Solar Curtailment Research Articles

What is driving the remarkable decline of wind and solar power curtailment in China? Evidence from China and four typical provinces

The world’s key renewable power markets are generally challenged by wind and solar power curtailment. Research on the influencing factors of curtailment improvement can provide a reference for the further penetration of renewable energy (RE) into the power system. Based on the logarithmic mean Divisia index (LMDI) method, this paper analyzes the factors influencing the wind and solar power consumption rate in China from 2015 to 2018, and four typical provinces are discussed in detail. By decomposing the consumption rate into six factors (transmission structure, generation proportion, power self-sufficiency, resource development, resource utilization, and power consumption), and focusing on the transmission structure factors, this paper further clarifies the contributions of local use, ultra-high-voltage (UHV) transmission, and conventional transmission to the improvement of curtailment. The results reveal the following. (1) At the national level, the power mixing effect and the resource development effect have played major roles in the change of the consumption rate, but the influences of these two effects have been decreasing. (2) The UHV power lines provide a robust resource for the inter-provincial deployment of renewable power (such as the RE power transfer in Inner Mongolia and Gansu), but the role of UHV transmission has not yet been fully utilized. (3) The impact of the electricity consumption effect on renewable power consumption is increasing year by year, but the local consumption capacity remains insufficient (both at the national level and in the four typical provinces analyzed), so it is necessary to further increase the share of renewable electricity in UHV power transmission and the local consumption capacity. (4) The expanding development of wind and solar power may be the reason for the curtailment in Shaanxi, and a reasonable renewable power development plan must be further formulated.

Assessment of the solar energy accommodation capability of the district integrated energy systems considering the transmission delay of the heating network

With the rapid development of global solar energy applications in recent years, it is crucial to establish a scientific assessment method for solar energy accommodation capability to formulate a rational solar energy utilization scheme. With consideration of two typical solar utilization technologies, photovoltaic and solar thermal collecting technology, and the difference between the energy grades of electricity and heat, new assessment indicators are proposed to investigate the accommodation capability of solar energy. Based on the proposed assessment indicators, an assessment model is carried out to determine the solar accommodation capability of district integrated electrical and heating systems (DIEHSs) considering transmission delay of district heating network (DHN), which can inform the development of an optimal solar accommodation scheme. The effectiveness of the assessment model is verified through a DIEHS consisting of an IEEE 33-bus system and a DHN in China. Finally, specific strategies for enhancing solar energy accommodation capability are given by analyzing the influences of transmission delay of DHN, operating temperature range, solar energy curtailment rate, and energy storage on solar energy accommodation capability.

Optimum allocation of battery energy storage systems for power grid enhanced with solar energy

Penetrations of renewable energy sources, particularly solar energy, are increasing globally to reduce carbon emissions. Due to the intermittency of solar power, battery energy storage systems (BESSs) emerge as an important component of solar-integrated power systems due to its ability to store surplus solar power to be used at later times to avoid wastage and increase utilities profit. Conventionally, BESSs are by default placed on busses where solar farms are located, so that surplus solar power can be stored immediately without undergoing delivery over transmission lines and therefore avoid line losses. By extension, this placement method considers that each of the deployed BESS is dedicated to only the local solar farms. In other words, BESSs are deployed without considering network topology and cooperation among BESSs by pooling all their capacities to store surplus solar power is limited. Therefore, this paper proposes a method that optimally deployed BESSs and determined their capacity in a two-part framework to minimize solar energy curtailment, by considering network topology and power flow constraints. Results demonstrate that our proposed method is more efficient than the conventional deployment strategy in that it manages to store more surplus solar power.

Efficient deployment of solar photovoltaic stations in China: An economic and environmental perspective

Solar photovoltaic (PV) systems have developed rapidly in China, and the issues on where to locate the solar PV stations become critical. In some provinces, the markets are already saturated, and even solar energy curtailment has occurred due to oversupply. Thus, depicting an efficient deployment picture of the solar PV stations in China is in urgent need. To explore this issue, a profit-maximizing model is proposed to optimize the allocation of solar PV stations and interprovincial transmission strategies. The profit of solar PV stations in each province/municipality is evaluated from both economic and environmental perspectives. The economic performance is evaluated by net present value and levelized cost of energy, and the environmental benefits refer to the abatement of CO2 emissions. The results show that the northwestern and northeastern regions are not suitable to further develop solar PV systems in recent years, while the developed regions including the Yangtze River Delta, Tianjin, Beijing, and Guangdong should be encouraged to deploy more solar PV systems, especially the distributed solar photovoltaics for the commercial/industrial sectors. In 2020, the newly-added installed capacity in Zhejiang, Shandong, Beijing, and Henan is supposed to excess 8500 MW.

Large scale and rooftop solar generation in the NEM: a tale of two renewables strategies

Solar generation has increased dramatically over recent years, breaking world records of meeting 100% of the electricity demand on occasion in Australia’s National Electricity Market (NEM). Solar output variability coupled with rooftop solar impact on demand as a behind-the-meter resource poses challenges to electricity price stability. Using 30-minute intraday data, we investigate how electricity spot prices are impacted by the rapid uptake of large-scale and rooftop solar generation in the NEM. We find that, on average, both large-scale and rooftop solar generation depress the level of spot prices and positively impact price volatility. However, over 30-minute intervals, we find that solar generation tends to increase electricity prices in the early morning and in the evening due to the high cost of traditional fossil-fuelled generators required to meet the lost output when the sun is below the horizon. While large-scale solar generation typically smooths spot price volatility, rooftop solar tends to increase it, reflecting the dominance of axis-tracking systems in the former and north-facing systems in the latter. The increasing incidences of variable renewable energy curtailment undermine the merit order effect in the middle of the day but with the benefit of reducing price variability. Moreover, solar generation impact on electricity prices differs substantially across seasons. Our results stress the need for policy adjustment to increase the correlation between solar output and electricity demand through small-scale renewable energy schemes and state-based policies, rooftop solar curtailment, dynamic feed-in tariffs, and two-sided market reform.

Оптимізація диспетчеризації генеруючих потужностей енергосистеми за умови обмеження генерації вітрових та сонячних електростанцій

To solve the actual task of finding optimal generation mix and dispatch of thermal and nuclear power units, and hydro units of hydro pumped storages of Ukraine to cover day load power profile according to one of possible wind and solar generation curtailment mode the modification of mathematical MIP model is proposed. There are three modes of wind and solar generation curtailment revised in the article: 1) absolute maximum generation curtailment, 2) single for whole day decreased load factor, and 3) one per one hour decreased load factor. The possibility to allocate an exogenously determined level of frequency containment reserves (secondary reserves) on thermal power units is realized in the MIP model. The calculation’s results analysis shows that among revised wind and solar generation curtailment methods the method 2) is most appropriate in the short term because only administrative measures implementation is required, which could be put into force with appropriate legislation and does not require essential investments or implementation of complicated technical measures. The additional possible positive effect caused by the implementation of method 2) is it makes background for participation wind and solar generation in the ancillary services market and intraday balancing. In the middle term, the gradual implementation of method 3) is the most appropriate decision because a more stable power system balancing mode (minimum import/export amounts) could be provided. Moreover, extra nuclear power units and fewer coal thermal power units could be dispatched that is decreases hazard pollutions and carbon emission. The MIP model is written using MathProg language, a freeware version of AMPL. As a solver, the GNU GLPK program is used. The overall time for one calculation with standard table PC is about 30 seconds. MIP model could be used both for short-term power system optimal dispatch and for long-term national generation mix development studies under the growth rates of renewable installed capacities. Keywords: power system, daily load profile, robust decisions, mixed linear-integer problem, frequency containment reserve

Bottlenecks and Countermeasures of High-Penetration Renewable Energy Development in China

China has become the world’s largest producer and consumer of energy, and ranks first in its wind and solar power installation capacity. However, serious wind and solar curtailment in China has significantly hindered the development and utilization of renewable energy. To address problems in the consumption of renewable energy, this paper analyzes four key factors affecting the capacity of power generated from renewable energy sources: power balance, power regulation performance, transmission capacity, and load level. Focusing on these bottlenecks, we propose seven solutions: centralized and distributed development of renewable energy, improving the peak-load regulation flexibility of thermal power, increasing the proportion of gas turbines and pumped-hydropower storage, construction of transmission channels and a flexible smart grid developing demand response and virtual power plants, adopting new energy active support and energy storage, and establishing appropriate policies and market mechanisms. The Chinese Government and energy authorities have issued a series of policies and measures, and in the past three years, China has had remarkable achievements in the adoption of renewable energy. The rate of idle wind capacity decreased from 17% in 2016 to 7% in 2018, and that of solar decreased from 10% in 2016 to 3% in 2018.

Well-to-wheels emissions, costs, and feedstock potentials for light-duty hydrogen fuel cell vehicles in China in 2017 and 2030

Hydrogen has the potential to contribute to a clean, secure, and affordable energy future. It can provide distributed energy storage for intermittent renewable energy resources and support hydrogen-based transportation. This study reports well-to-wheels greenhouse gas (GHG) and criteria air pollutant emissions and levelized cost of driving (LCD) for light-duty fuel cell vehicles (FCVs) in China in 2017 and 2030, powered with hydrogen from renewable and conventional sources. All FCV pathways, except electrolysis using grid electricity or liquefied hydrogen pathways, have GHG, volatile organic compounds (VOCs), nitrogen oxides (NOX), fine particulate matter (PM2.5), and sulfur dioxide (SO2) emissions lower than, or comparable to, gasoline vehicles. Electricity sources strongly influence the environmental impacts for electrolysis-based hydrogen pathways. For FCV GHG, NOX, PM2.5, and SO2 emissions to be lower than gasoline vehicles, the share of coal-fired electricity used in hydrogen production must be less than 50%, 58%, 20%, and 34%, respectively; the share of coal in electricity generation in China was ~65% in 2017 and is projected to be ~50% in 2030. A case study shows that additional electricity is required to supplement wind curtailment to achieve higher hydrogen production volumes thus lowering production cost. Assuming decreased costs for both hydrogen production and FCVs in 2030, the LCD for wind-electrolysis FCV pathway (~$0.31/km) could approach that for gasoline (~$0.29/km) and battery electric vehicles (~$0.30/km). Wind and solar curtailment were 27.7 TWh and 5.5 TWh in 2018 and could be used to produce hydrogen for 4.9 million FCVs.

Integration Design and Operation Strategy of Multi-Energy Hybrid System Including Renewable Energies, Batteries and Hydrogen

In some areas, the problem of wind and solar power curtailment is prominent. Hydrogen energy has the advantage of high storage density and a long storage time. Multi-energy hybrid systems including renewable energies, batteries and hydrogen are designed to solve this problem. In order to reduce the power loss of the converter, an AC-DC hybrid bus is proposed. A multi-energy experiment platform is established including a wind turbine, photovoltaic panels, a battery, an electrolyzer, a hydrogen storage tank, a fuel cell and a load. The working characteristics of each subsystem are tested and analyzed. The multi-energy operation strategy is based on state monitoring and designed to enhance hydrogen utilization, energy efficiency and reliability of the system. The hydrogen production is guaranteed preferentially and the load is reliably supplied. The system states are monitored, such as the state of charge (SOC) and the hydrogen storage level. The rated and ramp powers of the battery and fuel cell and the pressure limit of the hydrogen storage tank are set as safety constraints. Eight different operation scenarios comprehensively evaluate the system’s performance, and via physical experiments the proposed operation strategy of the multi-energy system is verified as effective and stable.

Renewable Energy Curtailment: Prediction Using a Logic-Based Forecasting Method and Mitigation Measures in Kyushu, Japan

High variable renewable energy (VRE) penetration led to the first-ever VRE curtailment in Japan, occurring in Kyushu in October 2018. Since then, there has been an average of 3% solar curtailment, with a peak of 13.7% in April 2019, resulting in approximately ¥9.6 billion of wasted energy. The VRE curtailment is expected to worsen as VRE penetration continues to increase along with nuclear energy increment in line with Japan’s 2030 energy goals. To prevent this curtailment and increase energy stability, a novel, logic-based forecasting method using hourly supply/demand data was developed. Initially, inaccurate results were returned; however, after several rounds of calibration that adjusted the quartile value of the max/min operating windows, the overall accuracy of this method was increased to 97% of real curtailment. This calibrated model was then used to test several curtailment mitigation scenarios. Some scenarios increased curtailment, while the two most successful scenarios, which reduced the installed nuclear capacity either seasonally or totally, limited curtailment by 95% and 97%, respectively. Another scenario with increased grid interconnection between regions reduced curtailment by 79%. Moreover, it would provide other benefits by unifying the national grid thereby increasing disaster resistance, reducing curtailment, improving grid flexibility and allowing for higher VRE penetrations. Currently, the situation is worsening, and some actions are required to reduce the curtailment and to achieve its 2030 energy goals in Japan. The mitigation measures studied by the logic method could be recommended to be referred to.

Statistical Modeling of Networked Solar Resources for Assessing and Mitigating Risk of Interdependent Inverter Tripping Events in Distribution Grids

It is speculated that higher penetration of inverter-based distributed photo-voltaic (PV) power generators can increase the risk of tripping events due to voltage fluctuations. To quantify this risk utilities need to solve the interactive equations of tripping events for networked PVs in real-time. However, these equations are non-differentiable, nonlinear, and exponentially complex, and thus, cannot be used as a tractable basis for solar curtailment prediction and mitigation. Furthermore, load/PV power values might not be available in real-time due to limited grid observability, which further complicates tripping event prediction. To address these challenges, we have employed Chebyshev's inequality to obtain an alternative probabilistic model for quantifying the risk of tripping for networked PVs. The proposed model enables operators to estimate the probability of interdependent inverter tripping events using only PV/load statistics and in a scalable manner. Furthermore, by integrating this probabilistic model into an optimization framework, countermeasures are designed to mitigate massive interdependent tripping events. Since the proposed model is parameterized using only the statistical characteristics of nodal active/reactive powers, it is especially beneficial in practical systems, which have limited real-time observability. Numerical experiments have been performed employing real data and feeder models to verify the performance of the proposed technique.

Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers

Summary As the share of variable renewable energy (VRE) grows in the electric grid, so does the risk of curtailment. While energy storage and hydrogen production have been proposed as solutions to the curtailment problem, they often pose technological and economic challenges. Here, we analyze the potential of data center load migration for mitigating curtailment and greenhouse gas (GHG) emissions. Using historical hourly electricity generation, curtailment, and typical data center server utilization data, we simulate the effect of migrating data center workloads from the fossil-fuel-heavy PJM to the renewable-heavy CAISO. The results show that load migration within the existing data center capacity during the curtailment hours in CAISO has the potential to reduce 113–239 KtCO2e per year of GHG emissions and absorb up to 62% of the total curtailment with negative abatement costs in 2019. Our study demonstrates the overlooked role that data centers can play for VRE integration and GHG emissions mitigation.

Probabilistic State Forecasting and Optimal Voltage Control in Distribution Grids under Uncertainty

Nowadays, the uncertainty in distribution systems rises, notably due to an increasing share of solar panels and electric vehicles whose power production and consumption are characterized by a high volatility. This poses challenges to distribution system operators to ensure stable and secure operation of their grid. Hence, an optimal integration of these distributed energy resources in real-time control schemes inevitably relies on appropriate forecasts of the near-future system state. This paper investigates the short-term probabilistic state prediction of low-voltage grids for operation purposes. The performance of two quantile forecasting algorithms is evaluated for different levels of distributed energy resources penetration and availability of measurements. Quantile forecasts are finally integrated into the framework of an optimization problem that aims at minimizing the costs associated with overvoltages by suitable solar power curtailment. The advantages of quantile forecasts considering different imbalance prices are demonstrated.

An investment decision framework for photovoltaic power coupling hydrogen storage project based on a mixed evaluation method under intuitionistic fuzzy environment

Abstract The photovoltaic power coupling hydrogen storage (PVPCHS) system has been considerably valued due to the solar curtailment phenomenon as well as the long-term and large-scale energy storage characteristic of hydrogen energy. And the investment decision of PVPCHS project is a novel multi-criteria decision-making (MCDM) problem, thus it is necessary to establish a decision framework considering the characteristics of PVPCHS project. First, the criteria system that includes resource, economy, environmental and social aspects is established. Second, in consideration of the uncertainty in the decision-making process, this paper applies the TIFN-fuzzy DEMATEL method to the weight determination stage and employs the TIFN-fuzzy TODIM method to rank the alternative PVPCHS projects. Finally, a case study in Inner Mongolia of China is executed to prove the validity of the hybrid evaluation method. The results indicate that the scheme A3 located in Hohhot possesses the best comprehensive performance. Moreover, the sensitivity analysis reveals the necessity to set up diverse scenarios based on the individual demands of decision makers. Meanwhile, the comparative analysis results show the exceptional applicability of the hybrid evaluation method. This paper can not only be adopted by investors to make decisions, but also be referred by further investigators.

Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study

To mitigate solar curtailment caused by large-scale development of photovoltaic (PV) power generation, accurate forecasting of PV power generation is important. A hybrid forecasting model was constructed that combines random forest (RF), improved grey ideal value approximation (IGIVA), complementary ensemble empirical mode decomposition (CEEMD), the particle swarm optimization algorithm based on dynamic inertia factor (DIFPSO), and backpropagation neural network (BPNN), called RF-CEEMD-DIFPSO-BPNN. PV power generation is affected by many factors. The RF method is used to calculate the importance degree and rank the factors, then eliminate the less important factors. Then, the importance degree calculated by RF is transferred as the weight values to the IGIVA model to screen the similar days of different weather types to improve the data quality of the training sets. Then, the original power sequence is decomposed into intrinsic mode functions (IMFs) at different frequencies and a residual component by CEEMD to weaken the fluctuation of the original sequence. We empirically analyzed a PV power plant to verify the effectiveness of the hybrid model, which proved that the RF-CEEMD-DIFPSO-BPNN is a promising approach in terms of PV power generation forecasting.

Stochastic Online Generation Control of Cascaded Run-of-the-River Hydropower for Mitigating Solar Power Volatility

To exploit the massive solar energy available in the region, photovoltaic plants have been built in the mountain areas in Southwest China, coexisting with many small cascaded run-of-the-river hydropower plants, interconnected by short-distance transmission lines. Network constraints inside these systems are often negligible. However, due to the volatile nature of solar power and the weak connection to the external power grid, without proper coordination, solar power curtailment and water spillage are inevitable. To address this issue, this paper proposes an ultra-short-term stochastic generation control method for cascaded hydropower to mitigate solar power volatility. Two technical challenges are specifically addressed: to tackle the time-varying stochastic volatility of solar power, the Itô process model is introduced; to characterize the spatial-temporal hydraulic coupling of the cascaded hydropower plants and river operation constraints, a state-space dynamic river model derived from shallow water equations is included. Followingly, a stochastic control (SC) approach for hydropower generation based on stochastic programming is proposed, where hydropower generation commands are parameterized as affine functions of measured solar power, enabling quick response to solar power volatility. Simulation of a real-life cascaded hydro-solar system and a modified system verifies the proposed control method.

Insights of Japan’s Development Strategy of Hydrogen Society on Xinjiang’s Development in Hydrogen Production with Residual Electricity

As China’s important energy base and with its favorable geographical position, Xinjiang is the key region in the development strategy of China’s “Silk Road Economic Belt”. This paper first examines in depth the developmentstrategy and status quo of Japan’s hydrogen society, and then illustrates the current state of Xinjiang’s wind and solar energy development. Combining Xinjiang’s current situation of “wind and solar energy curtailment”, the paper focuses on discussing the necessity of hydrogen production by residual electricity in promoting Xinjiang’s renewable energy market development, and also gives analysis on the prospect and major existing obstacles of Xinjiang’s development in hydrogen production with residual electricity. The research content of this article has positive significance for the healthy development of renewable energy industry in Xinjiang.

Model Predictive Control Approach for Optimal Power Dispatch and Duck Curve Handling Under High Photovoltaic Power Penetration

In this paper, an energy management system (EMS) has been developed based on model predictive control (MPC) to optimally dispatch the power units and particularly handle the duck curve fast ramping events. The methodology is specifically developed considering higher penetration of solar photovoltaic power subjected to realistic physical constraints. Battery energy storage, load shedding and solar curtailment have been utilized to effectively control the duck curve fast ramping events. The proposed system has been assessed with the help of a case study using a 24-bus RTS system. Consequently, detailed flexibility analyses were carried out and it has been proven that the given energy management and control system is capable of handling fast ramping events of duck curve. Furthermore, it has been observed that the overall operation cost of the system is also minimized. The performance of the developed model is compared with traditional non-MPC based mixed-integer linear programming approaches and it has been concluded that MPC-based optimization is more economical and effective in handling the duck curve challenges.

Research on New Energy and Thermal Power Proportion Comparison of UHVDC Generation Expansion Planning

Generation expansion planning of UHVDC plays a crucial role in optimizing resource utilization. A mixed delivery of wind, solar, and thermal power can help achieve multiple objectives such as to stabilize power supply, to raise renewable energy ratio, to reduce the wind and solar curtailment rate, while keeping the price acceptable. This paper considers 4 objectives of generation planning of UHVDC and formulates a model to simulate the operation and thus get the key indices that are used to choose from possible generation expansion planning options. We show the effect of the proposed method using a case study of the Xinjiang 3rd UHVDC channel.

Private Versus Shared, Automated Electric Vehicles for U.S. Personal Mobility: Energy Use, Greenhouse Gas Emissions, Grid Integration and Cost Impacts

Transportation is the fastest-growing source of greenhouse gas (GHG) emissions and energy consumption globally. While the convergence of shared mobility, vehicle automation, and electrification has the potential to drastically reduce transportation impacts, it requires careful integration with rapidly-evolving electricity systems. Here we examine these interactions using a U.S.-wide simulation framework encom- passing private electric vehicles (EVs), shared automated EVs (SAEVs), charging infrastructure, controlled EV charging, and a grid economic dispatch model to simulate personal mobility exclusively using EVs. We find that an SAEV fleet 9% the size of today’s active vehicles can satisfy trip demand with only 2.6 million chargers (0.2 per EV). Controlled EV charging can also reduce electricity demand variability, significantly lowering GHG emissions and decreasing solar curtailment by about one- third. While private EVs with uncontrolled charging would reduce GHG emissions by 53% compared to gasoline vehicles, SAEVs could achieve a 70% reduction.