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The energy system carbon reduction is an inevitable trend to deal with the global warming problem. The power industry, being a vital part of energy system, faces severe challenges. To decarbonize the power sectors, the implementation of low-carbon technologies and integration of high penetrated renewable power generation would be an effective solution. Therefore, given the existence of multi-type low-carbon technologies including the flexibility reformation of coal-fired units, construction of gas-fired units and installation of energy storage systems, a low-carbon economic planning model of integrated electricity-gas systems with high penetration of wind generation is proposed. The carbon tax and carbon capture technology are integrated to accelerate carbon reductions. In particular, the strategic planning cases contributed by different scenarios are formulated. The simulation results on an electricity-gas test system demonstrate the feasibility and effectiveness of the proposed model in carbon reduction as well as wind curtailment decrease. Compared with the initial state, the implementations of the proposed three planning strategies can reduce carbon emissions by 9.8%, 32.5% and 9.3%, respectively. Meanwhile, the wind power curtailment ratio is decreased by 63.2%, 38.9%, and 63.7%, respectively. Moreover, a sensitivity analysis of carbon tax price and wind power penetration level are performed to investigate the low-carbon transition of the integrated electricity-gas systems. • A low-carbon economic planning model is established. • Sensitivity analysis reveals the applicability of proposed strategies. • The flexibility could be enhanced by the proposed planning strategies. [ABSTRACT FROM AUTHOR]

• A novel hybrid short-term wind power forecasting method is proposed. • The automatic relevance determination algorithm is used to correct NWP data. • The spatial correlation of wind speed series between wind farms is extracted. • The corrected NWP and spatial correlation are integrated into a Gaussian process. Wind power generation rapidly grows worldwide with declining costs and the pursuit of decarbonised energy systems. However, the utilization of wind energy remains challenging due to its strong stochastic nature. Accurate wind power forecasting is one of the effective ways to address this problem. Meteorological data are generally regarded as critical inputs for wind power forecasting. However, the direct use of numerical weather prediction in forecasting may not provide a high degree of accuracy due to unavoidable uncertainties, particularly for areas with complex topography. This study proposes a hybrid short-term wind power forecasting method, which integrates the corrected numerical weather prediction and spatial correlation into a Gaussian process. First, the Gaussian process model is built using the optimal combination of different kernel functions. Then, a correction model for the wind speed is designed by using an automatic relevance determination algorithm to correct the errors in the primary numerical weather prediction. Moreover, the spatial correlation of wind speed series between neighbouring wind farms is extracted to complement the input data. Finally, the modified numerical weather prediction and spatial correlation are incorporated into the hybrid model to enable reliable forecasting. The actual data in East China are used to demonstrate its performance. In comparison with the basic Gaussian process, in different seasons, the forecasting accuracy is improved by 7.02%–29.7% by using additional corrected numerical weather prediction, by 0.65–10.23% after integrating with the spatial correlation, and by 10.88–37.49% through using the proposed hybrid method. [ABSTRACT FROM AUTHOR]

The stochastic nature of wind speed hinders the forecasting of wind power generation. To improve the accuracy of wind power forecasting and effectively utilize the capability of principal component analysis (PCA) to process high-dimensional data, and take the advantages of deep belief network (DBN) to process complex data and spatial correlation (SC) in considering geographical position and terrain, a hybrid forecasting method using numerical weather prediction (NWP) is presented in this paper. First, an improved DBN is proposed by introducing Gaussian-Bernoulli restricted Boltzmann machine, and an adaptive learning step technique is applied to improve the convergence speed. Furthermore, the principal components are extracted from high-dimension original data by PCA, which are further input to the improved DBN. Then, a wind speed correction model is established to address the inaccuracy of NWP. Moreover, the output of the target site is forecasted using the data of its neighboring observation sites. Finally, the advantages of the above methods are combined, and the sliding window strategy is utilized to adaptively update the training data. The simulation results verify the effectiveness of the proposed improved DBN and the hybrid method compared to the traditional DBN, the corresponding average increase of forecasting accuracy is 15.8975% and 29.3725%, respectively. • An improved deep belief network (DBN) adding Gaussian function to RBM is designed. • The spatial correlation (SC) are used to consider geographical position and terrain. • An adaptive learning step technique is applied to improve convergence speed. • Sliding window strategy is utilized to adaptively update training data of forecasting method. [ABSTRACT FROM AUTHOR]

An energy-reserve co-optimization model for electricity and natural gas systems with multi-type reserve resources is proposed. Considering the reserve capacity provided by generating units is difficult to meet the large-scale regulation requirements caused by wind power, load demand forecast errors and unexpected contingencies, a reserve scheme with multi-type reserve resources including generating units, energy storage devices, and interruptible loads is designed. Besides, the transmission capability of reserve capacity in the electricity and gas networks when facing forecast errors and contingencies is considered in the proposed model. To protect information privacy of different energy systems, an improved alternating direction method of multipliers (ADMM) is adopted to solve the energy-reserve co-optimization model. The feasibility and effectiveness of the proposed model and method are verified on a modified IEEE 24-bus power system and 6-node natural gas systems. • A reserve scheme with multi-type reserve resources is proposed. • The deliverability of reserve capacity is considered. • An improved ADMM based distributed solution strategy is designed. [ABSTRACT FROM AUTHOR]

Highlights • Dynamic gas system model to model H 2 injection into gas systems for better accuracy. • Wobbe Index and Combustion Potential to measure H 2 injection impact on gas quality. • Robust optimisation to manage H 2 mix with other gases to ensure gas quality. • Liquid petroleum gas and Nitrogen for changing Wobbe Index and Combustion Potential. Abstract A large volume of wind power is curtailed worldwide due to the intermittency and limited transportation capacity of electrical power systems. New technologies, Power-to-Gas via electrolysis, can convert excessive wind power into hydrogen to be transported by natural gas systems. However, the injection of H 2 into natural gas pipelines can cause gas quality issues due to changing gas compositions. This paper investigates the impact of injecting H 2 converted from wind power on natural gas quality. Two key indexes used to measure gas quality, Wobbe Index and Combustion Potential, are introduced to examine the impact. Then, in order to bring the two indexes into acceptable statutory ranges, H 2 is mixed with Liquid Petroleum Gas and Nitrogen. A robust optimization model, considering wind power uncertainties, is thereafter developed to manage the gas mixture, maximize H 2 injection. This paper uses the dynamic gas system model to represent real-time pipeline flows, which can better reflect gas flow features over time. The proposed method is demonstrated on a small integrated gas-electricity system. Results illustrate that excessive H 2 injection will reduce Wobbe Index but increase Combustion Potential. The robust optimization approach can effectively manage the mixture while ensuring gas quality with an uncertain wind power supply. The proposed method is beneficial to reducing renewable energy curtailment and maximizing H 2 injection, benefiting electricity system operators with low operation costs and wind power more penetration. [ABSTRACT FROM AUTHOR]

The water system management problem has been widely investigated. However, the interdependencies between water and energy systems are significant and the effective co-optimization is required considering strong interconnections. This paper proposes a two-stage distributionally robust operation model for integrated water-energy nexus systems including power, gas and water systems networked with energy hub systems at a distribution level considering wind uncertainty. The presence of wind power uncertainty inevitably leads to risks in the optimization model. Accordingly, a coherent risk measure, i.e., conditional value-at-risk, is combined with the optimization objective to determine risk-averse operation schemes. This two-stage mean-risk distributionally robust optimization is solved by Bender's decomposition method. Both the day-ahead and real-time operation cost are minimized with an optimal set of scheduling the multi-energy infrastructures. Case studies focus on investigating the strong interdependencies among the four interconnected energy systems. Numerical results validate the economic effectiveness of IES through optimally coordinating the multi-energy infrastructures. The proposed model can provide system operators a powerful two-stage operation scheme to minimise operation cost under water-energy nexus considering risk caused by renewable uncertainties, thus benefiting customers with lower utility bills. [ABSTRACT FROM AUTHOR]