Boosting the resilience of power systems is one of the core requirements of smart grid. In this paper, an integrated resilience response framework is proposed, which not only links the situational ...awareness with resilience enhancement, but also provides effective and efficient responses in both preventive and emergency states. The core of the proposed framework is a two-stage robust mixed-integer optimization model, whose mathematical formulation is presented in this paper as well. To solve the above model, an algorithm based on the nested column-and-constraint generation decomposition is provided, and computational efficiency improvement techniques are proposed. Preventive response in this paper considers generator re-dispatch and topology switching, while emergency response includes generator re-dispatch, topology switching and load shedding. Several numerical simulations validate the effectiveness of the proposed framework and the efficiency of the solution methodology. Key findings include the following: 1) in terms of enhancing power grid resilience, the integrated resilience response is preferable to both independent preventive response and independent emergency response; 2) the power grid resilience could be further enhanced by utilizing topology switching in the integrated resilience response.
We introduce emerging utility-scale energy storage (e.g., batteries) as part of the set of control measures in a corrective form of the security-constrained unit commitment (SCUC) problem. This ...enhanced SCUC (ESCUC) leverages utility-scale energy storage for multiple applications. In the base case, the storage units are optimally charged and discharged to realize economic operation. Immediately following a contingency, the injections of storage units are adjusted almost instantly to alleviate short-term emergency overloads, thereby avoiding potential cascading outages and giving slow ramping generating units time to adjust their output. The ESCUC is a large two-stage mixed-integer programming problem. A Benders decomposition has been developed to solve this problem. In order to achieve computational tractability, we present several acceleration techniques to improve the convergence of the proposed algorithm. Case studies on the RTS-79 and RTS-96 systems demonstrate the effectiveness of the proposed approach.
To seek the optimal defense strategy against malicious attacks, this paper proposes a novel defender–attacker–operator model applicable to the cyber‐physical power system. The proposed model ...considers the interdependence in functionality and topology of the cyber network and the power network as well as the cyber network's constraints. The defender aims to minimize the load curtailment caused by the attacker, while the latter intends to maximize the power loss. The operator in the bottom level takes corrective action to minimize the attack consequence. This tri‐level model is decomposed into a master problem and a subproblem, based on which the column‐and‐constraint generation algorithm is implemented to obtain the optimal solution. Comparative case studies based on IEEE RTS‐79 system are carried out to demonstrate the advantage of the proposed method, and to investigate the impact of the energy coupling strength, the topologically independent link and the defensive resource. The effectiveness of this model is validated by the sensitivity analysis.
This paper proposes a novel defender–attacker–operator model applicable to cyber‐physical power systems. The proposed model considers the functional and topological interdependence of the cyber network and power network as well as the cyber network's constraints. Comparative case studies validate the advantage and the effectiveness of the proposed method.
Regarding short-term reliability of composite power system, probability of critical event resulting in system failure within a short lead time is extremely low, which renders classical sequential ...Monte Carlo simulation method inefficient. In this paper, a cross-entropy-based three-stage sequential importance sampling (TSSIS) method is proposed to solve the low efficiency problem resulted from the low rate of component state transition during a fixed lead time. First, by assuming the system state transition process conforms to continuous time Markov chain, an analytical solution to optimal distorted component state transition rate to be used for sequential importance sampling is found by means of cross-entropy method. Second, TSSIS for a fixed lead time is constructed as follows: 1) acceleration of producing system state transitions; 2) enhanced learning to give optimal distorted transition rate; 3) compensation to the cost function. Case studies based on a reinforced Roy Billinton reliability test system and RTS-79 are carried out respectively for illustration of parameter settings of TSSIS as well as efficiency gain in comparison with the classical sequential Monte Carlo simulation method. The results demonstrate that given rational setting of parameters, TSSIS is of relatively high efficiency for sequential short-term reliability evaluation of composite power system.
Conventional power systems are developing into cyber-physical power systems (CPPS) with wide applications of communication, computer and control technologies. However, multiple practical cases show ...that the failure of cyber layers is a major factor leading to blackouts. Therefore, it is necessary to discuss the cascading failure process considering cyber layer failures and analyze the vulnerability of CPPS. In this paper, a CPPS model, which consists of cyber layer, physical layer and cyber-physical interface, is presented using complex network theory. Considering power flow properties, the impacts of cyber node failures on the cascading failure propagation process are studied. Moreover, two vulnerability indices are established from the perspective of both network structure and power flow properties. A vulnerability analysis method is proposed, and the CPPS performance before and after cascading failures is analyzed by the proposed method to calculate vulnerability indices. In the case study, three typical scenarios are analyzed to illustrate the method, and vulnerabilities under different interface strategies and attack strategies are compared. Two thresholds are proposed to value the CPPS vulnerability roughly. The results show that CPPS is more vulnerable under malicious attacks and cyber nodes with high indices are vulnerable points which should be reinforced.
This paper proposes a model for identifying critical components that affect the maintenance outage scheduling in reconfigurable distribution systems. The model considers critical factors such as ...prevailing severe weather conditions and component aging conditions, common mode outages, and repair rates in distribution systems. The effects of critical factors on forced outage rates of distribution system components are quantified by the proportional hazard model. A recursive sampling method is proposed to generate Monte Carlo scenarios with the given forced outage rate. In each scenario, the distribution system reconfiguration is optimized efficiently using a standard mixed-integer second-order cone program. Then absolute and relative criticality indices are calculated to describe the criticality of components. A new scenario is generated subsequently and the process is continued in the following iteration until the convergence condition is satisfied. The overall convergence of multiple scenarios is quantified by the coefficient of variation of criticality index. The final absolute and relative criticality indices are the average over multiple scenarios. The influence of component aging condition and forced outage characteristics, weather condition, and the system reconfiguration on component criticality for the maintenance outage scheduling is distinguished and exhibited in case studies. The case studies illustrate the effectiveness of proposed model and the solution method for identifying the critical components.
Economic dispatch (ED) considering valve-point effect, multiple fuel options, prohibited operating zones of generation units is a more accurate model compared to a conventional ED model. It is ...non-smooth and thus evolutionary algorithms (EAs) are so far the only feasible approaches for the model. In Part II of the paper, a new method, the dimensional steepest decline method (DSD), is proposed for the ED with non-smooth objectives. The DSD is based on the local minimum analysis of the ED problem presented in Part I of the paper. The fuel cost's decline rate between each two adjacent singular points is utilized to find the optimal solutions in a serial sequence. The computational complexity of the DSD is analyzed. The DSD has been applied to solve different types of ED problems on different test systems, including large-scale systems. The simulation results show that DSD can obtain more accurate solutions and consume much less time and its advantage is more obvious on large-scale systems, in comparison with the state-of-art EAs.
Power dispatch is a core problem for smart grid operations. It aims to provide optimal operating points within a transmission network while power demands are changing over space and time. This ...function needs to be run every few minutes throughout the day; thus, a fast, accurate solution is of vital importance. However, due to the complexity of the problem, reliable and computationally efficient solutions are still under development. This issue will become more urgent and complicated as the integration of intermittent renewable energies increases and the severity of uncertain disasters gets worse. With the recent success of artificial intelligence in various industries, deep learning becomes a promising direction for power engineering as well, and the research community begins to rethink the problem of power dispatch. This paper reviews the recent progress in smart grid dispatch from a deep learning perspective. Through this paper, we hope to advance not only the development of smart grids but also the ecosystem of artificial intelligence.
This paper discusses how fast-response distributed battery energy storage could be used to implement post-contingency corrective control actions. Immediately after a contingency, the injections of ...distributed batteries could be adjusted to alleviate overloads and reduce flows below their short-term emergency rating. This ensures that the post-contingency system remains stable until the operator has redispatched the generation. Implementing this form of corrective control would allow operators to take advantage of the difference between the short- and long-term ratings of the lines and would therefore increase the available transmission capacity. This problem is formulated as a two-stage, enhanced security-constrained OPF problem, in which the first-stage optimizes the pre-contingency generation dispatch, while the second-stage minimizes the corrective actions for each contingency. Case studies based on a six-bus test system and on the RTS 96 demonstrate that the proposed method provides effective corrective actions and can guarantee operational reliability and economy.
Cloud energy storage system (CESS) can effectively improve the utilization rate of the energy storage system (ESS) and reduce the cost. However, there is a lack of a model designed for large‐scale ...renewable energy power plants (REPPs). Due to the volatility and intermittency of renewable energy power generation, as well as the demand of following scheduling plan and market arbitrage, it is also necessary to configure ESS for REPPs. However, if the REPP builds ESS by itself, the investment is relatively high. Therefore, the application of CESS on the renewable energy generation side can reduce the investment cost and increase the revenue by utilizing the difference between actual output and demand.
Considering the uncertainties of renewable energy, this paper proposes a robust optimal configuration model of CESS based on the cooperative game. Firstly, the CESS model on the generation side is developed to describe the formation mechanism of ESS supply and demand. Then, the proposed model aims at maximizing the revenue of REPPs. The participants of the coalition are each REPP. By taking the renewable power uncertainty into consideration, the novel nested column‐and‐constraint generation (nested C&CG) method is utilized to solve the proposed model based on the min–max–min form. Furthermore, the Shapley‐value method is used to distribute the benefits to each member of the grand coalition. Finally, case studies verify the rationality and validity of the proposed model.
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