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Power systems are undergoing fundamental changes with increasing penetration of renewable energies. A reactive power ancillary service market is of significance to incentivize private providers, such as thermal power units and renewable power plants (RPPs), to participate in maintaining voltage security. Because of the local nature of reactive power, one of the recognized issues is that monopolistic participants may have great market power and strategically manipulate their costs. To pursue economic efficiency, this paper proposes an incentive-compatible mechanism based on Vickery-Clarke-Groves (VCG) auctions for the day-ahead reactive power ancillary service market. The obligations of reactive power ancillary service are quantified in the VCG-based market mechanism. Depending on the expected performance of participants compared with the corresponding obligations, payments are obtained for thermal power units, RPPs, and loads. Hence, providers have the incentive to declare their true costs and undertake obligations. Furthermore, a scarcity price is employed to reward the provision of scarce reactive power resources and ensure reliable market clearing. The proposed mechanism is illustrated using the IEEE 24-bus and 118-bus systems. The results verify the effectiveness of the proposed mechanism. Moreover, high rewards or penalties are observed for voltage weak areas, thereby inducing appropriate investments in reactive power capacities. [ABSTRACT FROM AUTHOR]

• Anovel analytical approach for the frequency stability constraint is proposed. • A frequency-constraint unit commitment model is established. • Adecomposition-based solution of the proposed model is introduced. • The effectivenessof the proposed modelisvalidatedin a real-life system. Modern power systems are confronted with great challenges of frequency instability due to the conventional generators constantly being replaced by renewable energy source. Therefore, it is necessary to consider the frequency stability constraints in unit commitment processes to obtain safer operation plans. In this paper, an analytical approach for the frequency stability constraint is proposed. This approach is given by estimating the largest disturbance the system can withstand. Then, a novel frequency constrained unit commitment model is proposed by incorporating the proposed approach into a unit commitment model. The diverse governor response characteristics are considered in the proposed frequency constrained unit commitment model, and the essential inertia and reserve level are accurately estimated. The proposed model is formulated as a mixed-integer nonlinear programming problem, and generalized Benders decomposition is adopted to facilitate the solution generation. The convergence of the decomposition-based solution is proved. Case studies on the New England 39-bus system and a regional system are carried out where the performance of the proposed model is compared with two existing frequency constrained unit commitment models. The results show that the proposed model can guarantee frequency security while preserving the economic factors in power systems with high renewable energy penetration. [ABSTRACT FROM AUTHOR]

• Joint dispatching of autonomous mobility-on-demand system and power system. • Multi-spatiotemporal scale dispatching models in power-traffic coordination. • Dispatching performance validated on a large-scale real-world case. • 15% reduction of total carbon emissions with vehicle-to-grid (V2G) considered. • Sensitivity analysis on fleet size, renewable energy profile, power system capacity. Electrified autonomous mobility-on-demand (E-AMoD) systems will play a crucial role in future cities, which couple the carbon emissions generated by the transportation system and the power system. This paper proposes a joint dispatching framework to facilitate city decarbonization by coordinating the fleet operator and power system operator. Given the substantial differences in spatial and temporal dispatching scales between the two systems, this framework effectively integrates micro vehicle-level service dispatching with macro fleet-level charging scheduling. Firstly, a novel bipartite matching model efficiently solves the short-term passenger serving and rebalancing dispatching at the vehicle level. The service dispatching results are then aggregated as spatial-temporal transition flows across different areas and time intervals. Incorporating these mobility constraints, the long-term charging-discharging scheduling at the fleet level is captured as an expanded network flow model and integrated with the power system dispatching model, which aims at renewable energy utilization and carbon emission mitigation. Finally, the fleet-level scheduling results are disaggregated to the serving-rebalancing-charging sequences of each vehicle, for practical implementation. To validate the proposed framework, numerical experiments are conducted on a large-scale real-world case. Multiple charging schedule scenarios, including heuristic charging, smart charging, and smart charging-discharging, are investigated under both centralized and distributed supplied power systems. (1) The framework proves to be an effective solution for addressing the differences in dispatching spatiotemporal scales, calculation efficiencies, and privacy concerns between power and traffic systems. (2) Through power-traffic coordination, the E-AMoD fleet demonstrates its ability to alleviate 8% of average renewable energy curtailment and reduce nearly 15% of the total emissions in a power system installed with renewable distributed generators. (3) Furthermore, the extent of emission mitigation effects is influenced by critical factors such as fleet size, renewable energy profile, and power system capacity. Overall, the E-AMoD system show great potential as mobile storage devices to provide spatial-temporal flexibility to the power system and contribute to city decarbonization. [ABSTRACT FROM AUTHOR]

Highlights • A model for detecting power system security weak spots is proposed. • Microclimates are considered in the proposed model. • A deep neural network is designed to extract deep features. • Parallelism is applied to enhance the training efficiency. • The proposed model can greatly improve the task accuracy. Abstract With the integration of renewable energy and microclimate-sensitive loads, secure and economic power system operation is becoming an increasingly important and complex problem. Therefore, based on big data from power systems and meteorological systems, a deep spatial-temporal data-driven model is proposed to predict and detect power system security weak spots during a future period. First, microclimates are considered in the proposed model. Then, a deep neural network structure is designed to extract deep features layer by layer for security weak spot detection. Furthermore, model simplification and parallelism as well as data parallelism are applied. Finally, the proposed model is evaluated based on the Guangdong Power Grid in China. The simulation results demonstrate that (1) power system security weak spots have spatial-temporal and microclimate-sensitive characteristics; (2) the deep model considering microclimates can greatly improve the task accuracy of online applications; and (3) simplification and parallelism can significantly enhance the training efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the impacts of intermittent renewable energy sources (RESs) and stochastic energy loads on the operation and uncertainties of an integrated energy system (IES). In our analysis, we use a first order reliability method (FORM) to estimate the failure probabilities, which are crucial for ensuring the reliability of the gas supply and surplus power absorption. The Hasofer Lind and Rackwitz Fiessler (HLRF) algorithm is introduced to solve the FORM optimization model while considering the stochastic behaviours and dependencies of multiple energy sources. A mathematical case is presented to demonstrate the use of the FORM in the estimation of failure probability, and the results are validated using the Latin hypercube sampling theories, including the Iman and Stein methods. The results of a failure probability analysis for an ideal IES are provided to illustrate the proposed technique. The failure probability can be used to improve IES operation and planning and ensure better reliability. [ABSTRACT FROM AUTHOR]

Inverter-based microgrids will be vital components of future energy systems with large-scale integration of renewable energy resources. To cope with the low inertia problem induced by inverters, fully distributed control has been shown to effectively improve system stability owing to its fast response capability. Investigating the property of multiple delays in distributed control architecture is critical to guide its practical implementation in cyber–physical systems. Compared with power systems equipped with decentralized or centralized controllers, inverter-based cyber–physical microgrids under fully distributed control have a more complicated and heterogeneous delay environment. Existing, proven delay properties in centralized systems, such as the delay merge property that measurement and control delays of decentralized power system stabilizers can be merged for small-signal stability analysis, are not applicable to distributed systems To investigate the novel delay properties in distributed systems, we first propose a block-matrix-based cyber–physical model considering heterogeneous delays for inverter-based microgrids under fully distributed control. Then, we derive and prove the novel properties of heterogeneous delay in such systems. Finally, we perform numerical tests based on a real-world 7-bus islanded microgrid and a modified PG&E 69-bus microgrid under fully distributed frequency control. Our findings show that the impacts of measurement delay and control delay of one distributed controller on system stability are equivalent only if all distributed controllers are identical. Moreover, as the diversity of distributed controllers increases, the heterogeneous delay property becomes more significant. • Inverter-based microgrid under fully distributed control is considered. • Heterogeneous delays are formulated in the proposed cyber–physical model. • Novel properties of distributed microgrids are found and proven. • Aggregated delay property is satisfied only if specific conditions hold. [ABSTRACT FROM AUTHOR]

The digital transformation of power systems into cyber-physical systems (CPSs) is the inevitable trend of modern power systems with the integration of large-scale renewable energy. The in-depth interdependence of cyber and physical spaces leads to more complicated external environments for such cyber-physical power systems (CPPSs) and brings great challenges to the resilience of CPPSs. A resilient CPS imposes strict requirements for its ability to cope with high-impact, low-probability cyber-physical disturbances. To better study the vulnerability and resilience of CPPSs, several representative blackouts from the past two decades are reviewed from the cyber-physical perspective. Inspired by general system theory, this study offers a framework with three key features of a CPPS and presents the three-layer interdependences from facilities to functions. The differences between CPPS resilience and conventional power system resilience are also emphasized. Thereafter, the study discusses the influence of cyber-physical disturbances from natural hazards, cyberattacks, and human-in-the-loop on the resilience of CPPSs. Accordingly, a survey of the state-of-the-art resilience-oriented techniques for CPPS in the face of natural hazards is organized based on quantitative metrics as well as planning and operation attributes. Regarding the resilience against cyberattacks, relevant cutting-edge research is reviewed in terms of prevention, detection, and mitigation strategies. Furthermore, from the cyber-physical-social perspective, the exploitation of social behaviors to inform the design of the physical system and the cyber system to ultimately enhance the resilience of CPPSs is also studied. Based on the findings from this research, the remaining challenges and the broad prospects of cyber-physical resilience enhancement techniques are also discussed. • A CPPS has three-layer interdependences on facilities, topologies, and functions. • External environments consist of hazards, cyberattacks, and human behaviors. • Resilience-oriented techniques for CPPSs in three categories are reviewed. • Cyber-physical collaborative techniques have broad prospects. [ABSTRACT FROM AUTHOR]

This letter presents a constraint relaxation optimal power flow (OPF) model to tackle the issues when traditional OPF is infeasible under large variations such as wind generation output. In this model, the original hard constraints are relaxed into soft constraints and the objective function is adjusted for the cost of constraint violations. To guarantee the equivalence to the original OPF model when there are feasible solutions, an exact penalty function method is introduced to justify the selection of penalty factor of constraint violations. By solving an optimization problem, the lower bound of the proper penalty factor is obtained. The results of a 6-bus test system show that the proposed method achieves the same solution when the original OPF has feasible region, and an optimal solution can be obtained with minimum constraint violation when original OPF has no feasible region. Lastly, three large IEEE systems are tested to verify the effectiveness of proposed method. [ABSTRACT FROM PUBLISHER]

Currently, a custom-designed configuration of different renewable technologies named the integrated energy system (IES) has become popular due to its high efficiency, benefiting from complementary multi-energy technologies. This paper proposes an information entropy approach to quantify uncertainty in an integrated energy system based on a stochastic model that drives a power system model derived from an actual network on Barry Island. Due to the complexity of co-behaviours between generators, a copula-based approach is utilized to articulate the dependency structure of the generator outputs with regard to such factors as weather conditions. Correlation coefficients and mutual information, which are effective for assessing the dependence relationships, are applied to judge whether the stochastic IES model is correct. The calculated information values can be used to analyse the impacts of the coupling of power and heat on power flows and heat flows, and this approach will be helpful for improving the operation of IES. [ABSTRACT FROM AUTHOR]

Owing to the potential higher energy supply efficiency and operation flexibility, integrated energy system (IES), which usually includes electric power, gas and heating/cooling systems, is considered as one of the primary forms of energy carrier in the future. However, with the increasing complexity of multiple energy devices and systems integration, IES planning is facing a significant challenge in terms of risk assessment. To this end, an energy hub (EH) planning model considering renewable energy sources (RES) and energy storage system (ESS) integration is proposed in this paper, in which the risk is measured by Conditional Value-at-Risk (CVaR). The proposed IES planning model includes two stages: 1) investment planning on equipment types and capacity (e.g., energy converters, distributed RES and ESS) and 2) optimizing the potential risk loss in operation scenarios along with confidence level and risk preference. The problem solving is accelerated by Benders Decomposition and Improved Backward Scenario Reduction Method. The numerical results illustrate the proposed method's effectiveness in balancing the potential operation risk and investment cost. Moreover, the effectiveness of reducing potential operation risk by introducing ESS and RES are also verified. [ABSTRACT FROM AUTHOR]

• A robust planning-operation co-optimization model for Energy Hub is proposed. • Sizing problem, precise model of storage and uncertainty are all considered. • Dual norm is used to solve two-stage robust optimization problem. • Precise storage model can improve economics of Energy Hub in planning and operation. • There are mutually beneficial relationships between multiple energy storage. The increasing penetration of renewable energy resources in integrated energy system makes it necessary to consider uncertainty, appropriate investment and scientific operation for energy storage systems, including battery energy storage systems and thermal energy storage systems. In this paper, we propose a two-stage robust planning-operation co-optimization method for Energy Hub considering uncertainties from renewable energy resources as well as multi-load demands, the sizing problem and precise economic model of energy storage systems, in which lifetime loss cost of battery energy storage systems and static loss of thermal energy storage systems are mainly included. Integer variables exist in both stages, thus in the proposed method, dual norms are used to transform uncertain constraints into robust counterparts with which the worst cases are calculated directly, then the problem is merged into a single stage mixed integer linear program. In case studies, optimal planning strategies including multiple storages in Energy Hub are explored under the consideration of sizing problem and uncertainties, and it's found that simultaneously constructing multiple energy storages can have mutually beneficial relationships and propose more economic planning/operation strategies. Then it's validated that precise energy storage model can better improve economics and flexibility of Energy Hub both in longer term and short term planning optimization. Meanwhile, convergence speed of the model and solution algorithm in this paper is acceptable for practical planning problem. [ABSTRACT FROM AUTHOR]