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A risk-aware scheduling method of multienergy virtual power plant (MEVPP) is proposed to measure the uncertainty in MEVPP. First, a novel day-ahead uncertainty scenario generation method based on denoising diffusion probabilistic model is proposed, and historical data are employed to learn the error relationship between real power curves and predict power curves. The probability distribution of the prediction error which describes the day-ahead output power curve of renewable energy source is learned by parameter training. Subsequently, the effect of risk aversion on decision-making is investigated by implementing conditional value-at-risk in the optimization model, MEVPP operation mode under the carbon trading and green certificate trading mechanism is analyzed. Finally, the proposed scheme is implemented on a test MEVPP with carbon trading, and green certificate trading is addressed in detail through a numerical study. Moreover, the effects of the operator's risk-averse behavior on the MEVPP are investigated. [ABSTRACT FROM AUTHOR]

With the rapid development of flexible interconnection technology in active distribution networks (ADNs), many power electronic devices have been employed to improve system operational performance. As a novel fully-controlled power electronic device, energy storage integrated soft open point (ESOP) is gradually replacing traditional switches. This can significantly enhance the controllability of ADNs. To facilitate the utilization of ESOP, device locations and capacities should be configured optimally. Thus, this paper proposes a multi-stage expansion planning method of ESOP with the consideration of tie-line reconstruction. First, based on multi-terminal modular design characteristics, the ESOP planning model is established. A multi-stage planning framework of ESOP is then presented, in which the evolutionary relationship among different planning schemes is analyzed. Based on this framework, a multi-stage planning method of ESOP with consideration of tie-line reconstruction is subsequently proposed. Finally, case studies are conducted on a modified practical distribution network, and the cost–benefit analysis of device and multiple impact factors are given to prove the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a dual-layer grid comprehensive resource optimizing allocation model is proposed, which considers power quality controlling and load optimization scheduling under large-scale application of distributed electric heating. The upper-layer planning aims to minimize the active power loss of the distribution network, the minimum voltage deviation, and the minimum investment cost of the power quality control device. The capacity configuration of the management device and the number and location of the commutation switch configuration were determined. The lower layer is load optimization scheduling, with the minimum number of action switches and the minimum three-phase imbalance as the planning goals, and the decision variable is the state of the commutation switch. By co-simulation through Matlab and OpenDSS, the improved particle swarm algorithm and genetic algorithm are used for multi-objective optimization and the solution. In this way, the capacity configuration of reactive power compensation and active filter, as well as the installation position and switch state of the commutation switch are optimized and managed. Finally, taking the rural low-voltage distribution network in the Tongzhou District as an example, simulations considering the variation in the distributed electric heating penetration rate in the range of 20–80% are carried out. The calculation example results show that the proposed algorithm is effective, can effectively improve the power factor, reduces the harmonic content of the distribution network and the three-phase unbalance, and significantly improves the distribution network voltage. [ABSTRACT FROM AUTHOR]

The integration of soft open point (SOP) effectively improves the flexibility of active distribution networks (ADNs). However, in practical operation, accurate network parameters are difficult to obtain and the operation state changes rapidly with distributed generators (DGs). With the development of information technologies, massive operation data can be acquired in ADNs. How to utilize multisource data has become the key to realize the intelligent operation of ADNs. This article proposes a data-driven operation strategy of SOP based on model-free adaptive control (MFAC). First, considering the inaccurate parameters and frequent change of operation states, a data-driven framework is formulated for the real-time operation of SOP. Then, the operation strategies of multiple SOPs are further improved with interarea coordination. The results of case studies show that driven by the measurement data, the potential benefits of SOPs are explored to adaptively respond to system state changes and improve the operational performance of ADNs. [ABSTRACT FROM AUTHOR]

Aerosol deposition (AD) is a coating technique wherein particles are impacted onto a target substrate at reduced pressures, and supersonic particle impact velocities lead to coating consolidation. The limiting step in AD application is often not supersonic deposition operation, but aerosolization of powder particles with the proper size distribution; the translational impact velocity is strongly size‐dependent. It is demonstrated that by directly synthesizing particles in the gas phase, size‐controlled ceramic particles can be injected into AD systems. This in situ formation step obviates the need for particle aerosolization. Ultrasonic spray pyrolysis (USP) is applied to produce yttria‐stabilized zirconia (YSZ), and USP is directly coupled with AD to produce consolidated, thick, YSZ coatings on metal substrates. USP‐AD yields YSZ coatings on stainless steel and aluminum substrates with porosities <0.20, which grow to thicknesses beyond 100 μm. Aerodynamic particle spectrometry and electron microscopy reveal that the depositing particles are 200 nm–1.2 μm in diameter, though each particle is composed of nanocrystalline YSZ. Supporting computational fluid dynamics calculations demonstrate that the YSZ particle impact speeds are above 300 m s−1. Thermal conductivity measurements demonstrate that USP‐AD coatings have conductivities consistent with those produced from high‐temperature processes. [ABSTRACT FROM AUTHOR]

Summary: In this article, a multi‐stage optimal allocation method for battery energy storage system (BESS) in distribution networks with photovoltaic (PV) system is proposed, which is to obtain its optimal installation location, capacity, power, and investment time. This method takes the time value of money, the PV growth rate, and the load increase during the investment period into consideration. Firstly, genetic algorithm and gravity search algorithm are combined to solve a single‐stage static optimal allocation problem and the BESS installation location and parameters in target year are obtained. Then, the obtained parameters are regarded as constraints and a heuristic multi‐path forward dynamic programming method is used to determine the specific investment time of each equipment. Finally, a case study is used to verify the effectiveness and the economy of the proposed method. The research provides a theoretical basis for the optimal allocation of BESS in distribution networks. [ABSTRACT FROM AUTHOR]

In recent years, distributed energy storage (DES) has experienced rapid growth and has been widely applied in active distribution networks (ADNs). Owing to the close correlation between the characteristics and the application scenarios, DES modeling needs to be parameterized separately for various application demands. In this paper, a parameterized model for optimal DES planning in ADNs is proposed. The typical scenarios for DES planning are generated by the clustering technique, containing the patterns of load demand, wind turbine output and photovoltaic output. Secondly, an optimal planning model of DES considering parameterized characteristics is established, which is essentially a mixed integer non-linear optimization problem. Then, the model is converted to a mixed-integer second-order cone programming model, which can be solved efficiently by available commercial software. Finally, case studies on the modified IEEE 33-node system and IEEE 123-node system verify the efficiency of the proposed method, and the effects of DES planning are validated by two evaluation indexes. [ABSTRACT FROM AUTHOR]

The asymmetric integration of distributed generators (DGs) exacerbates the three-phase unbalanced condition in distribution systems. The serious unbalanced operation causes inefficient utilization of network assets and security risks in the system. Soft open point (SOP) is a flexible power electronic device which can achieve accurate active and reactive power flow control to balance the power flow among phases. This paper proposes an SOPs-based operation strategy for unbalanced active distribution networks. By regulating the operation of SOPs, the strategy can reduce power losses and simultaneously mitigate the three-phase unbalance of the upper-level grid. Semidefinite programming (SDP) relaxation is advocated to convert the original non-convex, nonlinear optimization model into an SDP formulation, which can be efficiently solved to meet the requirement of rapid adjustment. Case studies are conducted on the modified IEEE 33-node and IEEE 123-node distribution system to verify the effectiveness and efficiency of the proposed strategy. [ABSTRACT FROM AUTHOR]

The impact of numerous distributed generators (DGs) coupled with the implementation of virtual inertia on the transient stability of power systems has been studied extensively. Time-domain simulation is the most accurate and reliable approach to evaluate the dynamic behavior of power systems. However, the computational efficiency is restricted by their multi-time-scale property due to the combination of various DGs and synchronous generators. This paper presents a novel projective integration method (PIM) for the efficient transient stability simulation of power systems with high DG penetration. One procedure of the proposed PIM is decomposed into two stages, which adopt mixed explicit-implicit integration methods to achieve both efficiency and numerical stability. Moreover, the stability of the PIM is not affected by its parameter, which is related to the step size. Based on this property, an adaptive parameter scheme is developed based on error estimation to fit the time constants of the system dynamics and further increase the simulation speed. The presented approach is several times faster than the conventional integration methods with a similar level of accuracy. The proposed method is demonstrated using test systems with DGs and virtual synchronous generators, and the performance is verified against MATLAB/Simulink and DIgSILENT PowerFactory. [ABSTRACT FROM PUBLISHER]

The increasing penetration of distributed generators (DGs) exacerbates the risk of voltage violations in active distribution networks (ADNs). The conventional voltage regulation devices limited by the physical constraints are difficult to meet the requirement of real-time voltage and VAR control (VVC) with high precision when DGs fluctuate frequently. However, soft open point (SOP), a flexible power electronic device, can be used as the continuous reactive power source to realize the fast voltage regulation. Considering the cooperation of SOP and multiple regulation devices, this paper proposes a coordinated VVC method based on SOP for ADNs. First, a time-series model of coordinated VVC is developed to minimize operation costs and eliminate voltage violations of ADNs. Then, by applying the linearization and conic relaxation, the original nonconvex mixed-integer nonlinear optimization model is converted into a mixed-integer second-order cone programming model which can be efficiently solved to meet the requirement of voltage regulation rapidity. Case studies are carried out on the IEEE 33-node system and IEEE 123-node system to illustrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

With the increasing penetration of distributed generators, various operational problems, especially severe voltage violation, threaten the secure operation of active distribution networks. To effectively cope with the voltage fluctuations, novel controllable power electronic equipment represented by soft open points has been used in active distribution networks. Meanwhile, the communication has dramatically increased due to the rise of the variety and number of devices within the network. This paper proposes a decentralized voltage control method of soft open points based on voltage-to-power sensitivity. The method reduces the burden of communication, storage, and calculation effectively in a decentralized manner and fulfills the rapid requirements of large-scale active distribution networks. First, the network is divided into several sub-areas; each is under the control of one soft open point at most. The initial strategies of soft open points are adjusted by local voltage-to-power sensitivity and the voltage information within the sub-areas. If some nodal voltages still exceed the expected range after the sub-area autonomy, the operation strategies of soft open points are further improved by inter-area coordination with the alternating direction method of multipliers algorithm. The effectiveness of the proposed decentralized control method is verified on the IEEE 33-node system. [ABSTRACT FROM AUTHOR]