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When deploying fast charging stations (FCSs) to support long-distance trips of electric vehicles (EVs), there exist indirect network effects: while the gradual diffusion of EVs directly influences the timing and capacities of FCS allocation, the decisions for FCS allocations, in turn, impact the drivers’ willingness to adopt EVs. This interplay, if neglected, can result in uncovered EVs and security issues and even hinder the effective diffusion of EVs. In this paper, we explicitly incorporate this interdependence by quantifying EV adoption rates as decision-dependent uncertainties (DDUs) using decision-dependent ambiguity sets (DDASs). Then, a two-stage decision-dependent distributionally robust FCS planning (D3R-FCSP) model is developed for adaptively deploying FCSs with on-site sources and expanding the coupled distribution network. A multi-period capacitated arc cover-path cover (MCACPC) model is incorporated to capture the EVs’ recharging patterns to ensure the feasibility of FCS locations and capacities. To resolve the nonlinearity and nonconvexity, the D3R-FCSP model is equivalently reformulated into a single-level mixed-integer linear programming by exploiting its strong duality and applying the McCormick envelope. Finally, case studies highlight the superior out-of-sample performances of our model in terms of security and cost-efficiency. Furthermore, the byproduct of accelerated EV adoption through an implicit positive feedback loop is highlighted.

The development of wind energy is indispensable in the pursuit of global carbon neutrality. This article's analysis of observational data across China reveals the annual average wind speed declined at a rate of -0.167 m · s-1decade-1between 1981 and 2014. This rate is 33 times faster than projections from the Coupled Model Intercomparison Project (CMIP) of the World Climate Research Programme. We propose a novel wind power scale estimation method based on annual average wind speed, suitable for assessing climate change impacts. Considering China's planned wind power generation in 2030, climate change may increase the required wind installed capacity by over 25% under the observed trend scenario. In contrast, historical average and CMIP scenarios could substantially overestimate wind potential while underestimating the necessary future wind power development scale. Climate change poses potential adverse impacts on China's carbon peak goals, necessitating targeted measures to mitigate these effects.

Weather-sensitive resources are the main source of uncertainties in power systems. However, the unpredictable climate change further introduces ambiguity into the system, since the weather-sensitive resources would evolve with the climate and gradually exhibit a different probability distribution from the past in an uncertain manner. Lack of considering this climate-induced ambiguity in transmission network expansion planning (TNEP) may cause misunderstanding of future operational scenarios. Aiming at a higher security operation level under climate change yet less line investment, this paper proposes a climate-adaptive TNEP, which is essentially a robust TNEP equipped with a climate-adaptive uncertainty set (CUS). Determination of the CUS involves three steps. First, model future unknown distribution under climate change. Specifically, the climate-driven evolution in distributions is quantified by an evolutionary distance between historical and future true distributions, whose upper bound is derived from practical data, while the future unknown distribution is then modeled by a distance-based ambiguity set; Second, determine the CUS which has a minimal volume yet a desired confidence level in the face of the ambiguous future distribution. To that end, a parametric Wasserstein distance-based distributionally robust optimization (p-WDRO) is developed over the ambiguity set; Third, solve the p-WDRO by a data-clustering-incorporated reformulation. After the CUS is determined, the overall climate-adaptive TNEP is solved by a column-and-constraint-generation method with an inner multi-loop algorithm tailored for the CUS. Simulations are conducted on three test systems with practical data, which demonstrate that the climate-adaptive TNEP can improve operational security under climate change while reducing investment costs.

Cascading failure causes a major risk to society currently. And dynamic thermal rating (DTR) technique is a cost-effective approach to mitigate the risk by exploiting potential transmission capability. From the perspectives of service life and Braess paradox, it is important and challenging to jointly optimize the DTR placement and its operation schedule for changing system state, which is a two-stage combinatorial problem with only discrete variables, suffering from no approximation guarantee and dimension curse in traditional solving algorithms. Thus, the present work proposes a novel two-stage submodular optimization (TSSO) of DTR for risk mitigation considering placement and operation schedule. Specifically, it optimizes DTR placement with proper redundancy in first stage, and then determines the corresponding DTR operation for each system state in second stage. Under the condition of the Markov and submodular features in sub-function of risk mitigation, the submodularity of total objective function of TSSO can be proven for the first time. Based on this, a state-of-the-art efficient solving algorithm is developed that can provide a better approximation guarantee than previous studies by coordinating the separate curvature and error form. The performance of the proposed optimization model is verified by case results.

PV-PQ switching of generator buses when hitting reactive power limits is an important factor in voltage stability. This article establishes the formulation and convexification of voltage stability constrained optimal power flow (VSC-OPF) considering PV-PQ switching that co-optimizes generation dispatch and bus type profile. The conventional power flow Jacobian is intractable to be adopted since it has a variable size under PV-PQ switching. To solve this issue, a big-M-parameterized constant-size power flow Jacobian is constructed which explicitly considers PV-PQ switching. It is proved that this new Jacobian has the same minimum eigenvalue as the conventional Jacobian, which enables us to propose a more tractable VSC-OPF by enforcing a lower bound for the constant-size Jacobian minimum eigenvalue. Further, a linear approximation of the voltage stability constraint is constructed by combining smoothing, relaxation and eigen-sensitivity techniques. Finally, we establish an semi-definite programming formulation for VSC-OPF considering PV-PQ switching, which can be efficiently solved by outer linearization algorithm. The proposed formulation and convexification techniques are extendable to include more general PV-PQ switching actions triggered by hitting the capability curve of converter-interfaced generators. The proposed method is verified via numerical tests on the IEEE 118-bus system. Comparing to the VSC-OPF under a predefined bus type profile, our method finds out the optimal bus type profile that helps to achieve a lower-cost solution and higher precision in voltage stability assessment.

This article proposes a novel method for analytically assessing the value of renewable forecasting accuracy in power system operation. Different from previous assessment methods where renewable forecasting accuracy is perturbed numerically, the proposed method directly derives sensitivity formulae of the operation model with respect to (w.r.t.) renewable forecasting error distribution change. Compared with conventional analytical sensitivity methods dealing with only deterministic parameters, deriving such sensitivity formulae is challenging and necessitates the closed-form expression of forecasting error distribution parameters in the operation model. To tackle this challenge, we propose to model and reformulate the two-stage stochastic unit commitment for wind-penetrated power systems based on the Point Estimate Method (PEM). Then, leveraging the explicitly expressed wind power statistical moments in the PEM-based model, the sensitivity formulae of expected operation costs w.r.t. both global and nodal wind power forecasting error distribution change are derived. The proposed method provides to assess the value of renewable forecasting accuracy and identify critical nodes with uncertain injections that are most influential on system operation without exhausting numerical simulations. Case studies on modified IEEE systems verify the effectiveness of the proposed method.

In this letter, we analytically investigate the sensitivity of stability index to its dependent variables in general power systems. Firstly, we give a small-signal model, the stability index is defined as the solution to a semidefinite program (SDP) based on the related Lyapunov equation. In case of stability, the stability index also characterizes the convergence rate of the system after disturbances. Then, by leveraging the duality of SDP, we deduce an analytical formula of the stability sensitivity to any entries of the system Jacobian matrix in terms of the SDP primal and dual variables. Unlike the traditional numerical perturbation method, the proposed sensitivity evaluation method is more accurate with a much lower computational burden. This letter applies a modified microgrid for comparative case studies. The results reveal the significant improvements on the accuracy and computational efficiency of stability sensitivity evaluation.

When performing the resilience enhancement for distribution networks, there are two obstacles to reliably model the uncertain contingencies: 1) decision-dependent uncertainty (DDU) due to various line hardening decisions, and 2) distributional ambiguity due to limited outage information during extreme weather events (EWEs). To address these two challenges, this paper develops scenario-wise decision-dependent ambiguity sets (SWDD-ASs), where the DDU and distributional ambiguity inherent in EWE-induced contingencies are simultaneously captured for each possible EWE scenario. Then, a two-stage tri-level decision-dependent distributionally robust resilient enhancement (DD-DRRE) model is formulated, whose outputs include the optimal line hardening, distributed generation (DG) allocation, and proactive network reconfiguration strategy under the worst-case distributions in SWDD-ASs. Subsequently, the DD-DRRE model is equivalently recast to a mixed-integer linear programming (MILP)-based master problem and multiple scenario-wise subproblems, facilitating the adoption of a customized column-and-constraint generation (C&CG) algorithm. Finally, case studies demonstrate a remarkable improvement in the out-of-sample performance of our model, compared to its prevailing stochastic and robust counterparts. Moreover, the potential values of incorporating the ambiguity and distributional information are quantitatively estimated, providing a useful reference for planners with different budgets and risk-aversion levels.