orientation. Utility-scale PV arrays are modeled at more than 10,000 pricing nodes across six United States electricity markets over the 2010–2017 time period. Large changes in revenue-optimized output profiles are observed for the California system, where solar capacity penetration has increased from ∼2% of peak load in 2010 to ∼28% of peak load in 2017, and the wholesale revenue benefits of temporal output shaping are increasing with time. On the California real-time market in 2017, compared to capacity-factor-optimized fixed-tilt arrays with must-run operation, curtailment increases revenues by 9%, curtailment in conjunction with fixed-tilt orientation optimization increases revenues by 20%, 1-axis tracking without curtailment increases revenues by 32%, and 1-axis tracking with curtailment increases revenues by 42% for the median node. Median optimal fixed-tilt azimuths for PV on the real-time market in California have increased from 192° in 2010 to 235° (i.e. 55° west of south) in 2017. Among the markets and years studied, the California market in 2017 demonstrates the largest potential benefit from temporal output shaping. These results highlight mechanisms for mitigating some of the decline in PV wholesale value at high solar penetrations, and illustrate the importance of adapting PV installation and dispatch strategies to changing power system conditions.

Energy and emissions analysis of ice thermal energy storage in the western US

Energy storage systems provide a variety of benefits to the electricity sector, including taking better advantage of renewable electricity when available and smoothing demand by shifting peaks to times when electricity prices and demand are lower. When low electricity demand occurs during the nighttime, additional benefits of overnight energy storage can also occur. Lower nighttime ambient temperatures can lead to efficiency improvements throughout the grid, including power generators, transmission and distribution systems, and chillers. For ice thermal energy storage, these benefits have even been shown to enable net reductions in primary energy consumption compared to normal cooling operations. The energy and emissions impacts of ice storage depend strongly on the diurnal temperature variation and power fleet characteristics in a given region, but previous analysis only examined a single location with relatively high humidity. This study extends the previous analysis to arid regions with high diurnal temperature variation, using Phoenix, AZ and Flagstaff, AZ as case studies, and to a region with high solar penetration, using Los Angeles, CA as a case study. This study demonstrates that the larger temperature variations in Arizona lead to higher round-trip efficiencies, with average primary energy-based effective round-trip efficiencies of 127% in Flagstaff, 107% in Phoenix, and 90% in Los Angeles, with site round-trip efficiencies of 133%, 127%, and 99%, respectively. These high round-trip efficiencies result in net reductions in carbon emissions of 34% in Flagstaff and 24% in Phoenix, but an increase of 53% in Los Angeles due to the absence of solar production overnight. Additionally, the lower icemaking efficiency during warm daytime temperatures makes ice storage poorly-suited for storing excess solar generation compared to other storage approaches. However, the presence of a mature commercial market and the possibility of gaining over 100% effective round-trip efficiency create a strong case for ice thermal energy storage as an energy storage approach for reducing both primary energy consumption and carbon emissions, particularly if fleet efficiencies are higher and carbon intensities are lower overnight.

Factors driving Indian consumer’s purchase intention of roof top solar

PurposeThe solar energy sector has been growing with dramatic reduction in commercial pricing, improved efficiencies and improved deployments/usage conveniences. The purpose of this study is to understand the drivers of the purchase intent for rooftop (RT) solar. This will enable policymakers to improve the penetration of this new and promising green technology.Design/methodology/approachThis research leverages the framework of unified theory of acceptance and use of technology 2 (UTAUT2) to identify and build a quantitative approach to identify factors (and their relative impact on) the purchase intent of a domestic RT solar buyer. The 400 respondents’ field data were collected in Bangalore (a high RT solar penetration region) and Delhi NCR (relatively lower RT solar penetration region).FindingsThe exploratory factor analysis study revealed that the consumers’ purchase intention of RT Solar is shaped by seven main factors, namely, environmental concerns, social beliefs, hedonic motivation, performance expectancy, price value, self-efficacy and effort expectancy, and that these factors explain 79.2 per cent of the field data. This study finds that social beliefs followed by effort expectance concerns are key factors explaining approximately 20 per cent of the purchase intent each, while unit change in price value beliefs explain about 18 per cent of the purchase intent.Practical implicationsSuggested policy measures include building on strengthening emergence of local solar evangelist groups in the communities and easing effort expectance items (e.g. building legal, regulatory frameworks and financial tools for solar penetration models such as renewable energy services companies).Originality/valueThis paper is the first attempt to model the consumer behavior of the Indian RT solar photovoltaic buyer leveraging the UTAUT2.

Distributed residential energy resource scheduling with renewable uncertainties

Advances in metering and two-way communication technologies foster the studies of Home Energy Management System (HEMS). This study proposes a new HEMS, which optimally schedules the distributed residential energy resources (DRERs) in a smart home environment with varying electricity tariff and high solar penetrations. The uncertainties of solar power output are captured by using Monte Carlo sampling technique to generate multiple solar output scenarios based on the probabilistic solar radiation model. The homeowner's rigid and elastic restrictions on the operations of the automatically controlled household appliances are modelled. Based on this, an optimal DRER scheduling model is proposed to minimise the home operation cost while taking into account the homeowner's requirements. A new heuristic optimisation algorithm recently proposed by the authors, i.e. natural aggregation algorithm, is used to solve the proposed model. Simulations based on real Australian solar data are conducted to validate the proposed method.

An integrative approach for indoor environment quality assessment of large glazed air-conditioned airport terminal in the tropics

The indoor environment quality in large glazed space such as airport terminals affects its users in many ways. The indoor environment quality assessment of such a building was typically conducted objective measurement using measurement and subjective assessment using questionnaire survey. However, limited resources and measurement period imply that would be incomplete and cannot provide accurate results. Computer modelling is thus an additional tool in the integrative approach for indoor environment quality assessment and analysis to improve its comfort and energy performance.Field measurement on the objective variables defined the environment quality and energy consumption, as well as questionnaire survey on the subjective judgment defined the indoor comfort. While the computer modelling was assessed across the air temperature, indoor glazed roof surface temperatures, mean radiant temperatures, operative temperatures and illuminance, to examine the building energy performance across the annual cooling load in 2010.The inner surface temperature over glazed roof were recorded as 56°C due to high level solar penetration, even when the indoor air temperatures over the floor level remain stable within the standard comfort zone. This rose the mean radiant temperature and was considered as main cause for indoor discomfort revealed by the survey. The survey found the employees to be slightly uncomfortable and dissatisfied, particularly in summer. The total energy consumption in 2010 was very high compared against the energy benchmarking. This study confirms that the large proportion of glazed roof is the cause for both overheating and thermal discomfort even with excessive use of the cooling system. In addition, recommendation was made to improve the thermal comfort condition in the large glazed air-conditioned terminal.

A Graphical Performance-Based Energy Storage Capacity Sizing Method for High Solar Penetration Residential Feeders

This paper presents a graphical, performance-based energy storage capacity sizing method for residential feeders with high solar penetration levels. The rated power and storage capacity of an energy storage device (ESD) are calculated to fulfill a specified operational requirement. Three locations for installing ESDs are investigated: 1) consumer-owned ESDs inside single-family households; 2) utility-owned distribution transformer-level ESDs; and 3) third-party owned ESDs in a community. First, historical solar radiation data, residential household load data, and residential load models are used for creating the net load (load minus solar generation) ensembles at the house level with resolution of 15 min. Then, a novel graphical capacity selection method using equal probability lines on compressed, composite cumulative distribution function curves is developed for sizing the energy storage needs at the house, distribution transformer, and community levels. Demand-side management methods are investigated for further reducing the need of energy storage. Simulation results demonstrate that the proposed method avoids over- or under-sizing ESDs and allows the users to compare the marginal benefit of increasing the capacity of the ESD.