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Reusing lithium-ion batteries retired from electric vehicles (EVs) has received great attention as the performance of these batteries is still adequate for many stationary energy storage applications, such as micro-grids (MGs). To date, the economic and technical performance of second-life batteries (SLBs) in isolated MG systems remains obscure. Hence, this study focuses on the economic assessment of SLBs in an isolated 100% renewable MG, in order to identify a profitable application that promotes SLBs. The MG capacity design parameters are obtained by a two-stage chance-constrained stochastic optimization framework. To enhance the robustness of the MG design, a reliability-oriented iterative design process is proposed. This process involves continuous updates to the MG design, guided by validation results, until all specified requirements are satisfied. The lifetime of SLBs is determined using a semi-empirical degradation model seamlessly integrated into the validation process. Economic comparisons between SLBs and first-life batteries (FLBs) are conducted under identical design requirements, with consideration given to estimating the "price ceiling" and the impact of market factors. The results show that SLBs prove to be a more cost-effective option, especially when there is a demand for high reliability standards and the local electricity prices are comparatively lower. [Display omitted] • A reliability-oriented iterative design framework is proposed for battery. • Second-life batteries outperform new ones when they cost less than 106.5 $/kWh. • High-reliability system designs benefit more from second-life batteries. • Lower electricity prices enhance the competitiveness of second-life batteries. [ABSTRACT FROM AUTHOR]

With climate change, we have been witnessing more frequent extreme weather events causing increasingly common large-scale power outages. It is essential and urgent to improve power system resilience, which also substantially impacts the resilience of dependent infrastructures, such as water and health systems. This work investigates the enhancement of power grid resilience using proactive network-constrained economic dispatch (NCED) strategies. An extreme weather event is modeled as an attacker interdicting a selected set of transmission lines to cause overloading of remaining lines, which potentially leads to cascading failures. We define a set of resilience metrics, with the first one being a weighted number of overloaded lines immediately after the attack to capture the potential cascading chain effect, the second one predicting the worst-case value of the first metric to provide a forward-looking evaluation, and the last one assessing whether each line can be overloaded in the worst case to supply more granular awareness. We also propose a defender–attacker–defender NCED model solved by a column-and-constraint generation algorithm to optimize the defined metrics. The model can generate strategies that (1) enhances resilience without additional NCED cost; (2) further enhances resilience with a budgeted extra NCED cost; and (3) achieves a moving target defense scheme shifting the grid's vulnerable part(s). The associated price of resilience is specifically evaluated. Results on standard test systems demonstrate the proposed methods' effectiveness. Overall, our methods and results provide insights on the establishment of social, economic and environmental resilience by contributing to the resolution of resilience-related power and energy issues. [Display omitted] • Three resilience metrics to assess cascading failure risks due to extreme weathers. • A novel defender–attacker–defender network-constrained economic dispatch model. • A moving target defense scheme of economic dispatch to shift grid vulnerabilities. • The associated price of resilience enhancement specifically evaluated. [ABSTRACT FROM AUTHOR]

Since the increasing integration of inverter-based resources leads to a transition to inverter-interfaced power systems, the additional physical limits of inverters raise concerns for secure operation, which needs to be not only ensured but also enhanced. This study, therefore, investigates how to geometrically ensure and enlarge the security region of single-inverter-interfaced power systems, followed by establishing the inverter-security-constrained optimal power flow for multi-inverter-interfaced power systems. Therein, the inverter over-current and over-modulation/over-voltage limits are visualized by projecting cones into circles, ellipses, and a quadratic curve in different control variable spaces. First, for single-inverter-interfaced power systems, this study proposes a closed-form sufficient condition that guarantees the security region existence under different control modes. Then, this study quantifies and enlarges the security region size by leveraging the counter-intuitive complementary impacts of inverter parameters and the opposite impacts of system parameters. As an extension, an inverter-security-constrained optimal power flow using second-order cone programming relaxation is formulated for multi-inverter-interfaced power systems, in which this study proposes a closed-form sufficient condition to ensure a non-empty and non-decreasing security region. Numerical results of a single-inverter-interfaced power system and a modified IEEE 14 bus multi-inverter-interfaced power system verify the proposed security region properties. [Display omitted] • Ensuring the security region existence for single- and multi-inverter-interfaced systems. • Enlarging the security region size from the dimensions of control variables and state variables. • Inverter-security-constrained optimal power flow via second-order cone programming relaxation. • Geometrical visualization of inverter limits via cones, circles, ellipses, and a quadratic curve. [ABSTRACT FROM AUTHOR]

The demand for carbon neutrality leads to the transition from traditional synchronous generator-based power systems to power electronics-enabled power systems. The controllability and observability of power electronics devices are achieved by underlying metering and communication infrastructures, which are vulnerable to cyber-attacks. This review comprehensively investigates the cyber resilience of power electronics-enabled power systems from three aspects, i.e., before, during, and after cyber-attack events. Specifically, the cyber resilience of multiple power electronics devices in power generation (photovoltaic and wind), power transmission (high-voltage direct-current), power prosumption (electric vehicle, smart building, microgrid), power storage, and grid-tied converters, are addressed, respectively. In addition, this review thoroughly investigates the representative cyber-attack events, cyber-defense threats, cybersecurity regulations, and graphical cyber–physical architectures of the power electronics-enabled power system. As a result, this review proposes a general cybersecurity paradigm of power electronics closed-loop controllers. Therein, the cyber timescale, stealthiness, and threat between power electronics closed-loop controllers and power grid open-loop applications are investigated, respectively. [Display omitted] • Cyber resilience of power electronics devices before, during, and after cyber events. • A general cybersecurity paradigm of power electronics closed-loop controllers. • Cyber events, threats, and regulations of power electronics-enabled power systems. • Graphical cyber–physical architectures of multiple power electronics devices. [ABSTRACT FROM AUTHOR]

Considering the time-delay of energy transmission in cooling/heating pipelines and the uncertainty of renewable energy (RE), a distributionally robust optimal (DRO) dispatch model of combined cooling, heating and power campus microgrids (CCHP-CMG) is established. The method of characteristics is used to obtain the analytical algebraic solution of the partial differential equation (PDE) describing the energy transmission in pipelines and to add the solution to the optimal dispatch model. Based on the Jensen–Shannon divergence distance, an ambiguity set including the probability distribution (PD) information of the actual historical data is proposed to describe the uncertainty of RE outputs, and a min–max bi-level DRO dispatch model is established. An alternative iteration method is proposed to solve the bi-level model to obtain the solution that satisfies the constraints for the worst PD of uncertain variables. Additionally, a method for reconstructing the inner-layer problem is proposed to significantly reduce the computational complexity. Test results for an actual CCHP-CMG demonstrated the high computational accuracy and efficiency of the method of characteristics in solving the PDE compared to the finite difference method. The proposed reconstruction method is accurate and efficient, and the proposed DRO method can easily adjust the conservativeness of the obtained dispatch scheme. • A DRO dispatch model for a CCHP-CMG considering time-delay of pipelines is proposed. • The method of characteristics is used to transform the PDEs into algebraic equations. • A JS divergence distance-based AS is proposed to describe the uncertain RE output. • A method for reconstructing the inner-layer optimization problem is proposed. • Results show the accuracy and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

• The extreme-weather-induced cascading failures among subsystems are discussed. • A bilevel resilient dispatch framework for the electricity-gas system is proposed. • Stackelberg game is utilized for optimal discharging prices and schedules of EVs. • Electricity-gas systems resilience is enhanced with the participation of VPPs. • Impacts of VPPs on resilience of power grids and gas networks are illustrated. As extreme weather disasters become more frequent and violent, there is grown emphasis on the resilience of electricity-gas systems, especially in preventing the extreme-weather-induced cascading failures among different subsystems. Meanwhile, with multiple advantages such as high flexibility, virtual power plants (VPPs) are gaining popularity, which shows their potential for providing advanced resilience services. Therefore, this paper proposes a bilevel coordinated dispatch strategy to utilize VPPs, consisting of battery storage devices and dispatchable electric vehicles (EVs), to enhance electricity-gas system resilience. Firstly, the Monte Carlo simulation method is adopted to simulate the probabilistic and sequential process of the extreme-weather-induced cascading failures between gas networks and power grids. Additionally, VPPs dispatch the battery storage through the direct control mode. Due to the price-sensitive nature of EVs, the interaction between VPPs and EV owners can be described as a Stackelberg game to determine the optimal discharging prices and schedule, which ensures the minimization of dispatch cost and the maximization of resilience and EVs owners' revenues. According to the Karush-Kuhn-Tucker optimality conditions, the bilevel game model can transform into single-level mix-integer linear programming. At last, the proposed model is applied to two case systems: a modified IEEE 30-bus system with an 11-node gas network and a modified IEEE 118-bus system with a 20-node gas network. And simulation results verify the effectiveness of the proposed model in enhancing electricity-gas system resilience against the cascading outages among subsystems through dispatching energy from virtual power plants. [ABSTRACT FROM AUTHOR]

The natural gas system and the electric power system are becoming increasingly coupled in consideration of more interdependent components, e.g., natural gas-fired units, in the systems. Energy supplies for these two systems need to be dispatched economically from their own perspectives when these two systems belong to different owners. This paper investigates the best response-based individually look-ahead scheduling for the natural gas system and the electric power system. The optimal scheduling strategies for these two systems are the best responses of the opposite sides, and the best response of each side is achieved by an iterative best-response-search approach. Each best-response-search process is established as a bilevel mixed integer linear programming model, in which the upper level aims to minimize each side's operational cost constrained by on–off states of generators/lines/gas sources, and the lower level aims to minimize the total energy consumption cost constrained by the operational constraints in consideration of the scheduling of generators/lines/gas sources of two systems in the upper level. To deal with solving difficulty caused by binary variables in the lower level, the original bilevel mixed integer linear programming model is converted into a constrained single-level formulation, which is solved by a decomposition algorithm based on an iterative column-and-constraint generation method. Two cases validate the model and the algorithm, and the costs with the best response-based model and the joint optimization-based model are compared. • Best response-based individually look-ahead scheduling of the power system and the natural gas system is presented. • The best response for each side is achieved by an iterative best-response-search approach. • Each best-response-search process is established as a bilevel mixed integer linear programming model. • The bilevel mixed integer linear programming model is converted into a constrained single-level model. • Costs with best response-based and joint optimization-based models are compared. [ABSTRACT FROM AUTHOR]

The increased high-impact and low-probability extreme weather events have posed unprecedented impacts on power system operation, and it is necessary to have appropriate methods to analyze the impacts. In this paper, a sequential steady-state security region (SSSR) is proposed to better describe the operational region impacted by sequential weather events, and SSSR is a polytope describing a region, where the operational constraints are satisfied. Based on SSSR with uncertain topology changes because of extreme weather events, a bi-level programming model is proposed. By means of Karush–Kuhn–Tucker conditions, the lower-level optimization model is equivalently transformed into a set of linear constraints, which are included in the upper-level optimization model. System topology scenarios are generated with the Monte Carlo method to avoid the curse of dimensionality caused by numerous uncertain topology scenarios. The generated system topology scenarios are mapped into the binary variables, representing line states, by means of the recursive McCormick (RMC) envelopes. Two test systems validate the proposed model. The results show that the proposed SSSR can well describe the feasible sequential region with regard to extreme weather events. • A sequential steady-state security region (SSSR) for power systems is developed. • SSSR-based power system resilience enhancement is established as a bi-level programming model. • Focus on SSSR with uncertain topology changes due to extreme weather events. • Power system topology changes are mapped as constraints with the recursive McCormick envelopes. [ABSTRACT FROM AUTHOR]

Natural gas, hydrogen, methane, or their mixture play important roles in the energy systems, and the coordinated optimal scheduling for the integrated energy systems are requisite in consideration of complicated system characteristics. This paper focuses on mixed-integer second-order cone programming-based optimal scheduling for the electric grids with gas flow dynamics in consideration of AC power flow, unit commitment, and line switching. For the gas grids, differential continuity equations and differential momentum equations, representing gas flow dynamics, are discretized to a group of algebraic equations with the implicit trapezoidal rules. In the algebraic equations, nonconvex bilinear terms with integer variables, representing gas flow directions, are transformed into linear inequality constraints with McCormick envelopes, and quadratic terms are relaxed by the second-order cone (SOC) approach. For the electric grids, SOC relaxation-based AC power flow models are employed, and the line switching problem is modeled by the improved SOC relaxation approach with McCormick envelopes. The entire problem is established as a mixed-integer second-order cone programming model, and the impacts of gas flow dynamics on the feasible region compared to the steady-state region are discussed. • Present MISOCP-based scheduling for gas-electricity systems with gas flow dynamics. • Investigate gas flow dynamics with implicit trapezoidal rules and McCormick envelopes. • Tightening relaxation in consideration of unit commitment and line switching is studied. • Discuss the impacts of gas flow dynamics on feasible regions of the optimization model. [ABSTRACT FROM AUTHOR]