In this letter, we propose a tractable formulation and an efficient solution method for the Wasserstein-metric-based distributionally robust unit commitment (DRUC-dW) problem. First, a distance-based data aggregation method is introduced to hedge against the dimensionality issue arising from a huge volume of data. Then, we propose a novel cutting plane algorithm to solve the DRUC-dW problem much more efficiently than state-of-the-art. The novel solution method is termed extremal distribution generation, which is an extension of the column-and-constraint generation method to the distributionally robust cases. The feasibility and cost efficiency of the model, and the efficiency of the solution method are numerically validated.

Electricity and heat energy carriers are mostly produced by the fossil fuel sources that are conventionally operated independently, but these carriers have low efficiency due to heat losses. Moreover, a high share of variable renewable energy sources disrupts the power system reliability and flexibility. Therefore, the coupling of multiple energy carriers is underlined to address the above-mentioned issues that are supported by the latest technologies, such as combined heat and power, heat pumps, demand response, and energy storages. These coupling nodes in energy hubs stimulate the conversion of the electric power system into the integrated energy system that proves to be cost-effective, flexible, and carbon-free. The proposed work uses EnergyPLAN to model electricity, district, and individual heating integrated energy system of China for the year 2020. Furthermore, the addition of heat pumps, thermal storage, and demand response is analyzed in different scenarios to minimize the annual costs, fuel consumption, and CO ₂ emissions. Technical simulation strategy is conducted for optimal operation of production components that result in the reduction of the above-mentioned prominent factors while calculating the critical and exportable excess electricity production. The simulation results demonstrate that demand response and thermal storage significantly enhance the share of variable renewable energy sources. In addition, it substantially reduces the annual costs and fuel consumption, while heat pump increases the system efficiency.

The Yunnan power grid has a large-scale hydropower station whereby the power generated at the station is transmitted via HVDC lines under an asynchronous operation mode. In the system performance test of the Yunnan asynchronous operation, a 0.05 Hz ultra-low-frequency oscillation occurred in the grid. This ultra-low-frequency oscillation is a frequency stability problem caused by instability of hydraulic turbine governing system, which is quite common in the islanded system and very rare in the normal synchronous system. The relationship between the speed governing system stability of one unit and that of the system with parallel operation units was proposed to explain that the high proportion of units with an unstable governing system will lead to system frequency instability and the reduction in loads in the traditional sense was proved to further worsen the frequency stability of the Yunnan asynchronous system. By using related measures, the ultra-low-frequency oscillation of the Yunnan asynchronous system have been curbed effectively. System performance testing verified the effectiveness of the analysis and the measures.

To facilitate wind energy use and avoid low returns, or even losses in extreme cases, this paper proposes an integrated risk measurement and control approach to jointly manage multiple statistical properties of the expected profit distribution for a wind storage system. First, a risk-averse stochastic decision-making framework and multi-type risk measurements, including the conditional value at Risk (CVaR), value at risk (VaR) and shortfall probability (SP), are described in detail. To satisfy the various needs of multi-type risk-averse decision makers, integrated risk measurement and control approaches are then proposed by jointly considering the expected, boundary and probability values of the extreme results. These are managed using CVaR, VaR and SP, respectively. Finally, the effectiveness of the proposed risk control strategy is verified by conducting case studies with realistic market data, and the results of different risk control strategies are analyzed in depth. The impacts of the risk parameters of the decision maker, the energy capacity of the battery storage and the price difference between the day-ahead and real-time markets on the expected profits and risks are investigated in detail.

Previous robust transmission expansion planning (RTEP) studies have rarely considered the important question of how large the uncertainty set should be, and hence, how the uncertainty budget can be determined objectively. This study addresses this issue by proposing a novel adaptive optimization method that optimizes the value of the uncertainty budget to minimize the size of the uncertainty set while considering the underlying risk for wind power generation (WPG) fluctuations residing outside of the proposed uncertainty set. As such, the proposed method ensures a good tradeoff between the robustness and costs of RTEP solutions. In addition, the proposed method optimizes investment strategies under forecasted WPG scenarios, while providing a security guarantee under extreme WPG scenarios. The variable-limit integral terms introduced by the proposed method are addressed in the solution process by applying the piecewise linearization approximation method combined with the quadratic Newton-Gregory interpolating polynomial technique, which allows the solution process to be cast as a mixed integer linear programming problem. The good performance and effectiveness of the proposed adaptive uncertainty set optimization method are verified by numerical results.