Duck Curve Research Articles

Optimal design of bifacial PV system to mitigate duck-curve problem of power system with the UC problem

High penetration of photovoltaic (PV) power generation systems is restricted due to the risk of a critical stability decline of the power system. The stability decline has been caused by the power supply/demand imbalance due to the mismatch between photovoltaic power generation and load demand, and exceedance of generators ramp up/down rate with netload variation. This risk is known as the “duck curve problem” in the planning phase. Measures have been proposed, such as demand response (DR), energy storage system (ESS) management, and curtailment of PV generation to mitigate this problem. In this paper, a bifacial PV system of the vertical installation is proposed for duck curve mitigation, which is the simplest and unprecedented approach for the power system. The bifacial PV module’s detailed irradiance model revealed that the proposed method could simultaneously achieves the three characterized peaks; low peak, shifted peak, and bimodal peak. The effect of three characterized peaks on the power system is verified by solving the unit commitment (UC) problem. Besides, the synergistic effect with the ESS management is also verified by the UC problem. Moreover, a multi-objective approach has been applied to the UC problem to assess the proposed PV system and ESS design. Finally, the proposal was clarified that advantages in points of PV installation and total PV output are increased to +34% and 21% respectively, daily operation costs are saved by 2.3%, and reduction of required ESS capacity is achieving to −94% by short cycle charge/discharge operation. This result is concluded that the proposal is in spite of a very simple method, it clarifies the remarkable effects of power system stability, economic advantage, and the possibility of high penetration of renewable energy.

Primary Energy Use and Environmental Effects of Electric Vehicles

The global market of electric vehicles has become one of the prime growth industries of the 21st century fueled by marketing efforts, which frequently assert that electric vehicles are “very efficient” and “produce no pollution.” This article uses thermodynamic analysis to determine the primary energy needs for the propulsion of electric vehicles and applies the energy/exergy trade-offs between hydrocarbons and electricity propulsion of road vehicles. The well-to-wheels efficiency of electric vehicles is comparable to that of vehicles with internal combustion engines. Heat transfer to or from the cabin of the vehicle is calculated to determine the additional energy for heating and air-conditioning needs, which must be supplied by the battery, and the reduction of the range of the vehicle. The article also determines the advantages of using fleets of electric vehicles to offset the problems of the “duck curve” that are caused by the higher utilization of wind and solar energy sources. The effects of the substitution of internal combustion road vehicles with electric vehicles on carbon dioxide emission avoidance are also examined for several national electricity grids. It is determined that grids, which use a high fraction of coal as their primary energy source, will actually increase the carbon dioxide emissions; while grids that use a high fraction of renewables and nuclear energy will significantly decrease their carbon dioxide emissions. Globally, the carbon dioxide emissions will decrease by approximately 16% with the introduction of electric vehicles.

Local distributions of electricity demand

A simple model for how electricity use might be distributed over space is developed and solved. The model depends on a simple characterization of “heat islands”, and considers summer cooling use in particular. Various technological changes to allow for electric grid use of renewable energy and other technologies, such as energy efficiency and hydrogen or electric vehicles, change the outcome of this model, and produce a “saddle”, not necessarily a “duck curve”. Inequality in income and wealth drives these results, and policies to ameliorate or remove these effects will have to be and be seen as fair to those of various income levels and locations.

Analyses of duck curve phenomena potential in polish PV prosumer households’ installations

In Poland, especially since 2018, a dynamic increase in installed capacity in photovoltaics has been observed. In households with a photovoltaic installation (of which there were about 350,000 in Poland at the end of 2020), a phenomenon called ”duck curve” is observed, consisting in low energy consumption from the power grid (correlated with the simultaneous delivery of the produced PV energy to the grid) during the day and relatively high energy consumption in the evening. This case concerns days with relatively high insolation values.The aim of the work is to analyse data from 608 PV installations, including the estimation of the difference (VDC) between maximum hourly electricity overproduction and maximum hourly net electricity consumption from the grid. The maximum monthly VDC value (calculated as the average of daily values) in 2019 reached the level of 2.9 kW per one prosumer household. Calculation for each day of 2019 allowed to determine the maximum VDC value of 3.9 kW, counting on a prosumer household. The median of this value (for 365 days) was 2.08 kW. It is estimated that the maximum power fluctuation in the national power grid due to the connection of 400,000 prosumer households in the grid is approximately 1.6 GW.

Analysing the role of roof mounted BIPV system optimization on decreasing the effect of duck curve in Perth, Western Australia: An experimental case study

This paper optimizes roof shape and associated Building Integrated Photovoltaic (BIPV) mounting strategies based on the newly announced feed-in tariff pricing range in Western Australia to confirm hypothesised benefits and potential influence on grid consumption reduction. Through analysis of a residential building in Perth, the study looks at: the current status of consumption, solar production, and energy balance. An Evolutionary Algorithm (EA) approach is deployed to optimize roof shape for each month and one whole year, with the aim of maximizing financial return by selling extra produced power to the grid. We have found that the optimized form has a tilt angle of 35.9° from horizontal and an azimuth angle of 47° toward the northwest. To compare the effectiveness of the newly announced tariff pricing, an analysis of economic benefits has been carried out comparing the ability of base case and optimized roof shapes to balance production and consumption surpluses and deficits. The outcome exemplifies the positive effect of an optimized roof shape that could supply 16% higher solar production than the base case shape. The current work provides practical guidelines for engineers, architects and researchers to design more suitably built environments.

The duck curve in a drying pond: The impact of rooftop PV on the Western Australian electricity market transition

Rooftop solar photovoltaics (PV) are significantly influencing the electricity market and system operability in Western Australia. Qualitative methods are used to determine likely impacts and solutions to associated technical and market challenges in this islanded electricity system. Solutions focus on flattening the load curve and addressing minimum system load issues, including via; tariff reform, new ancillary services, automation, storage and energy productivity; targeted markets to match energy supply to the new demand curve; together with enabling technologies such as; improved inverter functionality and control systems. A parsimonious model demonstrates the impacts of rooftop PV on the local mid-day wholesale energy prices.

Managing the duck curve: Energy culture and participation in local energy management programs in the United States

Local energy management programs have the potential to flatten the so-called “duck curve” and enable greater use of renewables by engaging residents to work toward shared sustainability goals. However, there are challenges with managing energy behavior to achieve desired outcomes. This study uses the Energy Cultures framework to examine motivations for participating in a local energy program. It extends the Energy Cultures framework by demonstrating its applicability to the analysis and co-design of local energy management programs, with a particular focus on community-level engagement. The research also identifies the types of information most useful in managing energy behavior. The study site is the Mueller community in Austin, Texas, U.S.A., which has high levels of solar generation and a resulting duck curve that can be addressed by local energy management. A mixed-methods approach was utilized, with qualitative data collection through stakeholder interviews and a community workshop, followed by quantitative data collected in a survey of residents. Results show a strong interest in the proposed energy program, with residents motivated by environmental, societal, and financial benefits. Individual and social norms drove favorable responses while material culture and practices did not. Valuable types of information feedback included energy usage recommendations and renewable energy use at the individual and community level. The findings demonstrate that community engagement, energy feedback, and energy culture are interdependent and that analyzing the energy culture of a community in a co-design process can help prioritize criteria for developing energy programs and tools that strengthen and sustain community engagement.

Enhancing PV Hosting Capacity Using Smart Inverters and Time of Use Tariffs

Optimizing the Photovoltaic (PV) hosting capacity (HC) considering the irradiance's variability properly and the load during the day represents a critical matter. Typically, the high PV HC results in overvoltage and high voltage fluctuations at the point of common coupling (PCC) with the utility. In this paper, a new algorithm is proposed for enhancing PV HC by considering the smart inverter functions to overcome key PCC issues. The Volt-Var and dynamic reactive current functions of the smart inverter are suggested to increase the PV HC. Furthermore, the time of use tariff is utilized for mitigating the duck curve issue at the utility by reducing the peak to valley difference of the substation net load curve. Quasi-static time-series simulations are performed using the OpenDSS program to prove the effectiveness of the proposed algorithm. The proposed algorithm is validated by extensive numerical analysis on the standard IEEE 123 node test feeder. Deduced outcomes are very encouraging in mitigating the overvoltage, reducing the energy losses, limiting the considerable number of on-load tap changes, and alleviating the high voltage fluctuations.

Duck curve leveling in renewable energy integrated grids using internet of relays

Power grids are undergoing deregulation, privatization, and decentralization worldwide. The smart grid allows the integration of clean power renewable energy technologies to the utility grid through mini, micro, and nano grids. Conventional centralized power grids used to rely on fossil fuel-fired power plants which are being replaced worldwide with solar, wind, small hydro, geothermal, biomass, wave, and alternative energy technologies. Installed solar and wind power generation capacity has surpassed 1300 GW in 2020. Solar and wind powers rise during the day and fall during the evening. Consumer demand increases steadily during the evening and peaks after sunset when solar generation becomes zero and wind power falls substantially. Decentralized smart grid faces duck curve limitation on the integration of renewable energy technologies as centralized utilities used to face the peak-hours issue. Smart grid operators have no effective solution of load peaking after sundown except keeping fossil fuel-fired plants on standby to ride through the duck curve. We present a solution to the duck curve problem by integrating smart load shedding devices in micro and nano grids to adjust their demand frugally during peak hours to support the national grid. Information and communication (ICT) technologies that enabled the internet of things (IoT) have been attempted to facilitate smart load shedding in nano grids. ICT enabled voltage transformers (VT) and current transformers (CT) supply signals to heuristic rules based multifunction smart relaying system. Smart meters, instrument transformers, and status monitoring field devices generate a massive amount of data. The research designed an IoT and LabVIEW based software platform to carry out demand-side management. Under frequency (UF) and under-voltage (UV) relaying functions were used to conduct load shedding in nano grids. This nanoscale demand-side management experiment paves the way to ride through utility-scale duck curve setback. This PT/CT data based economic solution performs at nanoscale same as ABB PML 630 load shedding controller. This UF and UV relaying based multifunction relay starts shedding loads when frequency falls below <1% and voltage below 10% of rated values. Our load shedding scheme has successfully functioned on nanoscale 5 kW system. The future study section proposes to integrate big data technologies in-home energy management system (HEMS) through extract, transform and load (ETL) pipeline to import data from PT/CT and circuit breakers (CB) integrated smart meter to transform it according to requirement by loading data into Hadoop “big data” processing platforms for utility-scale load management with help of day-ahead load and weather-dependent renewable power generation forecasting techniques.

Developing an Optimal Scheduling of Taiwan Power System With Highly Penetrated Renewable Energy Resources and Pumped Hydro Storages

Recently, Taiwanese Government has announced an ambitious energy policy: up to 20% of total power will be generated from renewable energy resources by 2025. Under such high-level penetration level of renewable energy resources, the typical net load curve will be definitely reshaped as the so-called duck curve. Operation modes of pumped hydro storage units in Taiwan power system will be totally changed. To tackle out this difficulty, the optimal scheduling of Taiwan power system is developed in this article for examining the coordination of highly penetrated renewable energy resources and pumped hydro storage plants under the required spinning reserve. The associated cost function and various constraints used in this optimal scheduling problem are described first. Physical characteristics of pumped hydro storage units in Taiwan will also be studied. By following the data format in the open source MATPOWER optimal scheduling tool, platform developments of this optimal scheduling program are reported. To validate the effectiveness of the proposed scheme, intensive simulations of both current scenarios and future scenarios are conducted. Simulation results indicate that existing pumped hydro storage units in Taiwan can indeed mitigate the impact of the duck-shaped net load curve in the generator scheduling of future Taiwan power system even under the worst scenario.

Peak Reduction and Long Term Load Forecasting for Large Residential Communities Including Smart Homes With Energy Storage

Domestic load profiles in the residential sectors are being modified with the adoption of smart home management systems and solar generation. In addition, houses with rooftop PV behave like local generators, contributing to the growth of the penetration of PV energy. Hence, the demand for power is declining day by day. However, the increasing PV penetration causes technical challenges for the power system, such as the “duck curve”. This can be addressed through home energy management (HEM) techniques including peak shaving, load shifting with smart home devices. In this regard, electric water heaters (EWH), with high thermal mass and being ubiquitous, are attractive and low-cost energy storage systems. In this article, a case study for one of the largest rural field smart energy technology demonstrators involving business, industries, and more than 5,000 residences, located in Glasgow, KY, US, is presented. Furthermore, a HEM system, which aims to minimize the total energy usage and peak demand by regulating the heating, ventilation, and air-conditioning (HVAC) systems, water heaters, and batteries, thereby benefiting both the utility and the consumer is proposed. This work also demonstrates the ability of EWH to provide ancillary services while maintaining customer comfort. The minimum participation rates for EWH and batteries are calculated and compared with respect to different peak reduction targets. Long term load prediction by considering different fractions of smart homes for the utility is also provided.

A techno-economic evaluation of the impact of electric vehicles diffusion on Italian customer billing tariffs

The sales of electric vehicles (EV) are recently experiencing an exponential growth. There is scientific evidence that EVs, in their lifecycle, generate a lower amount of greenhouse gases emissions and local pollution with respect to ICE ones. However, a fast EVs diffusion will increase the load on the power system in a still unclear manner. It is recognized that EV charging is a power intensive operation and that dumb charging strategies will increase security problems for grid management. This will likely happen at the end of business, when a huge quantity of EVs gets connected at the domestic socket at the same time, thus contributing to the ramp up needs from thermal generators to cope with the increasing slope in the “duck curve” at sunset. The presented model estimates the burden that different EV penetration scenarios will have on Italian power system in a short term (2025). This is evaluated in terms of energy flows and load profiles for the charging operations. A behavioural analysis of EV charging operations and a thorough investigation of Italian electricity tariff allows to calculate the economic impact of a base and a strategically regulated scenario. In the regulated scenario, featuring a regulatory toolbox to steer EVs diffusion efficiently, the impact on the power system is mitigated even in case of a larger EV penetration.

A multiobjective analysis of the potential of scheduling electrical vehicle charging for flattening the duck curve

In this paper, we investigate the demand-flexibility of large-collections of electric vehicles (EVs) by scheduling their demand to flatten the electricity duck curve that emerge as a result of growing solar power production. The scheduling problem is investigated in a bi-objective setting and an additional objective function related to the amount of charge provided to EVs is also analyzed. The first objective is the minimization of the ramp-up requirements of the system. The second objective reflects the quality of service and the potential level of charging station's profit margins. An important characteristics of the proposed model is the effect of total charging capacity on the two objective functions. The analysis is carried out based on a quadratic programming model which is used to calculate the Pareto Front of the two objective functions. This is done through a case study based on real-world data for EV driver behavior, solar generation, and energy consumption. The computational experiments show that there is a high level of competition between these two objectives. Moreover, the effect of different maximal charging capacities on these objectives is observed.

Modeling, Configuration, and Grid Integration Analysis of Bifacial PV Arrays

This paper develops an incident energy-based model for fixed multi-row bifacial PV systems considering ground albedo and different configuration parameters, including the inclination (tilt) and orientation angles, the height off the ground, and the horizontal distance between PV rows. The various view factors (VFs) involved in the study, sky model, and the angular position of the sun are included in the model. The proposed analytical model is capable of evaluating the solar irradiance reaching each side of a bifacial PV array at any time and location. The impacts of the configuration parameters on the total energy achievable on the front and back surfaces are studied in detail. Finally, simulations are performed on the IEEE 69-bus test network to show how bifacial PVs can help tackle the Duck curve issue caused by conventional mono-facial PVs.

The Challenges and Opportunities of Renewable Energy Source (RES) Penetration in Indonesia: Case Study of Java-Bali Power System

Nowadays, the integration of renewable energy sources, especially grid-connected photovoltaic, into electrical power systems, is increasing dramatically. There are several stimulants especially in the Java-Bali power system, including huge solar potential, a national renewable energy (RE) target, regulation support for prosumers, photovoltaic technology development, and multi-year power system planning. However, significant annual photovoltaic penetration can lead to critical issues, including a drop of netload during the day, ramping capability, and minimal load operation for thermal power plants. This study analyses the duck curve phenomenon in the Java-Bali power system that considers high shares of the baseload power plant and specific scenarios in photovoltaic (PV) penetration and electricity demand growth. This study also analyses future netload, need for fast ramping rate capability, and oversupply issues in the Java-Bali power system. The results showed that the duck curve phenomenon appears with a significant netload drop in the middle of the day because of high power generation from grid-connected PV. Furthermore, the need for fast ramp rate capability is critical for a higher peak load combined with the lowest netload valley. Moreover, the significant load growth with high grid-connected PV penetration level caused unit commitment issues for thermal power plants as baseload operators.

A Case for Using Distributed Energy Storage for Load Balancing and Power Loss Minimization in Distribution Networks

—We introduce an algorithm to solve the problem of load balancing and loss minimization in distribution networks impacted by temporary service restoration activities. The novelty of the proposed algorithm lies in employing utility directed usage of customer distributed battery energy storage systems, which are assumed to be present and available in the network. With increasing penetration of distributed renewable energy sources, such as photovoltaics and wind turbines, it is projected that batteries will also increasingly be adopted to address some of the new challenges with renewables, such as the so-called duck curve challenge. The deployment of the proposed solution is achieved through demand response signals. To verify its benefits, we develop a co-simulation framework which can be used to develop and study distribution level optimization techniques that exploit the interaction between a smart electric grid, smart buildings and distributed energy storage to achieve energy and cost savings and better energy management practices beyond what one can achieve through techniques applied at the building or network levels only. The proposed algorithm is implemented and verified within the co-simulation framework tool, SmartBuilds. Simulations show that energy storage systems can be used for temporary relief of distribution networks impacted by line failures.

An efficient solution method for integrated unit commitment and natural gas network operational scheduling under “Duck Curve”

An efficient solution method for integrated unit commitment and natural gas network operational scheduling under “Duck Curve”

A techno-economic comparison between piston steam engines as dispatchable power generation systems for renewable energy with concentrated solar harvesting and thermal storage against solar photovoltaics with battery storage

Given its abundance and distribution, solar electricity is clearly the power source of the future. Solar photovoltaics is currently the favoured electricity production technology but high battery costs limit its use as a truly dispatchable solution. Solar thermal electricity, with higher production costs but lower heat storage costs, is an important alternative. In this work we have compared solar thermal and solar photovoltaic renewable energy systems head to head: using simulations with our own models and the latest information from literature. The solar thermal system uses a Uniflow Piston Steam Engine for electricity production. The analysis incorporates the daily variance in electricity demand in the form of the CAISO duck curve. To supply off grid demand, if natural gas driven engines produce electricity at $0.134/kWh, photovoltaics with battery storage can supply it at 0.159 $/kWh (0.107 $/kWh best case) and solar thermal with piston steam engines can supply at 0.144–0.167 $/kWh at current prices. If solar concentrator prices drop to $75/m2-aperture, as seems promising, solar thermal can supply dispatchable electricity at 0.091–0.103 $/kWh. In view of the declining interest in solar thermal electricity, this is a valuable contribution to the conversation about the relative merits of the two technologies.

Bottom-up modeling of residential batteries and their effect on system-level generation cost

The declining cost of residential battery energy storage (BES) and photovoltaic (PV) systems enable customers to significantly reduce their energy dependency from the grid. By absorbing excess PV generation, BES systems could reduce impacts associated with the ‘duck curve’. However, since off-the-shelf (OTS) BES systems are controlled for customer benefits, this absorption might be limited at times, resulting in similar duck curve effects. This work proposes a bottom-up modeling approach to demonstrate the system-level generation cost effects from residential BES systems. Using a modified IEEE 9-bus system with real anonymized smart meter data, results show that with the OTS BES control, the overall reduction in system net demand due to peak PV generation still occurs which leads to baseload generation shutting off, incurring additional costs. Alternatively, the adoption of smarter BES controls aimed at mitigating impacts on distribution networks by reducing PV exports can bring significant system-level generation cost reductions.

Analysis of Challenges Due to Changes in Net Load Curve in South Korea by Integrating DERs

The development of Distributed Energy Resources (DERs) is essential in accordance with the mandatory greenhouse gas (GHG) emission reduction policies, resulting in many DERs being integrated into the power system. Currently, South Korea is also focusing on increasing the penetration of renewable energy sources (RES) and EV by 2030 to reduce GHGs. However, indiscriminate DER development can give a negative impact on the operation of existing power systems. The existing power system operation is optimized for the hourly net load pattern, but the integration of DERs changes it. In addition, since ToU (Time-of-Use) tariff and Demand Response (DR) programs are very sensitive to changes in the net load curve, it is essential to predict the hourly net load pattern accurately for the modification of pricing and demand response programs in the future. However, a long-term demand forecast in South Korea provides only the total amount of annual load (TWh) and the expected peak load level (GW) in summer and winter seasons until 2030. In this study, we use the annual photovoltaic (PV) installed capacity, PV generation, and the number of EV based on the target values for 2030 in South Korea to predict the change in hourly net load curve by year and season. In addition, to predict the EV charging load curve based on Monte Carlo simulation, the EV users’ charging method, charging start time, and State-of-Charge (SoC) were considered. Finally, we analyze the change in hourly net load curve due to the integration of PV and EV to determine the amplification of the duck curve and peak load time by year and season, and present the risks caused by indiscriminate DERs development.