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As sustainable natural sources such as wind and solar are increasingly integrated into power grids, conventional fossil fuel-based generating units will be gradually replaced, resulting in enormous pressure on frequency regulation of the novel power systems. This paper proposes a method for implementing a cascaded load-damping factor controller based on the controlled load-damping factor controller. Series-type voltage regulators, namely, solid-state transformers or dynamic voltage restorers, can regulate the voltage of the 10 kV feeder within the allowed range. Therefore real-time control of composite load power is realized, which significantly improves the primary frequency regulation capability of novel power systems with renewable energy generation(REG). The effect of actuator regulation time constant is considered, The impact of dynamic loads on the primary frequency regulation of the controller, designed based on a static load model, is analyzed, The effectiveness of the controller on the primary frequency regulation under different renewable energy penetration rates is discussed, The effectiveness of the controller in smoothing short-term power fluctuations of REG is examined. Finally, the effectiveness of the series-connected controlled load-damping factor controller in primary frequency regulation is verified on the RTDS simulation experimental platform. The proposed control system is characterized by a simple structure, easy implementation, and convenient maintenance, which can realize the coordination and unification of reactive-voltage and active-frequency control of the novel power system in the 110 kV customer substation and is beneficial to practical application in engineering.

This paper proposes a coordinated frequency control scheme for emergency frequency regulation of isolated power systems with a high penetration of wind power. The proposed frequency control strategy is based on the novel nonlinear regulator theory, which takes advantage of nonlinearity of doubly fed induction generators (DFIGs) and generators to regulate the frequency of the power system. Frequency deviations and power imbalances are used to design nonlinear feedback controllers that achieve the reserve power distribution between generators and DFIGs, in various wind speed scenarios. The effectiveness and dynamic performance of the proposed nonlinear coordinated frequency control method are validated through simulations in an actual isolated power grid.

The early detection of cascading failure plays an important role in the safe and stable operation of the power system with high penetration of renewable energy. This paper proposes a fault propagation dynamic model based on the epidemic model, and further puts forward a method to detect the development of cascading failures. Through the simulation of the IEEE 39-bus and 118-bus systems, this model is proven to be valid and capable of providing practical technical support for the prevention of cascading failures in power systems with high penetration of renewable energy. This paper also provides an analysis method for the choice of different protection and control measures at each stage of cascading failure, which has critical significance and follow-up value.

As the result of renewable energy natural intermittency, load shedding and renewables curtailment are inevitable to keep supply and demand balance in high penetration level power systems. Luckily, with accurate estimation and proper allocation of the reserve, power fluctuation can be smoothed appreciably. Therefore, this study presents a novel coordinated day-ahead economic dispatch model for both energy and reserve in bulk hybrid AC/DC power systems. Flexible frequency operation for each regional sub AC system is applied to estimate reserve demand, while renewable energy forecast error is described by the versatile probability distribution. Considering practical operation structure in China, a non-iterative bi-layer dispatch algorithm is proposed to share power and reserve between interconnected subsystems by applying DC tie-lines’ flexibility. This bi-layer structure is achieved through the upper layer deciding the boundary variables with aggregated generation information provided by the lower layer. Then each lower layer regional AC grid solves its own programming with the boundary variables. Simulation is carried out to verify the efficacy of the proposed dispatch model and distributed solution methodology.

As the steady-state frequency of an actual power system decreases from its nominal value, the composite load of the system generally responds positively to lower power consumption, and vice versa. It is believed that this load frequency damping (LFD) effect will be artificially enhanced, i.e., sensitivities of loads with respect to operational frequency will increase, in future power systems. Thus, for wind-integrated power systems, this paper proposes a frequency-dependent chance constrained unit commitment (FDCCUC) model that employs the operational frequency as a dispatching variable so that the LFD effect-based load power can act as a supplemental reserve. Because the frequency deviation is safely restricted, this low-cost reserve can be sufficiently exerted to upgrade the wind power accommodation capability of a power system that is normally confined by an inadequate reserve to cope with uncertain wind power forecasting error. Moreover, when the FDCCUC model is applied to a bulk AC/DC hybrid power system consisting of several independently operated regional AC grids interconnected by DC tie-lines, a hierarchically implemented searching algorithm is proposed to protect private scheduling information of the regional AC grids. Simulations on a 2-area 6-bus system and a 3-area 354-bus system verify the effectiveness of the FDCCUC model and hierarchical searching algorithm.

Abstract Direct wind power purchase for large industrial users is a meaningful way to improve wind power consumption and decrease industrial production costs. Short-term wind power fluctuations may lead to large-scale wind power curtailment problems. To promote use of wind energy, a demand side control method is proposed based on output regulator theory for a grid-connected industrial microgrid with electrolytic aluminum loads to continuously track and respond to wind power fluctuations. The control model of the EALs and the dominant frequencies of the wind power fluctuation signals are analyzed and incorporated into the demand side control plant. The feedback control signals with active power deviations on the tie-line are used to design the demand side controller. Simulations are conducted for an actual industrial microgrid to validate the feasibility and effectiveness of the proposed method. The results demonstrate that the proposed controller based on output regulator theory is able to effectively track wind power fluctuations.

Abstract Charging resources are scarce due to the limited capacity of charging stations and the congestion of distribution networks. Here, a hierarchical scheduling framework that considers the privacy of participants is established to optimize the charging power with a fair payment mechanism. The charging resource allocation model is established considering the scarcity of charging resources. Furthermore, the model is divided into three sub problems, including the congestion management problem of distribution network operator, the energy management problem of charging station and the charging mode problem of electric vehicle. Then the collaborative optimization algorithms are proposed to ensure the privacy of participants. Finally, a hierarchical payment mechanism based on the opportunity cost theory is proposed. The performance of the proposed hierarchical scheduling framework is evaluated using the Roy Billiton Test System with 2000 EVs. On the premise of ensuring distribution network economic operation, the proposed hierarchical scheduling strategy can satisfy the requirement for privacy protection and eliminate the overload problem caused by electric vehicle charging. The simulation results have shown that the additional capacity cost has an obvious levelling effect. With the application of the payment mechanism, charging resources is equitably allocated. Also, the equilibrium of a market and the stable operation of power system can be guaranteed.

With the rapid increase of distributed photovoltaic (PV) power integrating into the distribution network (DN), the critical issues such as PV power curtailment and low equipment utilization rate have been caused by PV power fluctuations. DN has less controllable equipment to manage the PV power fluctuation. To smooth the power fluctuations and further improve the utilization of PV, the regulation ability from the demand-side needs to be excavated. This study presents a continuous control method of the feeder load power in a DN based on the voltage regulation to respond to the rapid fluctuation of the PV power output. PV power fluctuations will be directly reflected in the point of common coupling (PCC), and the power fluctuation rate of PCCs is an important standard of PV curtailment. Thus, a demand-side management strategy based on model predictive control (MPC) to mitigate the PCC power fluctuation is proposed. In pre-scheduling, the intraday optimization model is established to solve the reference power of PCC. In real-time control, the pre-scheduling results and MPC are used for the rolling optimization to control the feeder load demand. Finally, the data from the field measurements in Guangzhou, China are used to verify the effectiveness of the proposed strategy in smoothing fluctuations of the distributed PV power.

Due to the high penetration of renewable distributed generation (RDG), many issues have become conspicuous during the intentional island operation such as the power mismatch of load shedding during the transition process and the power imbalance during the restoration process. In this paper, a phase measurement unit (PMU) based online load shedding strategy and a conservation voltage reduction (CVR) based multi-period restoration strategy are proposed for the intentional island with RDG. The proposed load shedding strategy, which is driven by the blackout event, consists of the load shedding optimization and correction table. Before the occurrence of the large-scale blackout, the load shedding optimization is solved periodically to obtain the optimal load shedding plan, which meets the dynamic and steady constraints. When the blackout occurs, the correction table updated in real time based on the PMU data is used to modify the load shedding plan to eliminate the power mismatch caused by the fluctuation of RDG. After the system transits to the intentional island seamlessly, multi-period restoration plans are generated to optimize the restoration performance while maintaining power balance until the main grid is repaired. Besides, CVR technology is implemented to restore more loads by regulating load demand. The proposed load shedding optimization and restoration optimization are linearized to mixed-integer quadratic constraint programming (MIQCP) models. The effectiveness of the proposed strategies is verified with the modified IEEE 33-node system on the real-time digital simulation (RTDS) platform.

As the intermittency of wind power is a growing concern in the day-ahead economic dispatch, this paper proposes a day-ahead economic dispatch method considering extreme scenarios of wind power by using an uncertainty set. The uncertainty set inspired by robust optimization is used to describe wind power intermittency in this paper. Four extreme scenarios based on the uncertainty set are formulated to represent the worst cases of wind power fluctuation. An economic dispatch method considering the costs of both load shedding and wind curtailment is proposed. The economic dispatch model can be easily solved by a quadratic programming method owing to the introduction of four extreme scenarios and the uncertainty set of wind power. Simulation is done using the IEEE 30-bus system and the results verify the effectiveness of the proposed method.

On the basis of obtaining many characteristic information and optimization evaluation indicators for characterizing the internal insulation condition of gas insulated switchgear (GIS), how to effectively classify weights and integrate information on the evaluation indexes, and establish a comprehensive evaluation model for insulation condition considering economic value and universality is another difficult problem to be solved. In this research, the above-mentioned 23 comprehensive evaluation index system, which are optimized to cover the PD hazard, SF6 insulation performance and time, environment and economy, are put forward, the idea of correcting the SF6 decomposition component monitoring data are proposed, and the general mathematical correction formula and method are given. The improved DS evidence fusion theory is used to make information decision fusion. The multi-class information fusion evaluation model of GIS equipment insulation condition is established. The fuzzy level analysis is used as the evaluation method framework to solve the problem of the index weight setting and membership determination of the evaluation indexes. The application of the example shows that the evaluation model and technology established in this research has achieved multiple types of information complementation, and the evaluation results are reasonable and credible.

High-voltage direct current (HVDC) transmission is playing an increasingly important role in modern power systems, and the resulted power/voltage stability issue has raised widespread concern. This paper presents an on-line power/voltage stability index (PVSI) for multi-in-feed HVDC (MIDC) systems. Different from the existing indices which are developed mainly for off-line and static analysis, the proposed PVSI can be applied in real time. Effects of system changes on stability assessment such as change of system states and control strategies are considered. Thus, helpful guidance can be provided for on-line HVDC stability and controls. The PVSI is originally deduced for single-infeed HVDC systems in an “AC way” by analyzing the power and voltage stability of both pure AC systems and HVDC systems. Moreover, its on-line application in practical MIDC systems is realized by building an equivalent single-infeed model, and utilizing nowadays measurement and communication infrastructures such as wide-area measurement system (WAMS). The effectiveness of the PVSI is verified through simulations in real-time digital simulator (RTDS).

Wind power ramp events increasingly affect the integration of wind power and cause more and more problems to the safety of power grid operation in recent years. Several forecasting techniques for wind power ramp events have been reported. In this paper, the statistical scenarios forecasting method is proposed for wind power ramp event probabilistic forecasting based on the probability generating model. Multi-objective fitness functions are established considering cumulative density functions and higher order moment autocorrelation functions with respect to the consistency of distribution and timing characteristics, respectively. Parameters of probability generating model are calculated by the iterative optimization using the modified genetic algorithm with multi-objective fitness functions. A number of statistical scenarios captured bands are generated accordingly. Eventually, ramp event probability characteristics are detected from scenarios captured bands to evaluate the ramp event forecasting method. A wind plant of Bonneville Power Administration with actual wind power data is selected for calculation and statistical analysis. It is shown that statistical results with multi-objective functions are more accurate than the results with single objective functions. Moreover, the statistical scenarios forecasting method can accurately estimate the characteristics of wind power ramp events. The results verify that the proposed method can guide the generation method of statistical scenarios and forecasting models for ramp events.

Various developing countries have been promoting public–private partnership (PPP) infrastructure projects in recent years. Energy infrastructure project construction, which needs substantial capital investments, is a cornerstone of and supports economic development. A PPP-based energy infrastructure project is beneficial for alleviating developing countries’ financial burden and for facilitating the diversified development of the energy market. Therefore, to increase government income and reduce infrastructure project costs, the PPP energy project construction model attracts considerable attention of various industries, especially in developing countries. For example, as gross domestic product growth decelerates and the government debt ratio rapidly increases, many Chinese provinces and cities face financial obstacles to economic development; therefore the Chinese government has issued a series of PPP-based energy policies. However, a number of risk factors associated with PPP projects, and conflicting interests between governments and private investors, have resulted in project failure. This study analyzes the development of PPP in China from the economic and industrial development perspective. It combines the latest PPP energy policies of China to solve problems that stem from the main risk factors involved in constructing and operating PPP energy projects. This research also provides game analysis for achieving maximum benefits as the government and the private investor have conflicting interests.

As one of the most important renewable energy resources, wind power has drawn much attention in recent years. The stochastic characteristics of wind speed lead to generation output uncertainties of wind energy conversion system (WECS) and affect power system reliability, especially at high wind power penetration levels. Therefore, a more comprehensive analysis toward WECS as well as an appropriate reliability assessment model are essential for maintaining the reliable operation of power systems. In this paper, the impact of wind turbine outage probability on system reliability is firstly developed by considering the following factors: running time, operating environment, operating conditions, and wind speed fluctuations. A multistate model for wind farms is also established. Numerical results illustrate that the proposed model can be well applied to power system reliability assessment as well as solving a series of reliability-centered decision-making problems of power system scheduling and maintenance arrangements.

Frequency security is vital to the safety of power systems and has been scrutinized for many years. The conventional frequency security analysis only checks whether the frequency after anticipated initial failures can remain in the normal range based on some aggregated models, but the influence of potential cascading failures has not been considered yet. This is not enough, especially when the modern power system suffers the increasing threat of cascading failures. Therefore, this paper proposes a novel frequency simulation model considering the influence of cascading failure to reveal the security level of power systems comprehensively. The proposed model is based on a platform on which the frequency dynamics and the power flow distributions can be calculated jointly. Moreover, simulation models of protection devices and some supervisory operation-control schemes are also taken into account. Case studies validate the effectiveness of the proposed model on the IEEE 39-bus system. Moreover, the results of some further probabilistic simulations under different operation parameters are obtained, which show the great significance of improving the frequency regulation performance to cope with challenges of blackouts.

Embden-Meyerhof pathway (EMP) in tandem with 2-C-methyl-D-erythritol 4-phosphate pathway (MEP) is commonly used for isoprenoid biosynthesis in E. coli. However, this combination has limitations as EMP generates an imbalanced distribution of pyruvate and glyceraldehyde-3-phosphate (G3P). Herein, four glycolytic pathways-EMP, Entner-Doudoroff Pathway (EDP), Pentose Phosphate Pathway (PPP) and Dahms pathway were tested as MEP feeding modules for isoprene production. Results revealed the highest isoprene production from EDP containing modules, wherein pyruvate and G3P were generated simultaneously; isoprene titer and yield were more than three and six times higher than those of the EMP module, respectively. Additionally, the PPP module that generates G3P prior to pyruvate was significantly more effective than the Dahms pathway, in which pyruvate production precedes G3P. In terms of precursor generation and energy/reducing-equivalent supply, EDP+PPP was found to be the ideal feeding module for MEP. These findings may launch a new direction for the optimization of MEP-dependent isoprenoid biosynthesis pathways.