Power Curtailment Research Articles

Optimal planning of energy storage system under the business model of cloud energy storage considering system inertia support and the electricity-heat coordination

As the penetration rate of renewable energy increases in the electric power system, the issues of renewable power curtailment and system inertia shortage become more severe. Innovative solutions such as Cloud Energy Storage (CES) can be employed to address this challenge. However, the energy storage resources aggregated by the traditional CES business model mainly concentrate on Electrical Energy Storage (EES), which is still limited and expensive. It necessitates the exploration of new approaches to enhance the flexibility and cost-effectiveness of energy storage utilization, in which using District Heating System (DHS) as an equivalent energy storage resource of the power system is an effective method. Therefore, this paper proposes an optimal planning strategy of energy storage system under the CES model considering inertia support and electricity-heat coordination. Firstly, the system components and business model of the CES are described, and the framework of energy storage planning problem from the perspective of CES operator is formulated. Then the evaluation methods of energy storage utilization demand from CES users are proposed, including the evaluation of the renewable power curtailment, system minimum inertia requirement, and the equivalent energy storage ability of DHS. Based on this evaluation results, a bi-layer optimal energy storage planning model for the CES operator is established, where the upper-layer model determines the installed capacity of lithium (Li-ion) battery station and the lower-layer model determines the optimal schedules of the CES system. The numerical tests based on the operation profile of a typical city in China are carried out to demonstrate the effectiveness of the proposed method. The simulation result shows that the annual profit of the CES system can be improved by 15.26% after installing the energy storage system whose capacity is determined by the proposed method.

Methods for increasing the potential of integration of EV chargers into the DC catenary of electric transport grids: A trolleygrid case study

The traction substations of urban electric transport grids are oversized and underutilized in terms of their capacity. While their over-sizing is an unfortunate waste, their under-utilization creates the major hurdle for the integration of renewables into these grids due to the lack of a base load. Therefore, integrating smart grid loads such as EV chargers is not only an opportunity but a necessity for the sustainable transport grid of the future.This paper examines six methods for increasing the potential of EV chargers in three case studies of a trolleygrid, namely a higher substation no-load voltage, a higher substation power capacity, a smart charging method, adding a third overheard parallel line, adding a bilateral connection, and installing a multi-port converter between two substations. From the case studies, the most promising and cost-effective method seems to be introducing a bilateral connection, bringing a charging capacity for up to 175 electric cars per day. Meanwhile, other costly and complex methods, such as smart charging with grid state sensors and communication, can offer charging room for over 200 electric cars per day. Furthermore, using solar PV systems to power the grid showed a more than doubling of the directly utilized energy by installing a 150kW charger, from 19% to 41%. This reduces the power mismatch between the trolleygrid and the PV system from 81% to 59% and thereby reduces the severe economic need for storage, AC grid power exchange, or PV power curtailment while allowing a high penetration of renewables.

Analysis of System Balancing and Wind Power Curtailment Challenges in the Ethiopian Power System under Different Scenarios

In this paper, an hourly dispatch model was developed to analyze the system balancing and wind power curtailment challenges in the future of the Ethiopian electric power grid system. The developed model was validated using historical data and was used for the analysis of the grid system in 2030 with different scenarios. The model was used to examine the impacts of transmission capacity, regulation reserve requirement, and daily minimum generation of hydropower for irrigation with three cases of wind annual energy share of 14.5%, 17.8%, and 25.2%. Thus, the curtailment was found to be below 0.2%, 1.1%, and 9.8% for each case, respectively. The cost of wind energy increases in proportion to the percentage of curtailment and the increase in transmission line capacity. Reducing the minimum hydropower generation results in smaller wind power curtailment and better generation–consumption balancing.

A Coordinated Voltage Regulation Algorithm of a Power Distribution Grid with Multiple Photovoltaic Distributed Generators Based on Active Power Curtailment and On-Line Tap Changer

The aim of this research is to manage the voltage of an active distribution grid with a low X/R ratio and multiple Photovoltaic Distributed Generators (PVDGs) operating under varying conditions. This is achieved by providing a methodology for coordinating three voltage-based controllers implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS). The first controller is for the On-Line Tap Changer (OLTC), which computes its adequate voltage reference. Whereas the second determines the required Active Power Curtailment (APC) setpoint for PVDG units with the aim of regulating the voltage magnitude and preventing continuous tap operation (the hunting problem) of OLTC. Finally, the last component is an auxiliary controller designed for reactive power adjustment. Its function is to manage voltage at the Common Coupling Point (CCP) within the network. This regulation not only aids in preventing undue stress on the OLTC but also contributes to a modest reduction in active power generated by PVDGs. The algorithm coordinating between these three controllers is simulated in MATLAB/SIMULINK and tested on a modified IEEE 33-bus power distribution grid (PDG). The results revealed the efficacy of the adopted algorithm in regulating voltage magnitudes in all buses compared to the traditional control method.

Real-Time Transient Simulation and Studies of Offshore Wind Turbines

The real-time models of offshore wind turbine generators complied with industry standards are presented in this paper. The developed models provide essential capabilities for future electromagnetic transient controls and testing offshore wind farms, such as low-voltage ride-through, active/reactive power support, transient current limiting. Furthermore, the proposed models provide an additional capability of controlling negative sequence current, which meets the future requirements for protecting wind farm systems. This paper presents average-value and detailed switching real-time models in the Opal-RT real-time simulator. The efficacy of the proposed real-time models in terms of computational efficiency is demonstrated by comparing them with the non-real-time models. Extensive case studies are performed to demonstrate the control functions of the proposed models, such as dynamic-wind condition, power curtailment, low-voltage and fault ride-through, negative sequence current control, etc. The wind turbine model validation against the generic model presented by the Western Electricity Coordinating Council (WECC) is conducted to show their compliance with industry standards utilized by WECC. A real-time model of a 450 MW offshore wind farm is developed to show the feasibility of large-scale wind farm modeling.

Coordinated Heat and Power Cyber-Attacks With Time Window Matching Strategy

The integration of information and communication infrastructures has dramatically increased the cyber vulnerability of integrated energy systems (IES). Due to the coupling among various energy systems, cyber-attacks on one sub-energy system may transmit to and affect other sub-energy systems, subsequently threatening the security of the whole IES. This paper develops a destructive coordinated heat and power cyber-attack for the first time, analyzes its risk on the heat and electric integrated energy systems, and reveals its potential consequences. In the developed coordinated attack, a bi-level attacker-operator model is formulated to identify the worst-case scenario caused by the coordinated attack. Two types of false data injection attacks are considered in this coordinated attack strategy: the improved load redistribution attack on the power system and the false heat load attack on the heating system. In order to accurately characterize the stealthiness and adverse effect of the coordinated attack, a time window matching strategy that handles the delay characteristics of the heating system and the latency property of the false heat load attack is proposed, along with the matching of attack parameters in different attacks. Simulation results show that the proposed coordinated attack significantly impacts the system in economic losses, load shedding, renewable power curtailment, and line overload.

A method to control distributed energy resources in distribution networks using smart meter data

Voltage regulation has been one of the major challenges for distribution network operators (DNOs) due to the integration of distributed energy resources in the recent years. Control approaches mitigating over-voltage (OV) generally require full network model, which for low-voltage distribution networks (LVDNs), can be inaccessible to DNOs. This paper presents a novel data-driven model-free control framework for improving voltage regulation in 4-wire 3-phase LVDNs. In the proposed approach, first, using smart meter data, the aggregated resistance and reactance matrices of the network are estimated. Then, these matrices serve as an input for the proposed optimal power flow (OPF) method to control the DERs’ active and reactive power outputs. This method also lets the DNO adjust the fairness in prosumers’ generation curtailment. The performance of the proposed approach is evaluated in various case studies conducted on a real-world low-voltage test feeder. The simulation results exhibit significant effectiveness in solving the OV problem associated with DERs. For example, in the studied network, the proposed approach limits the maximum voltage of the network to 1.1 pu, while without having control over the DERs output power, it can even overpass 1.17 pu during the peak generation period. In addition, utilizing DERs’ reactive control capability reduces the amount of active power curtailment, and prosumers are able to collectively inject 5.66% more active power.

Multi-Time Scale Economic Dispatch of Integrated Electricity and Natural Gas Systems with Flexibility Constraints Based on Chance-Constrained Programming

The connection between various energy types in the integrated power and natural gas system has grown stronger in recent years, as has the penetration rate of clean energy. Wind power generation volatility offers a considerable barrier to power system operation. This research provides a multi-time scale economic dispatch model with flexibility limitations to address this issue. Through chance-constrained programming, the equipment flexibility is described by probability functions and predetermined confidence levels in this model, and the generating cost and wind power consumption are improved through day-ahead and intra-day optimal scheduling. Finally, the effectiveness of the proposed model is verified by two case studies of integrated energy systems, where the results show that about 68.0–72.1% wind power curtailment can be effectively reduced while satisfying all load and system safety requirements.

A continuous-time voltage control method based on hierarchical coordination for high PV-penetrated distribution networks

The photovoltaic (PV) integration brings both fast photovoltaic generation (PVG) variations and a large number of PV inverters to the distribution networks (DNs). The management of the PVG variation and the PV inverter is an urgent problem. The discrete-time model can’t describe PVG variations during the schedule interval and the regulation potential of the PV inverter is limited by a fixed dispatching value. This paper proposes a continuous-time voltage control method based on hierarchical coordination for high PV-penetrated DNs. In the central schedule, based on the Bernstein polynomial method, continuous-time models of PVGs are established and reference values of local inverter control curves are dispatched as continuous-time functions in a sub-hourly timescale. In the local control, reactive power and active power curtailment of PV inverters are set as the reference values, i.e., the corresponding values of the continuous-time functions, every 30 s to optimize the central dispatching objective under the voltage constrains, and deviate from the reference values according to both the local control curve and voltage measurement only when the real-time bus voltage violation occurs. In addition, the cooperation between inverter and mechanical equipment in the continuous-time framework is also considered, and the Bernstein polynomial method is developed to the continuous-time modeling of voltage control in DNs. The simulation shows that the proposed method has lower maximum voltage magnitude and lower energy loss compared with discrete-time central schedule method.

Screening and ranking Japanese gas fields for underground H2 storage potential: Impact of the reservoir drive mechanism

To accomplish the net-zero goal, Japan intends to increase its reliance on renewable energies such as solar power (36–38 % in 2030). However, considering the intermittent nature of solar power, the resulting energy must be transformed to a secondary energy carrier such as H2. Japan, on the other hand, is expected to import 20 × 106 tons of H2 each year by 2050. Because of these large volumes, new H2 storage techniques must be developed, in particular underground hydrogen storage (UHS). This paper ranks eight gas reservoirs out of 106 oil and gas fields in four Japanese prefectures regarding their suitability for UHS, and then suggests a H2 injection and withdrawal scheme based on the history of electricity consumption and solar power curtailment. Then, a reservoir simulation study was performed to investigate the effects of factors such as hysteresis, repeated cycles and cushion gas on the performance of these fields. The outcomes for water-drive and volumetric gas reservoirs were compared to better understand the effects of an aquifer on the long-term performance of a gas reservoir when used for UHS. Finally, it was demonstrated that the technology sought for the conversion of H2 back to electricity after withdrawal from a UHS site governs the overall efficiency of a power-H2-power process.

Multi-objective optimization of distribution network considering battery charging and swapping station

Battery charging and swapping station (BCSS) can provide flexibility for the distribution network due to accumulating a large number of batteries. This paper proposes a multi-objective optimization method of distribution network considering BCSS, aiming to minimize the transmission losses, voltage deviation and curtailment of wind and photovoltaic power. Besides, The present study puts forth a novel integrated modeling methodology for batteries, which is founded upon the State of Charge (SOC) interval. This approach includes the total energy constraint, as well as the charging and discharging priorities of the batteries. Finally, numerical tests are carried out to validate the correctness and rationality of the presented method. The outcomes of the study show that 2% reduction in wasted energy is achieved with BCSS.

A Hierarchical Multi-Stage Coordination of Inverters for Voltage Control in Active Distribution Networks with a µPMU-PDC

This paper proposes a hierarchical multi-stage approach based on a distributed level phasor measurement unit (µPMU) at local controllers and a phasor data concentrator (PDC) at the central control unit to restore system voltage when it exceeds the limits recommended by the IEEE 1547-2018 standard. The proposed algorithm does not need an accurate system model or employ optimization solutions. Therefore, it has less implementation complexity and would be popular among distribution network service providers (DNSPs) and distribution network operators (DNOs) as it does not suffer from cost and computational complexity limitations. A PMU-PDC-based communication platform has been developed as a more efficient alternative to the supervisory control and data acquisition (SCADA) system, and provides superior characteristics, including a higher sample rate, higher data resolutions, and faster communication. The proposed coordinated algorithm aims to postpone power generation curtailment in distributed energy resources (DERs) with overvoltage problems (local DERs) by incorporating all the DERs that are not subjected to voltage violation (remote DERs) by absorbing their maximum reactive power capacity. If the system voltage has not recovered after absorbing all of the reactive power capacity of all the DERs, a reduced active power curtailment proposed by the algorithm is then applied to the system to control the voltage. The proposed strategy has been simulated in MATLAB and applied to IEEE 13-bus and IEEE 33-bus radial distribution benchmark systems to validate the performance of the system, in terms of its ability to coordinate voltage control and the accuracy of the PMU-PDC-based communication interface.

Multi-objective reactive power optimization strategy of power system considering large-scale renewable integration

To fully tap the abilities of renewables in reactive power optimization, this paper develops a detailed model for the power regulation capabilities of wind turbines and photovoltaic units and studies their impact on the power system’s operation. First, the power system model with renewables integration is established using AC power flow. The wind turbines and photovoltaic units are modeled in detail according to their topologies and operating characteristics, and then further simplified according to the feasible region. An improved DC power flow model is adopted to handle the non-linear characteristics of the power system. On this basis, a multi-objective reactive power optimization model is constructed to minimize the power generation cost, wind and solar power curtailment, and voltage offset. Finally, comparisons between two types of models in different scenarios are designed. Numerical simulations demonstrate that the participation of renewables in reactive power regulation can improve the operational economy and voltage stability of power systems.

Techno-economic impacts of Volt-VAR control on the high penetration of solar PV interconnection

This paper investigates the operation and control of smart inverters (SI) for solar and solar-plus-storage systems with an emphasis on Volt-VAR Control (VVC) at solar interconnections. The paper provides techno-economic recommendations for both normal-size and oversized solar inverters equipped with VVC. The objective is to minimize the voltage violations, and active power curtailment by providing reactive power support to the grid while the IEEE 1547-2018 standards are satisfied. Moreover, a collaborative VVC is tested where a battery storage system is paired with the solar facility which is becoming a popular configuration to enhance energy resilience in communities. An unbalanced distribution network is modeled based on a modified IEEE 13-bus system to which three 700 kW solar PV plants are connected through smart inverters. Solar data from the University of Louisiana’s 1.1 MW solar farm is used and grouped into 28 clusters. Those clusters represent a 2-year period with 15-min granularity. Typhoon HIL 402 real-time simulator is utilized for modeling and verifying the proposed approach. A control prototype is also developed on dSPACE MicroLabBox to further investigate the interaction between the controller and the rest of the system in a more realistic testing environment.

Cause Analysis and Countermeasures of Wind Power Curtailment

The current world has numerous issues, such as insufficient electrical supply, excessive fossil fuel consumption, and environmental contamination. As a rapidly increasing renewable energy in recent years, wind energy plays a significant role in mitigating these issues. Nevertheless, the rapidly expanding wind energy business has led to the emergence of the problem of wind power delays. Using the existing state of wind power in China as a starting point, this article examines the causes of curtailment of wind power and the obstacles that must be overcome to improve the situation. The objective of this study is to examine the current problem of wind curtailment in China, its causes, and the problems it faces in order to give readers with further suggestions for solving wind power curtailment problem and promoting the wind power industry's continued progress toward low-cost, high-quality mature energy.

An analytical method to evaluate curtailment of hydro–photovoltaic hybrid energy systems and its implication under climate change

Identifying power curtailment is essential for system reliability, especially in hydro–photovoltaic (PV) hybrid energy systems (HPESs) exposed to future climate variability. However, the relatively low temporal resolution of climate projections challenges the effectiveness of current numerical simulation methods for evaluating the curtailment rate of HPESs. This study derives an analytical method based on daily hydropower and PV power rather than the commonly used hourly data in numerical methods, to reduce reliance on the temporal resolution of climate inputs. A short-term operation model is constructed to verify the effectiveness of the derived power curtailment function. A long-term operation model incorporating the analytical power curtailment function is then established to analyze variations in future curtailment rates using climate information projections. Results of a case study in the Yalong River Basin validate the accuracy of the derived PV curtailment function. Compared with the reference period, future hydropower and PV power will increase in most climate scenarios with substantial variances, whereas the PV curtailment rate exhibits an overall increase in the near future (+2.49%) and a larger increase in the far future (+5.53%). The most noticeable increases in future PV curtailment rates occur in June, July, and October (+3.15%, +5.47%, and +3.11%). The proposed analytical method provides valuable insights for the risk evaluation of HPESs by coupling the short-term power curtailment rate with mid- and long-term climate variations.

Multi-objective two-stage stochastic unit commitment model for wind-integrated power systems: A compromise programming approach

In this paper, we introduce a compromise programming (CP) framework for solving a multi-objective two-stage stochastic unit commitment problem characterized by high penetration of wind power. The proposed framework aims at finding best-compromise Pareto efficient on/off schedules, accounting for wind and power demand uncertainties: such solutions must trade off the three objectives of operating cost, CO2 emissions, and wind power curtailment in accordance to the decision maker preferences. To achieve this, we introduce a practical procedure to compute the ideal and Nadir points associated to the multi-objective two-stage stochastic unit commitment problem and propose a linearized ℓ1 norm-based compromise program to design best-compromise on/off schedules that correspondingly minimize and maximize their weighted distances to the ideal point and to the Nadir point, considering the preference weights assigned by the decision maker to each of the three objective functions. The proposed CP framework is applied to a case study related to the New England IEEE-39 bus test system. The results show that, compared to the schedule obtained through the traditional minimization of operating cost, the designed best-compromised schedules considerably improve CO2 emissions reduction and wind power curtailment performance by conservatively sacrificing operating cost performance.

Point-on-Wave Voltage Phase-Angle Jump Sensitivity Analysis of Grid-Connected Single-Phase Inverters

An extensive experimental analysis of the behavior of thirty-one off-the-shelf distributed photovoltaic (DPV) inverters to voltage phase angle jump (VPAJ) disturbance is done in this paper. Classification of undesirable behavior from DPV inverters is carried out based on the responses to voltage phase angle jump and identified the cause of such behavior through simulation and experimental evidence. Grid-connected inverters can respond undesirably to grid disturbance, such as disconnection and power curtailment, which can put <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ 3-5$</tex-math></inline-formula> % of demand at risk and a cumulative <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ 3-4$</tex-math></inline-formula> % grid at risk. This is a much larger contingency than before. Further, statistical analysis demonstrates the impact of such disturbances on DPV inverters in the Australian National Electricity Market. A sensitivity analysis is carried out to identify the vulnerability of inverters for VPAJ that occur at different points-on-wave (POW). This research aims to determine the scale of at-risk generation, understand how they operate, and what had to be performed to stop or reduce the risk. The ultimate impact is how to model this effect properly to guide the responses of DPV inverters, also making a significant study case internationally for systems with high penetration of DPV inverters.

Hierarchical Central-Local Inverter-Based Voltage Control in Distribution Networks Considering Stochastic PV Power Admissible Range

This paper proposes a hierarchical distribution network voltage control method considering active and reactive power coordination of PV units in both central and local control stages. In contrast to the traditional local control methods, the proposed method defines the admissible range (AR) of PV power and determines it via centralized optimization to realize active PV power curtailments in the local control stage. The affine decision rule (ADR) is adopted to control the reactive power with respect to active power within AR. A distributionally robust chance constraint is designed, based on statistical indices of active PV power, to assess the probability that active PV power generation would fall inside AR. A two-stage optimization problem that can simultaneously provide central and local control strategies is proposed, which is transformed into tractable formulas based on the Gauss Inequality as well as ADR and binary expansion techniques. The proposed method solutions are compared with those of the theoretically optimal method, a robust central-local control method, and a local control method to show the value of hierarchical inverter control with AR in reducing PV power curtailment and ensuring nodal voltages within limits.

Modeling and operation optimization of hydrogen-based integrated energy system with refined power-to-gas and carbon-capture-storage technologies under carbon trading

As a promising solution to realize the energetic complementarity and energy cascade utilization, integrated energy system with a large-scale integration of renewable energy garners considerable attention recently. However, the renewable energy curtailment and uncertainty become two prominent challenges. To the end, we establish a hydrogen-based integrated energy system (HIES) operation framework comprising a refined power to gas (P2G) and carbon capture system (CCS) under carbon trading. Subsequently, a Wasserstein metric based distributional robust optimization method (WDRO) is developed to address wind power uncertainty. Finally, numerical analysis is performed. Simulations results show that: (1) the proposed approach can realize a low-carbon electric energy supply and consumption mode and satisfies the hydrogen demand through internal equipment of the system; (2) compared with the benchmark, the proposed model reveals outstanding economic, environmental and energy performance by obtaining 31.81% decline in operation cost, a 20.19% mitigation in CO2 emission, 7.89% reduction of wind power curtailment and a higher energy efficiency; (3) the validation of WDRO model is demonstrated by the data-driven and robustness test. Moreover, the model exhibits advantages due to its trade-off between conservativeness and computational complexity. The optimization results provide a good reference for the HIES investment strategy and government carbon pricing decision.