Frequency security is one of the challenges for the power grid penetrated by renewable energy systems (RESs). Virtual inertia provision from the RESs is considered to be efficient support for frequency security enhancement. How to estimate the frequency nadir rapidly and accurately is crucial for the online inertia allocation for the RESs. This paper develops a generic average system frequency (G-ASF) model for the power grid to estimate the frequency nadir at first. The low-order generic model of the speed governing system of the SG is proposed to enhance the accuracy and flexibility of the G-ASF model. Then, based on the G-ASF model, an online inertia allocation strategy for the grid-connected RESs under frequency nadir constraint is proposed. With the pre-identified generic models of the speed governing systems, the G-ASF model of the entire power grid is periodically updated according to the grid operation state. Next to the model updating, the critical inertia of the power grid is periodically calculated under the pre-defined contingencies for frequency security verification. Then, the virtual inertia provision tasks are determined and allocated to the RESs periodically, and the grid frequency security is effectively ensured. The simulation results validate the proposed model and strategy.

With the increasing penetration of renewable energy, designing the efficient midterm (i.e., annual) scheduling for multi-source power systems is valuable in providing substantial cost reductions and flexibilities while ensuring system security against uncertainties of renewables. This paper presents a risk-averse two-stage stochastic midterm scheduling model for thermal-hydro-wind systems against short-term uncertainties of renewable generation. A network-constrained clustered unit commitment (NC-CUC) model is proposed to effectively incorporate short-term operation constraints into the midterm scheduling model, for improving the solution accuracy while reducing computational burden. Specifically, as the traditional CUC model cannot ensure consistent unit commitment status of generators across all scenarios, a power flow-based unit clustering method is proposed to improve the accuracy of short-term operation results. In addition, the conditional value-at-risk (CVaR) is used to quantify the system operational risk caused by uncertainties of wind generation and natural water inflows. Case studies show that the risk-averse two-stage stochastic midterm scheduling model can ensure operation dynamics and solution efficiency, and the proposed clustering method can achieve a tradeoff between solution accuracy and computational burden for the stochastic network-constrained CUC model.

Bipolarity in dc microgrids is desirable as it enhances the system's reliability and efficiency. However, the stability assessment for a bipolar dc microgrid is challenging due to the integration of a three-wire dc distribution line and numerous connected power converters, which is different from the stability analysis of a conventional unipolar dc microgrid. In this article, the basic form is arranged by a generalized voltage source or current source, and the simplified form is derived by looking into the different bus ports of a bipolar system in detail. Then, an impedance sum criteria-based stability conditions of different bus ports in the bipolar dc microgrid are proposed. To explore the mutual influence of the stability among different bus ports and investigate the stability issues caused by unbalanced loads connected to the symmetrical bus port, three cases are studied: 1) ± V _dc bus ports are connected with balanced and unbalanced loads; 2) − V _dc bus port introduces the photovoltaic unit and the energy storage unit; and 3) the 2 V _dc bus port introduces the photovoltaic unit and the energy storage unit. Finally, a ±24 V bipolar dc microgrid platform is set up to conduct experiments, verifying the accuracy and effectiveness of the stability evaluation method.

Renewable energy plays a crucial role in achieving sustainable development and has the potential to meet humanity’s long-term energy requirements. Integrated electric–thermal energy systems are an important way to consume a high proportion of renewable energy. The intermittency and volatility of integrated electric–thermal energy systems make solving energy management optimization problems difficult. Thus, this paper proposes an energy management optimization method for an integrated electric–thermal energy system based on the improved proximal policy optimization algorithm, which effectively mitigates the problems of the traditional heuristic algorithms or mathematical planning methods with low accuracy and low solving efficiency. Meanwhile, the proposed algorithm enhances both the convergence speed and overall performance compared to the proximal policy optimization algorithm. This paper first establishes a mathematical model for the energy management of an integrated electric–thermal energy system. Then, the model is formulated as a Markov decision process, and a reward mechanism is designed to guide the agent to learn the uncertainty characteristics of renewable energy output and load consumption in the system through historical data. Finally, in the case study section, the proposed algorithm reduces the average running cost by 2.32% compared to the other algorithms discussed in this paper, thereby demonstrating its effectiveness and cost-efficiency.

Hybrid pumped hydro storage plants, by integrating pump stations between cascade hydropower stations, have overcome the challenges associated with site selection and construction of pure pumped hydro storage systems, thereby becoming the optimal large-scale energy storage solution for enhancing the absorption of renewable energy. However, the multi-energy conversion between pump stations, hydropower, wind power, and photovoltaic plants poses challenges to both their planning schemes and operational performance. This study proposes a multistage stochastic coordinated planning model for cascade hydropower-wind-solar-thermal-pumped hydro storage (CHWS-PHS) systems. First, a Hybrid Pumped Hydro Storage Adaptive Initial Reservoir Capacity (HPHS-AIRC) strategy is developed to enhance the system’s regulation capability by optimizing initial reservoir levels that are synchronized with renewable generation patterns. Then, Non-anticipativity Constraints (NACs) are incorporated into this model to ensure the dynamic adaptation of investment decisions under multi-timescale uncertainties, including inter-annual natural water inflow (NWI) variations and hourly fluctuations in wind and solar power. Simulation results on the IEEE 118-bus system show that the proposed MSSP model reduces total costs by 6% compared with the traditional two-stage approach (TSSP). Moreover, the HPHS-AIRC strategy improves pumped hydro utilization by 33.8%, particularly benefiting scenarios with drought conditions or operational constraints.

The intensifying impacts of climate change induced by carbon emissions necessitate the implementation of urgent mitigation strategies. Given that the power sector is a major contributor to global carbon emissions, strategic decarbonization planning in this sector is of paramount importance. This study proposes a synergistic planning framework for low-carbon power systems that integrates coal-fired power plants (CFPPs) and a carbon and green certificate market coupling mechanism, thereby facilitating a “security–economic–low-carbon” tri-objective transition in power systems. The proposed framework facilitates dynamic decision-making regarding the retrofitting of CFPPs, investments in renewable energy resources, and energy storage systems. By evaluating three distinct CFPP retrofitting pathways, the framework enhances economic efficiency and reduces carbon emissions, achieving reductions of 28.67% in total system costs and 2.96% in CO₂ emissions. Implementing the carbon–green certificate market coupling mechanism further unlocks the market value of green certificates, thereby providing economic incentives for clean energy projects and increasing flexibility in the allocation of carbon emission quotas for enterprises. Relative to cases that consider only carbon trading or only green certificate markets, the coupled mechanism reduces the total cost by 10.96% and 15.56%, and decreases carbon emissions by 27.10% and 47.36%, respectively. The collaborative planning framework introduced in this study enhances economic performance, increases renewable energy penetration, and reduces carbon emissions, thus facilitating the low-carbon transition of power systems.