The increasing penetration of line-commutated converter based high-voltage direct current (LCC-HVDC) systems can diminish power grid strength, potentially precipitating static voltage stability issues. To maintain the required level of grid strength while minimizing the total system cost, this paper proposes a coordinated expansion planning framework for multi-infeed LCC-HVDC (MI-HVDC) systems that explicitly incorporates generalized short-circuit ratio (gSCR) constraints. Specifically, the gSCR requirement is innovatively formulated as a semidefinite constraint, and the proposed framework is reformulated as a mixed-integer semidefinite program (MISDP) model to guarantee the global optimality. Then, to address the computational intractability of the MISDP model, a generalized Benders decomposition (GBD)-based approach is employed for efficient solution. Time-domain simulations conducted in PSCAD/EMTDC demonstrate the effectiveness of the proposed framework in enhancing the voltage stability of MI-HVDC systems.

Power systems are undergoing a policy-driven transition to decarbonization, which increases uncertainty and fluctuation due to the high penetration of renewable energy. Concurrently, advancements in communication and control technologies are unlocking greater demand-side flexibility. However, existing conventional long-term resource planning models for power systems do not adequately integrate demand response (DR) resources, operation simulations that account for uncertainty, and social factors such as government policy. To address these shortcomings, this paper introduces a robust planning frame-work for power system decarbonization pathways, specifically considering DR load as a critical flexible resource along with other techniques and impacting factors. Firstly, the long-term impacts of government policies and other social factors on system resource investment costs and development constraints are quantitatively considered, and the adjustable and transferable DR loads as key flexible resources are incorporated to smooth the fluctuations caused by renewable energy generation. Then, a three-stage robust planning model is developed by integrating long-term development planning, day-ahead unit commitment simulation, and intra-day power dispatch simulation to obtain the optimal solution with the lowest total cost over the planning period. Moreover, the column and constraint generation algorithm is modified to solve the planning model, specifically designed to address the decision-dependent uncertainty arising from new asset investments. Case study based on a provincial power system shows that the proposed framework enhances the integration of renewable energy by applying DR resources in line with real social development trends. The combination of long-term system planning, refined short-term operation simulation, and strategic DR integration ensures that the resulting decarbonization pathway is not only economically feasible but also highly robust and reliable.

An electricity market is a complex, dynamically operated network encompassing multiple participants under defined rules, thereby ensuring real-time supply-demand balance and system reliability. However, the inherent complexity and dynamism of the electricity market pose significant challenges to conventional modelling approaches, which often rely on expert knowledge and manual processes informed by market regulations. This reliance frequently leads to inefficiencies and elevated risks of error. To address these limitations, this paper proposes a framework for automated electricity market modelling and simulation centered on a large language model based agent, termed the modelling and simulation system agent (MSS-Agent) framework. The proposed MSS-Agent framework employs the hierarchical chain-of-thought (HCoT) method to more accurately extract essential information from relevant documents, thereby enhancing modelling fidelity. Moreover, it integrates tool usage and reflexive debugging to optimize the code generation process, ensuring reliability in automated electricity market modelling and simulation. Experimental results demonstrate that the proposed MSS-Agent framework significantly improves both mathematical model extraction accuracy and code execution reliability. Consequently, the proposed MSS-Agent framework not only increases simulation efficiency but also provides more precise and dependable tools for informed decision-making in electricity markets.

Inconsistent degradation of battery systems is a critical factor limiting the performance and safety. To address this challenge, this paper proposes a novel hybrid balancing strategy for simultaneously achieving high capacity utilization and lifetime optimization. The balancing performance is systematically evaluated within an electro–thermal–aging coupled simulation framework. The proposed strategy considers key state variables, including state of charge (SOC), state of health (SOH), and state of function (SOF), and adaptively switches the balancing variable under different SOC regions and operating conditions. Simulation results demonstrate that the proposed balancing strategy significantly improves battery consistency and mitigates performance degradation while maintaining high capacity utilization. Compared with SOC-based and SOF-based balancing strategies, the hybrid method reduces the standard deviation of SOH by approximately 34.6% and 38.1%, and the SOH range by approximately 26.3% and 33.3%, respectively, after 500 cycles. Meanwhile, the battery’s cycle number (when SOH reaches the 80%) is extended by 10.6% and 8.0% compared to SOC-based and SOF-based strategies, respectively. In addition, under second-life application scenarios, the proposed strategy also exhibits strong capability in suppressing inconsistency evolution, further demonstrating its effectiveness for long-term operation and its potential for practical engineering applications.

Energy transition in resource-constrained regions faces particularly severe challenges. This paper focuses on regions characterized by coal scarcity, lack of oil, limited gas reserves, and constrained renewable energy endowments, constructing a simulation model for regional low-carbon energy transition pathways that considers incoming electricity from imported sources. It also proposes a comprehensive assessment indicator system encompassing multiple dimensions such as energy, emissions, and economy for the transition pathways. Taking a certain resource-constrained province in middle China as a case study, the paper designs representative low-carbon energy transition pathways for the province's unique resource characteristics, conducts simulation exercises and quantitative assessment of these pathways, and carries out sensitivity analyses on parameters like coal price, carbon quota baseline, imported electricity price, and utilization hours of new energy sources. The computational examples demonstrate that the simulation model and indicator system developed herein can provide substantial support for the quantitative assessment of low-carbon energy transition pathways in resource-constrained regions.

Planning the low-carbon transition pathway of the power sector to meet the carbon neutrality goal poses a significant challenge due to the complex interplay of temporal, spatial, and cross-domain factors. A novel framework is proposed, grounded in the cyber-physical-social system in energy (CPSSE) and whole-reductionism thinking (WRT), incorporating a tailored mathematical model and optimization method to formalize the co-optimization of carbon reduction and carbon sequestration in the power sector. Using the carbon peaking and carbon neutrality transition of China as a case study, clustering method is employed to construct a diverse set of strategically distinct carbon trajectories. For each trajectory, the evolution of the generation mix and the deployment pathways of carbon capture and storage (CCS) technologies are analyzed, identifying the optimal transition pathway based on the criterion of minimizing cumulative economic costs. Further, by comparing non-fossil energy substitution and CCS retrofitting in thermal power, the analysis high-lights the potential for co-optimization of carbon reduction and carbon sequestration. The results demonstrate that leveraging the spatiotemporal complementarities between the two can substantially lower the economic cost of achieving carbon neutrality, providing insights for integrated decarbonization strategies in power system planning.

Assessing the benefits and costs of digitalization in the energy industry is a complex issue. Traditional cost-benefit analysis (CBA) might encounter problems in addressing uncertainties, dynamic stakeholder interactions, and feedback loops arising out of the evolving nature of digitalization. This paper introduces a methodological framework to help address the intricate inter connections between digital applications and business models in the energy industry. The proposed framework leverages system dynamics to achieve two primary objectives. It investigates how digitalization generally influences the value proposition, value capture, and value creation dimensions of business models. It also quantifies the financial and social impacts of digitalization from a dynamic perspective. The proposed dynamic CBA allows for a more precise quantification of the benefits and costs, associated with evidence-based decision-making. Findings from an illustrative case study challenge the static assumptions of conventional methods. These methods often presume continuous operation, neglecting reinvestment and operational feedback loops, and resulting in negative net present values. Conversely, the outcomes of the proposed method indicate positive net present values when accounting for factors such as reinvestment rates and the willingness to invest in digitalization projects. The principles outlined in this paper can enable a more accurate assessment of digitalization projects, thus catalyzing the development of new CBA applications and guidelines for digitalization.

Islanded multi-microgrids (MMGs) can provide reliable emergency power supply during extreme scenarios. However, under complex operating conditions and disturbances, the effectiveness of this capability depends critically on the design of power supply strategies and the system's dynamic response performance. To address this challenge, this paper proposes an emergency power supply strategy for MMGs based on improved finite-time consensus algorithm. The proposed strategy achieves distributed frequency and voltage regulation, day-ahead preventive dispatch, and intra-day emergency control through consensus cooperation among agents. The day-ahead preventive dispatch comprises the optimization of distributed generator (DG) outputs at the microgrid (MG) level and power coordination at the MMG level, achieving inter-MG coordination while respecting the autonomy of individual MGs. In the presence of any disturbances, the intra-day emergency control adjusts DG outputs in real time according to their respective participation factors to maintain the scheduled power exchanges among MGs, thus enhancing the robustness of the strategy. Furthermore, the improved finite-time consensus algorithm significantly accelerates the convergence speed of the solution. Case studies based on a MMG simulation model are conducted to verify the effectiveness and superiority of the proposed strategy.

With the increase in the permeability of renewable energy and the frequency of extreme weather, the power system requires a large amount of flexible power regulation capacity. In order to realize the multi-day cooperation of reserve resources, the stochastic optimization of medium-and short-term reserve arrangement considering the typhoon uncertainty is studied in this paper. Firstly, the extreme scenario generation and reduction method considering the typhoon path-intensity prediction uncertainty is constructed. Then, considering the combined cost of preventive and emergency control for adequacy in multiple scenarios, the reserve arrangement optimization model in extreme weather is built. In this model, the pre-dispatching strategies for multiple types of reserve resources are proposed to maintain the medium-and short-term coordination. Finally, case studies on a simplified 24-node power system of Zhejiang province, China are presented based on the data of the typhoon Fireworks in July 2021, and the result shows that the proposed reserve arrangement optimization model can reduce the total cost of power systems and the risk of operation under the typhoon disaster.

Voltage is a key operation index for active distribution networks. Coordinated power compensation of distributed energy resources (DERs) has shown promising results in alleviating voltage violation. Most methods use global optimization to obtain the optimal use of network-wide available resources, which becomes unfair when solving the same voltage issue of regional buses. Refined coordination of DERs on regional buses has not been formed as well. Therefore, this paper proposes a voltage sensitivity-related hybrid coordination algorithm. In order to fully tap into surplus reactive power of DESs, Volt/VAr control is considered in a higher priority than Volt/Watt control. For the selected crucial bus, a hybrid coordinated power compensation of regional DERs is designed in descending order of sensitivity or in proportion to available capacity under the same sensitivity value, which prioritizes using local and adjacent DERs to improve regulation effect and reduce changes in power flow. Due to the fact that voltage information is updated through association, the crucial bus needs to be reselected based on voltage assessment as a result, and the algorithm process will continue until the updated bus voltages become acceptable. Finally, the case study results in MATLAB indicate the effectiveness.

Many countries have set power system emissions reduction goals. However, in developing the electricity-carbon system, regulators may prioritize final targets while overlooking the planning of pathways. The paper aims to develop a simulation framework for the long-term interactive evolution of electricity-carbon systems and optimize a developing trajectory. To begin, the electricity system is modeled as a daily spot market over 365 days, and simulated by fast unit commitment (FUC) method. Then, considering the internal multi-player gaming, the multi-class mean field game (MMFG) theory is introduced to simulate the carbon market. Subsequently, a state transition equation for the electricity-carbon evolution is formulated based on the concept of trajectory optimization from optimal control theory. Here, the regulator can steer the evolution by adjusting the carbon emission intensity benchmark (CEIB) in the carbon market. Finally, employing the Twin Delayed Deep Deterministic Policy Gradients (TD3) technique, the problem characterized by high-dimensional state space and continuous action space is efficiently solved. The effectiveness of the proposed method is examined by case studies on a provincial-scale grid with over 200 units, where the optimal CEIB can be achieved within a second and the control precision of trajectory evolution can be limited to 2%.

The nuclear event risk (NER) is an important and disputed factor that should be reasonably considered when planning the pathway of nuclear power development (NPD) to assess the benefits and risks of developing nuclear power more objectively. This paper aims to explore the impact of nuclear events on NPD pathway planning. The influence of nuclear events is quantified as a monetary risk component, and an optimization model that incorporates the NER in the objective function is proposed. To optimize the pathway of NPD in the low-carbon transition course of power supply structure evolution, a simulation model is built to deduce alternative NPD pathways and corresponding power supply evolution scenarios under the constraint of an exogenously assigned carbon emission pathway (CEP); moreover, a method is proposed to describe the CEP by superimposing the maximum carbon emission space and each carbon emission reduction (CER) component, and various CER components are clustered considering the emission reduction characteristics and resource endowments of different power generation technologies. A case study is conducted to explore the impact of NER and its risk valuation uncertainty on NPD pathway planning. The method presented in this paper allows the impact of nuclear events on NPD pathway planning to be quantified and improves the level of coordinated optimization of benefits and risks.

During the whole restoration procedure of power systems, i.e., from the initial blackout to the complete restoration, coordinating the full-stage restoration of coupled transmission and distribution systems (CT&DSs) and dealing with multiple kinds of uncertainties are challenging problems to solve. Given this background, a triple-level full-stage adaptive restoration optimization method for CT&DSs considering restoration security risks is proposed in this paper. First, a full-stage coordinated restoration framework for CT&DSs is established, which outlines the restoration objectives and distinctive constraints for various entities, along with the information and service interaction relationships between these entities. Subsequently, a triple-level full-stage restoration optimization model for CT&DSs is presented to obtain the optimal restoration strategy and maximize the restoration benefits for CT&DSs. Then, a receding horizon control-based adaptive optimization method considering restoration security risk assessment is proposed to effectively deal with multiple kinds of uncertainties in the actual restoration process. Finally, case studies on an extended WECC 179-bus CT&DSs are conducted to test the performance of the proposed method, and simulation results show that the proposed method can achieve higher restoration benefits, faster black-start and load restoration speed compared with some existing restoration methods.

Future energy systems (FESs) require greater interaction, integration, and cooperation between physical infrastructure, cyber technologies, and human participants from prosumers to communities and governments. Cyber-Physical-Social Systems (CPSSs) will be the enabling technology to ensure the efficiency, effectiveness, sustainability, security and safety of energy generation and use. In this paper, we will first present an overview of the challenges in CPSSs. We will then outline potential contributions that CPSSs can make to FESs, as well as the opportunities that FESs present to CPSSs.

The increasing penetration of renewables has made electric power systems meteorology-sensitive. Meteorology has become one of the decisive factors and the key source of uncertainty in the power balance. Macro-scale meteorology might not fully represent the actual ambient conditions of the loads, renewables, and power equipment, thus hindering an accurate description of load and renewables output fluctuation, and the causes of power equipment ageing and failure. Understanding the interactions between microclimate and electric power systems, and making decisions grounded on such knowledge, is a key to realising the sustainability of the future electric power systems. This review explores key interactions between microclimate and electric power systems across loads, renewables, and connecting transmission lines. The microclimate-based applications in electric power systems and related technologies are described. We also provide a framework for future research on the impact of microclimate on electric power systems mainly powered by renewables.

The intensification of global climate change has led to a widespread consensus on carbon reduction, with the power industry being the principal contributor to carbon emissions necessitating an inevitable transition of its energy structure. Concomitantly, the escalating frequency of natural disasters caused by extreme weather presents formidable challenges to both secure power supply and the low-carbon transition of the power system. To fulfill the developmental requirements of a low-carbon power system and address the carbon emission risks imposed by natural disasters, this paper proposes a bi-level model for generation expansion planning (GEP) that incorporates constraints on the carbon emission trajectory and the influence of natural disasters. The planning-level model optimizes investment costs of various generation technologies and energy storage (ES), as well as the overall operational expenses over the planning period, with an objective to minimize them. It incorporates carbon emission trajectory constraints and policy constraints, such as carbon peaking, carbon neutrality and renewable energy (RE) penetration rates, in order to optimize the planning installed capacity of power sources. The operational-level model aims to minimize typical daily operating costs while also simulating power unit outputs in routine and disaster scenarios. A case study is conducted in a disaster-prone province in southern China to analyze the power generation expansion planning and the trajectory of carbon emissions from 2020 to 2060 under different scenarios. The simulation results show that compared to thermal power, the planning scheme mainly focused on RE with ES is better suited to achieve the goal of a low-carbon transition of the power grid. Moreover, after considering natural disasters, the cost and carbon emissions of power system planning are higher, and the risk of carbon emissions increases with the severity of disasters.

The long-term evolution of coal power installed capacity in the physical dimension is affected by social factors such as coal-related policy mechanisms (carbon pricing and capacity electricity prices) and the decommissioning decision of existing coal power. Under the guidance of the Cyber-Physical-Social system in Energy (CPSSE), a hybrid simulation model covering coal power policy, existing coal power decommissioning decision, and long-term evolution of coal power installed capacity is established. The evolution process of the long-term financial condition and decommissioning time of each coal power plant with different carbon emission costs and capacity price revenue are obtained by the simulation model. And impact of coal power plant decommissioning on the long-term power generation balance of the whole system are analyzed. The results show that high carbon emission costs may have a fatal impact to the financial condition of coal power plants, and the resulting large-scale decommissioning in advance of coal power plants can cause long-term power generation balance risks of the whole system, and a reasonable capacity price can help to address the above risks.

The carbon market plays a critical role in promoting the transition toward renewable energy sources and reducing greenhouse gas emissions in the electricity generation and transmission. Extant research has overlooked the dynamic bilateral causality that exists between electricity and carbon markets. Moreover, these studies have frequently treated the macroeconomic effect as exogenous. To bridge this research gap, this paper presents a holistic modeling framework that comprehensively captures the intertwined nature of electricity and carbon markets and their concomitant interactions with the overarching economy. The suggested modeling framework is an integration of three principal modules, namely, a carbon market, an electricity market, and economic system. This synergistic blend provides an exhaustive understanding of the entire market operation cycle. It offers detailed clearance rules, and most importantly, it adopts a macroeconomic systematic modeling approach for evaluating the impact emanating from the interconnected electricity and carbon markets. To illustrate the practicality and effectiveness of the proposed approach, a case study anchored on empirical data sourced from the electricity and carbon markets in China is conducted. The empirical findings underscore the fact that incorporating a green certificate market into the modeling framework can precipitate a reduction in greenhouse gas emissions. Additionally, the results indicate that expanding the scale of the green certificate market from 1.9% in 2021 to 33% by 2023 will increase the generation of green electricity by 10%.

Due to cyber-physical fusion and nonsmooth characteristics of energy management, this article proposes a security event-trigger-based distributed approach to address these issues with developed smoothing technique. To tackle with nonconvex and nondifferentiable issue, a randomized gradient-free-based successive convex approximation is developed to smooth economic objective function. Due to resilience ability against security issue, a security event-triggered mechanism-based distributed energy management is proposed to optimize social welfare, which coordinately controls both power generators and load demand. The security event-triggered mechanism is designed to reduce power system security risks, and relieve communication burden caused by smoothing calculation, the convergence of proposed distributed algorithm is also properly proved. According to those obtained results on both IEEE 9-bus and IEEE 39-bus systems, it reveals that the proposed approach can achieve good convergence performance and have less security risks than other alternatives, which also proves that the proposed approach can be a viable and promising way for tackling with energy management issue of cyber-physical isolated power system.

With the development of carbon markets, conventional generators are undergoing electricity markets and carbon markets at the same time. To fulfill the carbon market requirements and cope with the emerging cross-market arbitrage, the multi-market strategic behaviors of the generation company (GenCo) should be carefully studied. In this paper, the strategic behaviors of a GenCo with joint consideration of the electricity market and carbon market are modeled. First, the dynamic carbon emission intensity (CEI) is introduced to achieve the numerical relationship between the power output and carbon emissions, by which GenCo is demonstrated as a prosumer in the carbon market. Then, the strategy model is built in a bi-level structure. GenCo's multi-market profits maximization model is built at the upper level to determine the best bidding curves. The lower level is made up of two market-clearing models: the day-ahead electricity market for economic power dispatch and the bi-directional carbon market accounting for prosumers’ variable trading positions. Then, the high dimensional nonlinearity bilevel model is reformulated and part-linearized by the Karush-Kuhn-Tucker and strong duality theorems. Case studies based on an IEEE 30-bus system indicate that dynamic CEIs have a significant impact on GenCo's cross-market arbitrage by determining its trading volume and positions in the carbon market.