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Abstract Microgrids, denoting small-scale and self-sustaining grids, constitute a pivotal component in future power systems with a high penetration of renewable generators. The inherent uncertainty tied to renewable power generation, typified by photovoltaic and wind turbine systems, necessitates counterbalancing mechanisms. These mechanisms encompass Energy storage systems or conventional thermal fossil-fuel generators imbued with heightened flexibility. Addressing the uncertainty stemming from renewable generators mandates a cost-effective assessment and operational strategy for said compensatory devices. To this end, myriad uncertainty factors warrant scrutiny, conceivably concretized into a unified probability distribution function (PDF) that takes into account their temporal inter-dependencies. Diverse uncertainty factors, characterized by varying marginal distributions and scales, can be assimilated into a multivariate probability distribution through a conversion to normal distributions via rank correlation. However, with the escalation in the number of uncertainty factors embraced within a microgrid context, the endeavour becomes notably intricate when aiming to define conditional probability distributions originating from joint PDFs. This paper presents a method proposing the modelling of net-load forecast error distribution, considering the interplay among uncertainty factors. The approach introduces a data-driven Gaussian process regression technique for training and validating conditional PDFs among these uncertainty factors. Notably, this approach facilitates the transformation of said factors into normal distributions while preserving their inherent marginal characteristics. The resultant conditional density function, as per the proposed methodology, exhibits enhanced suitability for estimating net-load error distribution. Consequently, the conditional density function stemming from this proposed approach demonstrates superior aptitude in approximating the distribution of net load error.

Abstract The increase of renewable energy use in a power generation decreases the flexibility of power supply. The intermittent and variable nature of renewable resources necessitates the use of a transmission system operator to prepare reserve generating power in case of power imbalance. Unfortunately, the generation and reserve margins of renewable energy generators are often insufficient. To ensure a balanced and resilient power system, surplus power generation from renewable resources must provide frequency support with the essential number of synchronous generators being remained in the network. Assessment of the amount of frequency support power from wind farm became available through forecast and real‐time operation planning. However, the exact amount of back‐up power that expected to be provided by individual wind farm can be barely determined due to the uncertainty and variation of renewable resources generation. A stochastic method for assessing the required frequency support operation of specific wind power plants is thus proposed in this paper. The stochastic correlation characteristics between a single wind farm and power system stability are applied in this method. The essential metrics for deloading operation and inertia response are calculated using the stochastic correlation model based on copula function.

Abstract This study considers the optimal reactive power dispatch strategy that minimizes electrical loss while working within the constraints of the internal layout voltage. Wind turbines (WTs) with a power converter system can promptly generate reactive power, and when a reactive power order arrives from the connected grid, an operator can assign a reactive power reference to each wind turbine. Recently, multiple objective functions in use have been based on centralized control, but their flexibility has been determined as inadequate for situations in which the voltage range of the wind turbine is also a factor. The designated constraints of the voltage fluctuation for each section have thus been considered by designing and applying an adaptive method. The main purpose of such a method is to use general dispatch optimization while maintaining the voltage of each connection point within operating range. The included case studies are designed using an electromagnetic transients program that checks both the voltage and loss of the entire wind farm. We tried to identify voltage violations and confirmed the effectiveness of the proposed method. This work thus attempts to confirm whether an objective function can be implemented while also adhering to a certain grid code.

Abstract The increase of distributed Photovoltaic (PV) generators in the power system changes the load demand pattern as some of them are located behind the meter (BtM). As known as BtM PV generators, the impact on netload demand pattern increases the uncertainty of scheduling and balancing of the power system. The distortion of load demand by BtM PV must be decoupled to make an accurate forecast model for the load. In this paper, the impact of BtM PV on stochastic forecast error (SFE) is analyzed and verified how they change the efficiency of microgrid operation. The net load pattern and solar irradiation data are collected and used to decouple BtM PV distortion by using K‐mean clustering and support vector machine regression analysis. Then, the baseload demand and solar irradiation forecast model is built using recurrent neural networks with external input parameters. Using the forecast model, the correlation between distributions of forecast and forecast error is built as a joint probability distribution. The SFE is modeled as conditional forecast error in a form of a probability density function and used for microgrid scheduling optimization. Case studies are based on data acquired from global data assimilation and prediction system (GDAPS) and local transmission system operator (TSO) at Jeju island in Korea.

With the increased usage of different types of renewable energy based on variable output characteristics, power curtailments have become a major problem in grid operation. The advanced controllers in distribution networks are significant in reducing the uncertainties caused by renewable generators. Along with this, at the operational level, the determination of the appropriate amount of curtailment for the stability and economics of the system was taken into account The paper proposes an adaptive curtailment method in order to improve the accuracy of the power flow coming from the distribution network, with the signal created according to the system operator based on the response of the electric vehicle (EV). The method covers the economic loss caused by the curtailment process through the compensation profits. The main determinant reference proposal concerns the sensitivity and ability to increase the stability and economic feasibility of the interconnected power system. The benefits generated by the EV charging are provided to wind turbine operators in the scenario to compensate for excessive constraints. The whole method is applied in the IEEE-69 bus system to verify the effect with MATLAB.

The importance of the auxiliary control of power electronic devices such as HVDC and FACTs is growing owing to the increase in the generation capacity of renewable energy sources. This paper presents a temporary frequency support strategy using the electrostatic energy in voltage-sourced converter-type high-voltage direct current systems. An analytical derivation for selecting the optimal droop constant to improve the frequency nadir was established. The optimal droop constant was derived via system frequency response analysis by using the support termination time. Furthermore, the optimality of the support was computed using numerical equations. Modified outer control loops were implemented in the master controller to utilize the proposed method in voltage-sourced converter control. The effect of the fast frequency support control strategy was verified via a time-domain electromagnetic transient program simulation based on case studies. To examine the practical applicability of the proposed strategy, the parameter robustness was investigated by performing deviated system parameter simulations. The results validated the proposed strategy.

In recent years, the expansion of renewable energy in electric power systems has been increasing at such a rapid pace that it has started affecting frequency stability. Renewable generators connected to the grid produce variable amounts of power, and in most cases have no inherent inertia response (IR) to the system frequency. Therefore, the high penetration of renewable generators in the system results in low inertia and frequency distortion. If renewable generators account for a high proportion of the supply in a power system, the use of energy storage systems (ESSs) with frequency-support algorithms (in the place of synchronous generators) can stabilize the network. The participation of ESSs in frequency support must be organized precisely, so that they are fully devoted to their own purpose. In this paper, the frequency-support parameters of ESSs are calculated for achieving stable frequency response from a network. An estimation and calibration process is conducted during the active power-order change of the ESSs in the substation, and is verified through electromagnetic-transients-including-DC (EMTDC)-based simulations.

Recent development and cost down of PV(Photovoltaic) technology drive change of power system structure. The participation of PV generation is increasing, and the size of each PV farm is getting larger. In order to integrate large PV farms into the main grid, substation for interconnection needs to be sized properly. Unlike substations for load and conventional generators, PV farm substation has an uneven utilization ratio due to characteristics of solar radiation. With proper sizing method for the capacity of the substation can reduce the building cost of facilities. A combination of an energy storage system can further reduce the capacity of the substation. Battery energy storage system (BESS) can shift the peak production of PV during the daytime to midnight. According to market circumstances, BESS can reduce further construction costs by producing profit based on time difference of electric cost. For proper sizing of substation capacity, several factors must be considered including environmental factors, market structure and BESS in the system. In this article, a series of assessment methodology is introduced to calculate the optimized capacity of substation and BESS for PV farm interconnection. The long-term solar radiation data is analyzed for a given site of the PV farm. Based on market structure, the operation of BESS is optimized to make maximum profit during operation. The iterative calculation of each step results in the calculation of the optimized capacity of BESS and substation for given PV farm size.

Renewable generation in power systems has proved to be challenging for system operators owing to the increasing levels of penetration. The operation of power systems currently requires additional flexibility and reserves due to the intermittency and unpredictability of renewable generators. However, it is difficult to precisely predict and control the stochastic nature of renewable sources; nevertheless, its capacity continues to increase. To monitor and control renewable generators efficiently, the entire system needs to be established in a hierarchical order. This study proposed the concept of a substation that is uniquely designed for renewable interconnection. The purpose of this substation is simple: to make the renewable generators dispatchable to operators such that each group of renewable generators is sufficiently stable to be considered as conventional generators. For this purpose, methods for sizing and controlling energy storage system are proposed based on forecasts and error distributions.

A system that combines a wind turbine and a wave generator can share the off-shore platform and therefore mix the advantages of the transmission system construction and the power conversion system. The current hybrid generation system considers output limitation according to the instructions of the transmission system operator (TSO), and controls the profile using wind turbine pitch control. However, the integrated wave generation system utilizing a DC network does not adapt a power limitation scheme due to its mechanical constraints. In this paper, a control plan focusing on the electrical section of wave generators is formed in order to effectively manage the output profile of the hybrid generation system. The plan pays attention to power reserve flexibility for the utility grid using the analysis of the controllable elements. Comparison with the existing system is performed based on real offshore conditions. With the help of power system computer aided design (PSCAD) simulation, the ability of the novel technique is estimated by proposing the real power control based on the reference signal of TSO and the reactive power capacity it produces.

In regard to electric devices, currently designed large-scale distributed generation systems require a precise prediction strategy based on the composition of internal component owing to an environmental fluctuating condition and forecasted power variation. A number of renewable resources, such as solar or marine based energies, are made up of a low voltage direct current (DC) network. In addition to actively considering a power compensation plan, these generation systems have negative effects, which can be induced to a connected power system. When a storage is connected to a DC-based generation system on an inner network along with other generators, a precise state analysis plan should back the utilization process. This paper presents a cooperative operating condition, consisting of the shared DC section, which includes photovoltaic (PVs) and energy storage devices. An active storage management plan with voltage-expectation is introduced and compared via a commercialized electro-magnetic transient simulation tool with designed environmental conditions. Owing to their complexity, the case studies were sequentially advanced by dividing state analysis verification and storage device operation.

In order to solve the problems brought upon by off-shore wind-power plants, it is important to improve fault ride-through capability when an on-shore fault occurs in order to prevent DC overvoltage. In this paper, a coordinated control strategy is implemented for a doubly-fed induction generator (DFIG)-based off-shore wind farm, which connects to on-shore land by a modular multilevel converter (MMC)-based high voltage direct current (HVDC) transmission system during an on-shore fault. The proposed control strategy adjusts the DC voltage of the off-shore converter to ride through fault condition, simultaneously varying off-shore AC frequency. The grid-side converter detects the frequency difference, and the rotor-side converter curtails the output power of the DFIG. The surplus energy will be accumulated at the rotor by accelerating the rotor speed and DC link by rising DC voltage. By the time the fault ends, energy stored in the rotor and energy stored in the DC capacitor will be released to the on-shore side to restore the normal transmission state. Based on the control strategy, the off-shore wind farm will ride through an on-shore fault with minimum rotor stress. To verify the validity of the proposed control strategy, a DFIG-based wind farm connecting to the on-shore side by an MMC HVDC system is simulated by PSCAD with an on-shore Point of Common Coupling side fault scenario.

Various control methods and applications have been introduced to power systems in order to alleviate the uncertainties of relying on renewable sources. This paper, focusing on a reactive power allocation process, discusses the development of a self-modification algorithm and its introduction to an online reference management system. The algorithm is designed to predict unexpected situations occurring in real-time operations, when, due to technical issues, the control system becomes unavailable. A test system has been formed to check the feasibility of the composed method. As the main purpose of the feasibility study is checking availability with a complex solution, case studies are designed to adapt a reactive power allocation method based on loss minimization. When unexpected systemic errors were induced, the proposed modification plan changed order automatically with maintaining previous objective function. The improved effects, caused by realistic responses, are classified according to EMTDC (Electro-Magnetic Transient Design and Control) analysis.