Low-carbon unit commitment with intensive wind power generation and carbon capture power plant

Wind power and carbon capture power plants (CCPPs) are two important viable options to reduce CO2 emission in low-carbon power systems. Especially, the combination of the two options, i.e., the CCPP integrating wind power, can effectively cut the CO2 emission of the power plant. This paper presents a low-carbon dispatch model for a CCPP integrating wind power, considering the ramping cost, fuel cost, and carbon emission cost. To simplify the model, the flexibility of the CCPP is defined as the capture percentage. Then, the impact of the ramping cost on the operation of the CCPP integrating wind power is studied under different carbon taxes. Numerical examples based on an IEEE 30-bus tested system show that the carbon taxes for the dispatch model including the ramping cost can be divided into two zones: the flexible zone and the inflexible zone. The simulation results demonstrate that within the flexible zone, considering the ramping cost for the dispatch model can effectively improve the economy o...

Low-carbon electric power systems are often characterized by high shares of renewables, such as wind power. The variable nature and limited predictability of some renewables will require novel system operation methods to properly size and cost-efficiently allocate the required reserves. The current state-of-the-art stochastic unit commitment models internalize this sizing and allocation process by considering a set of scenarios representing the stochastic input during the unit commitment optimization. This results in a cost-efficient scheduling of reserves, while maintaining the reliability of the system. However, calculation times are typically high. Therefore, in this paper, we merge a state-of-the-art probabilistic reserve sizing technique and stochastic unit commitment model with a limited number of scenarios in order to reduce the computational cost. Results obtained for a power system with a 30% wind energy penetration show that this hybrid approach allows to approximate the expected operational costs and reliability of the resulting unit commitment of the stochastic model at roughly one thirtieth of the computational cost. The presented hybrid unit commitment model can be used by researchers to assess the impact of uncertainty on power systems or by independent system operators to optimize their unit commitment decisions taking into account the uncertainty in their system.

Assuming that all electric vehicles (EVs) integrated to the grid are managed by aggregators, it is possible to make full use of the controllability of the charging EVs. Based on this, the charging load of each EV aggregator is defined as one of the optimal scheduling variables in the unit commitment (UC) model. This can be accomplished by assessing the upper and lower limit of charging load of each EV aggregator in different scenarios, which is simulated by Monte Carlo method. According to this, a UC model is proposed for optimal coordination of EV aggregators and wind generation, considering the EV owner's requirement, the range of charging load and the unit constraints. Using the10-units system as the test system, the results of UC models with different EVs charging strategies and the wind power integration indicators such as utilization rate are investigated. Simulation results show that both the abandoned wind power and the unit operating costs can be decreased more effectively in the proposed UC model.

This study proposes a novel robust unit commitment (UC) framework with data-driven disjunctive uncertainty sets for volatile wind power generation, which integrates a two-stage adaptive robust UC model with machine learning techniques to flexibly capture the uncertainty space of the wind power forecast errors with disjunctive structures. K-means clustering and density-based spatial clustering of applications with noise (DBSCAN) are applied for clustering, and data-driven disjunctive uncertainty sets are constructed as the union of multiple basic uncertainty sets following the clustering results. The problem is formulated into a two-stage adaptive robust UC model with data-driven disjunctive uncertainty sets and with a multi-level optimization structure. A tailored decomposition-based optimization algorithm is developed to facilitate the solution process. A numerical experiment and a UC case study on the IEEE 39-bus system are presented to demonstrate the effectiveness of the proposed approach. In both applications, using the proposed disjunctive uncertainty sets can effectively reduce the price of robustness compared to the conventional "one-set-fits-all" approach. Furthermore, the disjunctive uncertainty sets using DBSCAN tend to provide less conservative solutions than those using K-means, implying that DBSCAN can handle the outliers and noise of the uncertainty data more efficiently.

With the increasing integration of renewable energy sources into the power system, challenges such as wind curtailment and operational flexibility are becoming more prominent. Therefore, this paper proposes a low‐carbon optimised strategy for integrated energy system (IES) that considers the efficient use of hydrogen energy and the flexible operation of carbon capture power plant (CCPP)–methane reactor (MR)–hydrogen‐doped combined heat and power (HCHP) combination. First, a model for the efficient utilisation of hydrogen energy containing wind power to hydrogen, hydrogen to thermoelectricity, gas‐mixed hydrogen and hydrogen to methane was established. Secondly, the co‐ordination mechanism among CCPP, HCHP and MR is explored, and the flexibility improvement of CCPP and HCHP is introduced by the liquid storage tank (LST) and Kalina cycle, respectively, and the joint CCPP‐MR‐HCHP flexible operation model is constructed. Finally, the integrated demand response (IDR) of electricity and heat is introduced, and a novel low‐carbon optimisation model of the IES is established by integrating low‐carbon and economic considerations. The simulation part of the example set up different scenarios for comparison, and the results showed that the introduction of an efficient hydrogen energy utilisation model can effectively improve the level of wind power consumption and reduce the total system cost and carbon emissions by about 11.35% and 24.73%, respectively. In addition, the proposed CCPP‐MR‐HCHP model can significantly improve the operational flexibility of the system, reducing the total system cost and carbon emissions by approximately 8.51% and 11.06%, respectively, compared to traditional operating modes.

Due to the stochastic and fluctuated nature of wind energy, it is difficult to predict wind power accurately. Thus, the day-ahead optimal dispatching for power system integrated large-scale wind power has a significant challenge. Therefore, a unit commitment (UC) method considering stochastic wind power is proposed to mitigate the impact of inaccurate predicted error in this paper. Firstly, the improved Lagrange relaxation algorithm is used to obtain the dual problem of the generator unit model. Secondly, based on the prediction of wind power interval, the scenario method is used to deal with the fluctuation of wind power through the scene generation and improved fast backward reduction technology. Finally, the 10-unit system model is built, and the feasibility of the proposed UC model is proved by comparing the stochastic UC model and deterministic model.

In recent years, high penetration of variable generating sources, such as wind power, has challenged independent system operators (ISO) in keeping a cheap and reliable power system operation. Any deviation between expected and real wind production must be absorbed by the power system resources (reserves), which must be available and ready to be deployed in real time. To guarantee this resource availability, the system resources must be committed in advance, usually the day-ahead, by solving the so-called unit commitment (UC) problem. If the quantity of committed resources is extremely low, there will be devastating and costly consequences in the system, such as significant load shedding. On the other hand, if this quantity is extremely high, the system operation will be excessively expensive, mainly because facilities will not be fully exploited. This thesis proposes computationally efficient models for optimal day-ahead planning in (thermal) power systems to adequately face the stochastic nature of wind production in the real-time system operation. The models can support ISOs to face the new challenges in short-term planning as uncertainty increases dramatically due to the integration of variable generating resources. This thesis then tackles the UC problem in the following aspects: Power system representation: This thesis identifies drawbacks of the traditional energy-block scheduling approach, which make it unable to adequately prepare the power system to face deterministic and perfectly known events. To overcome those drawbacks, we propose the ramp-based scheduling approach that more accurately describes the system operation, thus better exploiting the system flexibility. UC computational performance: Developing more accurate models would be pointless if these models considerably increase the computational burden of the UC problem, which is already a complex integer and non-convex problem. We then devise simultaneously tight and compact formulations under the mixed-integer programming (MIP) approach. This simultaneous characteristic reinforces the convergence speed by reducing the search space (tightness) and simultaneously increasing the searching speed (compactness) with which solvers explore that reduced space. Uncertainty management in UC: By putting together the improvements in the previous two aspects, this thesis contributes to a better management of wind uncertainty in UC, even though these two aspects are in conflict and improving one often means harming the other. If compared with a traditional energy-block UC model under the stochastic (deterministic) paradigm, a stochastic (deterministic) ramp-based UC model: 1) leads to more economic operation, due to a better and more detailed system representation, while 2) being solved significantly faster, because the core of the model is built upon simultaneously tight and compact MIP formulations. To further improve the uncertainty management in the proposed ramp-based UC, we extend the formulation to a network-constrained UC with robust reserve modelling. Based on robust optimization insights, the UC solution guarantees feasibility for any realization of the uncertain wind production, within the considered uncertainty ranges. This final model remains as a pure linear MIP problem whose size does not depend on the uncertainty representation, thus avoiding the inherent computational complications of the stochastic and robust UCs commonly found in the literature.

The integration of wind resource into the electric grid brings significant challenges due to the variable nature and anti-peak-regulation characteristic of wind power. Based on least square method, an improved normal distribution model is proposed to fit the actual wind power forcast error. Furthermore, considering wind power forecast error and the great potential of battery energy storage system (BESS) technology to mitigate the impact of volatile wind power, a unit commitment (UC) model with large capacity BESS has been estanbished in this study. Case studies with modified IEEE 39-bus system are employed to validate the proposed method. The role of BESS on economics, peak load shifting and accommodating wind power is discussed. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.4937

In the current power system dispatching, the high penetration of renewable energy sources has caused a lot of uncertainty. To cope with the issue, this paper proposes a data-driven stochastic optimization model as a solution to the unit commitment (UC) problem. This model integrates wind power and energy storage and considers the correlation among the forecast errors of multiple wind farms. Based on the Dirichlet process Gaussian mixture model (DPGMM), this paper first uses historical data to construct a probability density function (PDF) for the uncertainty and spatial correlation of wind power forecast errors. Then the simultaneous backward reduction (SBR) algorithm is used to select typical scenarios from the large number of simulated scenarios generated by sampling the PDF obtained previously. As such, the uncertainty of wind power is represented by the typical scenarios. After that, a stochastic UC model is proposed, the objective function of which is established considering the cost of wind curtailment and load shedding. Finally, taking a ten-unit system with two wind farms and energy storage devices as an example, the effectiveness of the above models and methods is validated by the results of comparative exemplifications.

Hydrogen energy plays an increasingly vital role in global energy transformation. However, existing electric–hydrogen coupled integrated energy systems (IESs) face two main challenges: achieving stable operation when integrated with large-scale networks and integrating optimal dispatching code with physical systems. This paper conducted comprehensive modeling, optimization and joint simulation verification of the above IES. Firstly, a low-carbon economic dispatching model of an electric–hydrogen coupled IES considering carbon capture power plants is established at the optimization layer. Secondly, by organizing and selecting representative data in the optimal dispatch model, an electric–hydrogen coupled IES planning model considering the integration of wind, photovoltaic (PV), diesel and storage is constructed at the physical layer. The proposed electric–hydrogen coupling model mainly consists of the following components: an alkaline electrolyzer, a high-pressure hydrogen storage tank with a compressor and a proton exchange membrane fuel cell. The IES model proposed in this paper achieved the integration of optimal dispatching mode with physical systems. The system can maintain stable control and operation despite unpredictable changes in renewable energy sources, showing strong resilience and reliability. This electric–hydrogen coupling model also can integrate with large-scale IES for stable joint operation, enhancing renewable energy utilization and absorption of PV and wind power. Co-simulation verification showed that the optimized model has achieved a 29.42% reduction in total system cost and an 83.66% decrease in carbon emissions. Meanwhile, the simulation model proved that the system’s total harmonic distortion rate is controlled below 3% in both grid-connected and islanded modes, indicating good power quality.

This paper proposes a privacy-preserving framework that couples probabilistic wind power forecasting with decentralized anomaly detection in complex power networks. We first design an adaptive federated learning (FL) scheme to produce probabilistic forecasts for multiple geographically distributed wind farms while keeping their raw data local. In this scheme, an artificial neural network with quantile regression is trained collaboratively across sites to provide calibrated prediction intervals for wind power outputs. These forecasts are then embedded into an alternating direction method of multipliers (ADMM)-based load-side dispatch and anomaly detection model for decentralized power systems with plug-and-play industrial users. Each monitoring node uses local measurements and neighbor communication to solve a distributed economic dispatch problem, detect abnormal load behaviors, and maintain network consistency without a central coordinator. Experiments on the GEFCom 2014 wind power dataset show that the proposed FL-based probabilistic forecasting method outperforms persistence, local training, and standard FL in RMSE and MAE across multiple horizons. Simulations on IEEE 14-bus and 30-bus systems further verify fast convergence, accurate anomaly localization, and robust operation, indicating the effectiveness of the integrated forecasting–dispatch framework for smart industrial grids with high wind penetration.

The increasing high penetration of wind power will further increase the uncertainty in power systems, and three key issues should be addressed: 1) determining the maximum accommodation level of wind power without sacrificing system reliability; 2) quantifying the potential risk when the wind generation realization is beyond the prescribed uncertainty sets; and 3) how to reduce the risk loss. Motivated by these, a risk-based two-stage robust unit commitment (RUC) model is proposed to analyze the admissibility of wind power. In this model, the electricity storage system (ESS) is utilized for managing the wind power uncertainty to reduce the risk loss. Different from a determined uncertainty set in the previous RUC, the proposed method can flexibly adjust the uncertainty set by optimizing the operational risks including the wind spillage risk and load shedding risk. Conditional Value-at Risk (CVaR) is adopted to describe the risk loss when the real wind power output is beyond the predefined uncertainty set. Meanwhile, the low-probability, high-influence events are taken into the account based on CVaR to determine the optimal acceptable wind generation considering the tradeoff between reliability and economics. The proposed model is solved effectively by the modified column and constraint generation method. Case studies on two benchmark systems illustrate that the ESS can reduce the risk loss of power system and improve the ability to accommodate the uncertainty of wind generation.

A robust optimal power system dispatching method based on modified affine strategy is proposed to cope with the generation reserve coordination problem considering wind power uncertainty. Firstly, focusing on power system with wind turbines, a two-stage power optimal dispatch model of generation and reserve is established. Secondly, considering the statistical information of wind power errors, the self-organizing mapping(SOM) neural network is applied to make the wind power data get clustered, and then multiple uncertainty sets of wind power output error are constructed. Then, on the basis of the robust optimization model with multiple uncertainty sets, a modified affine strategy method is proposed, and it is transformed into linear programming(LP) model by dual principle. A 6-bus system and IEEE 118-bus system are tested for case studies, then the modified affine strategy is compared with the conventional affine strategy. The results show that the modified affine strategy has higher solution efficiency and better economic benefits, improves the problem of conservative modeling of uncertainty sets, and verifies the effectiveness and advancement of the proposed strategy.

For the purpose of mitigating carbon dioxide emissions, three renewable energy transportation systems are proposed in which methanol is synthesized by use of wind power generation energy at an oversea and is transported to Japan to be used for a power generation. The proposed systems are the following three systems: (1) wind energy and captured CO2 utilization system, (2) wind energy and coal utilization system, and (3) wind energy and biomass utilization system. The characteristic and cost of the proposed systems’ components such as a wind power generation and a methanol synthesis plants are investigated, and so are the energy and carbon flows of the systems, assuming that the wind power generation plant is constructed at the eastern coast of Russia. Major indicators such as energy efficiency, methanol cost, CO2 reduction cost, etc., of the proposed systems are evaluated together with those of a similar CO2 recycling system utilizing hydraulic power. On the basis of the evaluation results, the wind energy and biomass utilization system is shown to be the most excellent among the evaluated systems from the viewpoints of the CO2 reduction cost. When LNG cost is increased, its estimated CO2 reduction cost islower than that of a CO2 recovery system adopted to a conventional LNG-fired power plant. Consequently, the proposed system is expected to be a feasible option for CO2 reduction in the near future when the wind power generation cost is much decreased.

K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting