Wind Curtailment Costs Research Articles

Probabilistic co‐expansion planning for natural gas and electricity energy systems with wind curtailment mitigation considering uncertainties

AbstractIn response to growing reliance on electricity and gas systems, this paper introduces a stochastic bi‐level model for the optimized integration of these systems. This integration is achieved through sizing and allocating of power‐to‐gas (P2G) and gas‐to‐power (G2P) units. The first level of the model focuses on decisions related to P2G and G2P unit installations, while the second level addresses optimal system operation considering decisions made from first level and stochastic scenarios. The primary aim is to enhance energy‐sharing capabilities through coupling devices and mitigate wind generation curtailment. An economic evaluation assesses the model's effectiveness in reducing costs. N − 1 contingency analysis gauges the integrated system's ability to supply load under emergency conditions. Two new indices, performance of the electricity system and performance of the natural gas system, are proposed for N − 1 contingency analysis. These indices quantify the proportion of the supplied load to the total load, thereby illustrating the system's capacity to meet demand. For numerical investigation, the proposed model is applied to a modified IEEE 14‐bus power system and a 10‐node natural gas system. Numerical results demonstrate a 9.426% reduction in investment costs and a significant 10.6% reduction in wind curtailment costs through proposed planning model.

Low-Carbon Economic Dispatch Model of Integrated Energy System Accounting for Concentrating Solar Power and Hydrogen-Doped Combustion

Against the background of carbon peak and carbon neutralization, in order to solve the problem of poor flexibility of integrated energy systems and wind power consumption while improving the potential of hydrogen energy emission reduction, this study proposes an integrated energy system that takes into account the coupling of concentrating solar power (CSP), hydrogen-doped combustion, and power-to-gas (P2G) conversion. Firstly, a mathematical model of a CSP-CHP unit is established by introducing a CSP power station, aiming at the defect of the “heat to power” mode in the CHP system. Secondly, the energy consumption of P2G hydrogen energy production is satisfied by surplus wind power. The utilization stage of hydrogen energy is divided into supply CHP combustion and CO2 methanation, forming a CSP-P2G-HCHP collaborative framework and establishing an IES low-carbon economic dispatch model with CSP-P2G-HCHP. At the same time, the carbon trading mechanism is introduced to constrain the carbon emissions of the system. Finally, an optimization strategy with the minimum sum of the operation and maintenance cost, the energy purchase cost, the wind curtailment cost, and the carbon emission cost as the objective function is proposed, and the CPLEX solver is used to solve and carry out multi-case analysis. The simulation results show that the carbon emissions are reduced by 6.34%, the wind curtailment cost is reduced by 52.2%, and the total cost is reduced by 1.67%. The model takes into account the carbon reduction effect and operating efficiency and effectively improves the new energy consumption capacity.

Optimal Scheduling of Integrated Energy System Considering Hydrogen Blending Gas and Demand Response

In the context of carbon neutrality and carbon peaking, in order to achieve low carbon emissions and promote the efficient utilization of wind energy, hydrogen energy as an important energy carrier is proposed to mix hydrogen and natural gas to form hydrogen-enriched compressed natural gas (HCNG). It is also injected into the natural gas pipeline network to achieve the transmission and utilization of hydrogen energy. At the same time, the participation of demand response is considered, the load’s peak and trough periods are adjusted, and the large-scale consumption of renewable energy and the reduction in carbon emissions are achieved. First of all, a fine model of hydrogen production and hydrogen use equipment is established to analyze the impact of adding hydrogen mixing on the economy and the low-carbon property of the system. With green certificates and demand response, the utilization rate of hydrogen energy is improved to further explore the energy utilization rate and emission reduction capacity of the system. Secondly, on the basis of modeling, the optimal scheduling strategy is proposed with the sum of energy purchase cost, equipment operation cost, carbon emission cost, wind curtailment cost, and green certificate income as the lowest objective function. Considering the constraints such as hydrogen blending ratio and flexible load ratio of the pipeline network, a low-carbon economic scheduling model of hydrogen mixed natural gas was established. The model was linearized and solved by using MATLAB 2021a and CPLEX solver. By comparing different scenarios, the superiority of the model and the effectiveness of the strategy are verified.

Low-Carbon Economic Dispatch of Integrated Energy Systems in Industrial Parks Considering Comprehensive Demand Response and Multi-Hydrogen Supply

To address the increasing hydrogen demand and carbon emissions of industrial parks, this paper proposes an integrated energy system dispatch strategy considering multi-hydrogen supply and comprehensive demand response. This model adopts power-to-gas technology to produce green hydrogen, replacing a portion of gray hydrogen and incorporates a carbon capture system to effectively reduce the overall carbon emissions of the industrial park. Meanwhile, incentive-based and price-based demand response strategies are implemented to optimize the load curve. A scheduling model is established targeting the minimization of procurement, operation, carbon emission, and wind curtailment costs. The case study of a northern industrial park in China demonstrates that the joint supply of green and gray hydrogen reduces carbon emissions by 40.98% and costs by 17.93% compared to solely using gray hydrogen. The proposed approach successfully coordinates the economic and environmental performance of the integrated energy system. This study provides an effective scheduling strategy for industrial parks to accommodate high shares of renewables while meeting hydrogen needs and carbon reduction targets.

A risk-aware and second-order cone programming model for wind expansion planning considering correlated uncertainties using the copula method

Integration of large-scale renewable energy sources (RESs) into existing power grids is challenging for power system operators and planners due to the intermittent and uncertain characteristics of RESs and the limitation of transmission capacity. This paper presents a new risk-based framework to obtain the optimal placement and sizing of wind farms in the bulk power system considering the impact of mutual correlation between wind farms’ power productions in different candidate buses. In this regard, a second-order cone programming (SOCP) optimization problem is developed to minimize the total expected social cost including the operation cost of conventional generators, wind curtailment cost, and the investment cost of newly installed wind farms. In the proposed model, the correlated uncertainties of wind power production are incorporated using the copula method. Also, the conditional value-at-risk (CVaR) measure is utilized to manage the risk of the model associated with uncertainties. The proposed planning model is then implemented into the Garver 6-bus and IEEE 118-bus test systems. Given the results, employing SOCP-based AC OPF in the presented planning model results in a precise solution, demonstrating an 87% reduction in running time. Moreover, the numerical results illustrate that by considering the correlation between wind farms’ power production and reducing the risk parameter, there is a significant increase in the installed capacity of wind farms.

Optimal scheduling of electric-gas-heat system considering dual virtual energy storage of gas network management and heat network storage

Aiming at the problem of wind curtailment caused by the lack of system flexibility, an optimal scheduling strategy for improving the flexibility of the electricity-gas-heat interconnection system by the coordinated operation of the gas network management and storage characteristics and the heat storage characteristics of the heat network is proposed. Firstly, the influence of gas network storage and heat network storage on improving flexibility is analyzed respectively. Then, an electricity-gas-heat interconnected system scheduling model considering the dynamic characteristics of gas network management and t the delay characteristics of heat storage in heat network, which greatly improves the imbalance between supply and demand of flexibility in space and time brought by the anti-peak regulation characteristics of wind power to the system. Finally, the IEEE-24 nodes power system, Belgium’s 20-node natural gas network and 6-node thermal network are used, for example, analysis. The results show that the proposed scheduling scheme improves the flexibility of the system while its operating cost and wind curtailment cost are the lowest, which promotes the consumption of wind power.

Optimal Dispatch of Integrated Energy System under “Double Carbon” Target

With the aggravation of environmental problems, countries are more and more aware of the importance of protecting the environment. In 2020, China proposed the goal of “double carbon”. As the main carbon emission industry, the energy industry will become the main battlefield. Therefore, whether the power industry can achieve low-carbon electricity and promote the accommodation of new energy will be the key to achieving carbon neutrality. Under the new background, power grid dispatching should not only consider a single economic benefit, which will lead to a large number of wind and solar curtailments, but also take into account environmental benefits and reduce CO2 emissions. In this paper, an optimal dispatching model of an Integrated Energy System(IES) is established, and CCS is added to CHP units to capture CO2 produced by CHP and send it to P2G. Aiming at the total cost optimization, carbon trading mechanism, wind curtailment cost, and solar curtailment cost are added in the model to ensure the environmental benefits in operation.

Distributionally robust comprehensive declaration strategy of virtual power plant participating in the power market considering flexible ramping product and uncertainties

The volatility and randomness of renewable energy output make the flexible ramping ability demand of power system more urgent, and flexible ramping product (FRP) can effectively improve the flexibility of the power system. Virtual power plant (VPP) can regulate distributed generation, which is conducive to the regulation potential of flexible resources. To solve the problems of FRP and electric energy markets synergy and the wind power output uncertainty faced by the VPP in the declaration process, this paper proposes a comprehensive declaration strategy for the VPP to participate in the power market considering FRP and uncertainties. Firstly, this paper designs a synergistic trading mode covering flexibility resource demand determination and the virtual bidding curve formation for electric energy and FRP markets, in which the virtual bidding curve can reasonably compensate for the unit opportunity cost. Secondly, a comprehensive declaration-dispatching strategy decision-making model for VPP is constructed, and a two-stage distributed robust optimization (DRO) technology is used to deal with the wind power output uncertainty in the model, and flexible resources such as energy storage are used to mitigate energy deviation in the VPP. Finally, simulations implemented on a typical VPP are delivered to show that: 1) Virtual bidding curve realizes the accurate compensation for the units providing FRP. 2) Compared with the single market, VPP can increase the expected profit by 20.44% and reduce wind curtailment cost by 59.68% in the joint declaration of multi-markets.3) VPP can effectively suppress output uncertainty through energy storage system. 4) DRO model has significant advantages in data-driven, less conservative results and stable running time.

Bio-Inspired Optimal Dispatching of Wind Power Consumption Considering Multi-Time Scale Demand Response and High-Energy Load Participation

Bio-inspired computer modelling brings solutions from the living phenomena or biological systems to engineering domains. To overcome the obstruction problem of large-scale wind power consumption in Northwest China, this paper constructs a bio-inspired computer model. It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load. First, the principle of wind power obstruction with the involvement of a high-energy load is examined in this work. In this step, highenergy load model with different regulation characteristics is established. Then, considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed, a multi-time scale model of coordination optimization is built. An improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs, as well as to find the most optimal energy configuration within the system. Lastly, we take an example of regional power grid in Gansu Province for simulation analysis. Results demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system’s operational cost.

Environmental Economical Dispatching of Electric–Gas Integrated Energy System Considering Hydrogen Compressed-Natural Gas

As a high-quality secondary energy, hydrogen energy has great potential in energy storage and utilization. The development of power-to-hydrogen (P2H) technology has alleviated the problem of wind curtailment and improved the coupling between the power grid and the natural gas grid. Under the premise of ensuring safety, using P2H technology to mix the produced hydrogen into the natural gas network for long-distance transmission and power generation can not only promote the development of hydrogen energy but also reduce carbon emissions. This paper presents a new model for incorporating hydrogen into natural gas pipelines. To minimize the sum of wind curtailment cost, operation cost and carbon emission cost, an electric–gas integrated energy system (EGIES) model of hydrogen-compressed natural gas (HCNG) containing P2H for power generation is constructed. Aiming at the problem of global warming caused by a lot of abandoned wind and carbon emissions, the economy and environmental protection of the system model are analyzed. The results show that the model of EGIES considering HCNG can not only absorb excess wind power but also reduce carbon emission costs and system costs, which can reduce the total cost of the environmental economic dispatch of the EGIES by about 34.1%. In the context of the EGIES, the proposal of this model is of great significance to the economical and environmentally friendly operation of the system.

Operation optimization for gas-electric integrated energy system with hydrogen storage module

Hydrogen is gradually becoming one of the important carriers of global energy transformation and development. To analyze the influence of the hydrogen storage module (HSM) on the operation of the gas-electricity integrated energy system, a comprehensive energy system model consisting of wind turbines, gas turbines, power-to-hydrogen (P2H) unit, and HSM is proposed in this paper. The model couples the natural gas network and power grid bidirectionally, and establishes a mixed integer nonlinear programming problem considering HSM. The linearization model of the natural gas pipeline flow equation and the generator set equation is constructed by piecewise linearization method to improve the efficiency of solving the model. And the energy flow distribution in the gas-electricity integrated energy system is finally solved. In Model 1, compared with not considering the installation of P2H units, when the hydrogen doping ratio is 10%, the operating cost can be reduced by 6.63%, and the wind curtailment cost can be reduced by 17.54%, and the carbon emission can be reduced by 298.7 tons. The optimization results of Model 2 reveal that compared with no HSM, the system operating cost is reduced by 5.96%, the hydrogen content level in the natural gas pipeline network is increased by 42.12%, and the carbon emission of the system is reduced by 117.6 tons, and the fluctuation of wind power is suppressed. This study demonstrates the feasibility of large-scale absorption of renewable energy through HSM.

Wind integration cost in China: A production simulation approach and case study

The integration cost of large-scale variable renewable energy (VRE) in China’s coal-dominated inflexible power systems is of significant concern when studying the generation cost of VRE. One key component of studies about integration cost is the question of how to estimate the impact of large-scale VRE integrated into power systems on the operation mode of conventional generators. It is intuitive to compare system-level costs with and without VRE under a benchmarked system. In this study, we employ a unit commitment (UC) model to simulate the output of various types of units based on minimizing the total power system cost. Then, we estimate the conventional generator operation cost change caused by the change of operation mode. Using the power system in Jilin province as a typical case, the integration cost will increase with increased VRE penetration, reaching 123.5 CNY/MWh at 40% penetration level. The idle cost of the coal-fired power units accounts for 69% of the integration cost at 40% VRE penetration, the highest proportion; wind curtailment cost ranks second for 26%, while the incremental operating cost of coal power accounts for 4.7%. With a long-term perspective, establishing a complete power market and adopting an appropriate pricing mechanism to incentivize system flexibility is the best solution for adopting VRE.

Optimal Energy Reserve Scheduling in Integrated Electricity and Gas Systems Considering Reliability Requirements

With the growing interdependence between the electricity system and the natural gas system, the operation uncertainties in either subsystem, such as wind fluctuations or component failures, could have a magnified impact on the reliability of the whole system due to energy interactions. A joint reserve scheduling model considering the cross-sectorial impacts of operation uncertainties is essential but still insufficient to guarantee the reliable operation of the integrated electricity and natural gas system (IEGS). Therefore, this paper proposes a day-ahead security-constrained unit commitment (SCUC) model for the IEGS to schedule the operation and reserve si-multaneously considering reliability requirements. Firstly, the multi-state models for generating units and gas wells are established. Based on the multi-state models, the expected unserved energy cost (EUEC) and the expected wind curtailment cost (EWC) criteria are proposed based on probabilistic methods considering wind fluctuation and random failures of components in IEGS. Furthermore, the EUEC and EWC criteria are incorporated into the day-ahead SCUC model, which is noncon-vex and mathematically reformulated into a solvable mixed-integer second-order cone programming (MISOCP) problem. The proposed model is validated using an IEEE 30-bus system and Belgium 20-node natural gas system. Numerical results demonstrate that the proposed model can effectively schedule the energy reserve to guarantee the reliable operation of the IEGS considering the multiple uncertainties in different subsystems and the cross-sectorial failure propagation.

Network topology optimisation based on dynamic thermal rating and battery storage systems for improved wind penetration and reliability

The nonflexible operations of transmission networks with high load demand and wind power increase the likelihood of power congestions and deteriorate grid reliability. This condition causes higher load curtailments and inhibits the penetrations of wind power, subsequently leading to higher dispatch cost because more expensive generators compensate for the loss of wind integrations. These unfavourable factors contribute to a higher total system operating cost, which should be reduced. This paper applies a network topology optimisation technique to optimise line and busbar switching for relieving network congestions and improving network flexibility. A dynamic thermal rating system is used to enhance overhead line ratings. A battery storage system is utilised to time shift wind power usage and avoid wind spillage. These methods are effective but they have been studied only in isolation. This paper presents an assessment framework that combines all the three methods in a single model to evaluate their synergistic effects on wind integration and network reliability. The proposed framework is generic and can be applied on any networks with changes only to the numerical results. The battery energy and power ratings required to maintain the prevailing security of supply standards in a large scale wind-integrated network are determined probabilistically. Case studies performed on a modified IEEE 24-bus reliability test system show that the proposed combination of methods reduces system dispatch, load curtailment and wind curtailment costs the most when compared to any combinations with fewer methods or using each method in isolation.

Capacity expansion planning of integrated energy system based on multi-energy coupling

This paper proposes a comprehensive energy expansion planning method based on multi-energy coupling, and establishes a mixed integer linear programming model with the goal of minimizing the sum of investment, operation, power shortage, and wind curtailment costs. The model can be used on the basis of ensuring computational efficiency. In the above, the uncertainty of wind power is accurately explored, and a two-stage planning and solving strategy based on the scenario method is established to ensure the efficiency and feasibility of the model solution. GAMS is used to optimize the calculation of the IEEE14-NGS14 example system, and the result of the optimal expansion plan proves the correctness and effectiveness of the method proposed in this paper.

Multi-area joint scheduling model considering peak load regulating of nuclear power

As the use of clean energy such as wind power and nuclear power has been increasing, the base load operation of nuclear power units usually means huge pressure for local power systems in regulating peak demand. In order to use more regulation resources, a multi-area joint optimization model involving peak regulating of nuclear power is proposed. Match performance of peak load regulating of wind power and nuclear power is also examined according to their output characteristics. Furthermore, aiming to make nuclear power constraint linearize, a method is introduced herein to subdivide the depth of peak load regulating of nuclear power. On the other hand, considering the power constraints of AC/DC transmission channel, wind curtailment cost and the operating cost of peak load regulating of nuclear power, a joint scheduling model with complementary energy sources in multi-area is established, which can effectively reduce the adverse impact of negative peak regulating of wind power and enhance consumption of clean energy. Finally, China’s inter-regional interconnected power system is introduced to validate the effectiveness of the proposed model.

Energy-storage configuration for EV fast charging stations considering characteristics of charging load and wind-power fluctuation

Fast charging stations play an important role in the use of electric vehicles (EV) and significantly affect the distribution network owing to the fluctuation of their power. For exploiting the rapid adjustment feature of the energy-storage system (ESS), a configuration method of the ESS for EV fast charging stations is proposed in this paper, which considers the fluctuation of the wind power as well as the characteristics of the charging load. The configuration of the ESS can not only mitigate the effects of fast charging stations on the connected distribution network but also improve its economic efficiency. First, the scenario method is adopted to model the wind power in the distribution network, and according to the characteristics of the EV and the driving probability, the charging demand of each station is calculated. Then, considering factors such as the investment cost, maintenance cost, discharging benefit, and wind curtailment cost, the ESS configuration model of the distribution network is set up, which takes the optimal total costs of the ESS for EV fast charging stations within its lifecycle as an objective. Finally, General Algebraic Modelling System (GAMS) is used to linearize and solve the proposed model. A simulation on an improved IEEE-69 bus system verifies the feasibility and economic efficiency of the proposed model.

A Coordinated Peak Shaving Strategy Using Neural Network for Discretely Adjustable Energy-Intensive Load and Battery Energy Storage

The large-scale wind power introduces the challenge of the power demand and generation balancing. Energy-intensive load (EIL) is a promising option for peak shaving since it can change its production time and power demand without affecting its overall production. However, EIL which is discretely adjustable is unable to track the net load in real time. A two-stage complementary peak shaving strategy of EILs with the aid of battery energy storage systems (BESSs) is proposed to address this issue. This paper establishes an optimization model with the minimum system operation costs and wind curtailment costs as the objective function, in which EIL operation constraints and BESS power and energy balance constraints are added to the unit commitment model. And the neural network algorithm is used to solve this optimization problem. Finally, a system with a high proportion of wind power is adopted to analyze the functions of EIL and BESS in the method. It is verified that the proposed strategy can effectively reduce the amount of wind curtailment and the operation costs of the system.

A scenario-based planning framework for energy storage systems with the main goal of mitigating wind curtailment issue

This paper provides a new multi-objective (MO) framework for expansion studies of energy storage systems (ESSs) in high wind penetrated power system. The proposed approach well considers the issues originated from the wind power curtailment via introducing expected of wind curtailment cost as an objective function of the studies. All other imposed costs of installing storage units are modeled as expected of total social cost. Also, the effect of uncertainties is modeled through an internal scenario analysis. In this regard, two criteria including maximum regrets of wind curtailment cost and total social cost are considered as the other objectives of the proposed MO optimization procedure. These all the crucial and maybe contradictory aspects of the ESSs expansion problem are treated via a Posteriori MO optimization algorithm, i.e. the Non-dominated Sorting Genetic Algorithm II (NSGA II). The proposed methodology is implemented on the modified IEEE 24-bus test system and its anticipated applicability is well verified.

Developing a multi-objective framework for expansion planning studies of distributed energy storage systems (DESSs)

This paper presents a framework for expansion planning studies of distributed energy storage systems (DESSs) in high wind penetrated power systems. The main objective is to find optimal location and capacity of DESSs in the viewpoint of independent system operator (ISO) while ensuring the maximum usage of wind farms output generation. Three different criteria are introduced for expansion planning studies. Minimizing wind curtailment cost together with transmission congestion cost are considered to properly deal with the issues associated with the curtailment of wind energy and constraints of transmission network. Furthermore, the minimum normalized profit for all DESSs' owners needs to be maximized to model the requirements of DESSs' owners in the studies. These all the crucial aspects of the DESSs expansion problem are treated via a well-organized posteriori multi-objective (MO) optimization algorithm, i.e. the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The proposed method is applied to the modified IEEE 24-bus test system, and the results are presented to verify the applicability and efficiency of the proposed DESSs planning in a renewable-based power system.