Solar Irradiance-based Load Controller for Frequency Regulation under High Intermittency Photovoltaic System

Real-time validation of RoCoF – Enhancing advanced coordinated control strategy for voltage unbalance mitigation implementing demand side management

The existing passive methods for islanding detection are mainly based on the detection of voltage and frequency deviation after islanding, using protections such as voltage vector shift (VVS) and rate of change of frequency (ROCOF). Although there are reported issues with these passive methods such as inherent non-detection zones and nuisance trips, utilities prefer the passive methods due to the low cost and simplicity of deployment. In this paper, one composite passive islanding detection method is presented. It tracks the voltage phase angle, the system frequency, and ROCOF every power cycle. If three phase voltage vectors shift in the same direction and the rotated angle values are balanced, the calculation of the accumulated phase angle drift (PAD) will be initiated. This calculation continues until the ROCOF measurement is below the ROCOF setting threshold. If the accumulated phase angle drift reaches the set angle threshold, the condition for islanding is claimed. The performance of this composite method is verified under different scenarios based on Matlab Simscape multidomain physical systems and practical waveforms recorded from sites. Although there are still non-detection zones, this composite PAD solution has better sensitivity than existing VVS and ROCOF methods and is stable under external system faults.

Submaximal eccentric (ECC) cycling exercise is commonly used in research studies. No previous study has specified the required time naïve participants take to familiarize with submaximal ECC cycling. Therefore, we designed this study to determine whether critical indicators of cycling reliability and variability stabilize during 15 min of submaximal, semi-recumbent ECC cycling (ECC cycling). Twenty-two participants, aged between 18–51 years, volunteered to complete a single experimental session. Each participant completed three peak eccentric torque protocol (PETP) tests, nine countermovement jumps and 15 min of submaximal (i.e., 10% peak power output produced during the PETP tests) ECC cycling. Muscle activation patterns were recorded from six muscles (rectus femoris, RF; vastus lateralis, VL; vastus medialis, VM; soleus, SOL; medial gastrocnemius, GM; tibialis anterior, TA), during prescribed-intensity ECC cycling, using electromyography (EMG). Minute-to-minute changes in the reliability and variability of EMG patterns were examined using intra-class correlation coefficient (ICC) and variance ratios (VR). Differences between target and actual power output were also used as an indicator of familiarization. Activation patterns for 4/6 muscles (RF, VL, VM and GM) became more consistent over the session, the RF, VL and VM increasing from moderate (ICC = 0.5–0.75) to good (ICC = 0.75–0.9) reliability by the 11th minute of cycling and the GM good reliability from the 1st minute (ICC = 0.79, ICC range = 0.70–0.88). Low variability (VR ≤ 0.40) was maintained for VL, VM and GM from the 8th, 8th and 1st minutes, respectively. We also observed a significant decrease in the difference between actual and target power output (χ214 = 30.895, p = 0.006, W = 0.105), expressed primarily between the 2nd and 3rd minute of cycling (Z = -2.677, p = 0.007). Indicators of familiarization during ECC cycling, including deviations from target power output levels and the reliability and variability of muscle activation patterns stabilized within 15 min of cycling. Based upon this data, it would be reasonable for future studies to allocate ∼ 15 min to familiarize naïve participants with a submaximal ECC cycling protocol.

Short-term frequency instability is one of the major challenges that arise due to large-scale penetration of converter interfaced renewable energy sources (RESs) in isolated power systems. The short-term frequency instability is associated with high values of rate of change of frequency (RoCoF) and larger frequency excursions. In this paper, a frequency constrained energy management algorithm is proposed. The proposed algorithm manages the power output of RESs, energy storage system, and electric vehicles (EVs) in such a way that the system keeps nonsynchronous power reserve for providing virtual inertia support to avoid high values of RoCoF and frequency excursions. The non-synchronous power reserve is readily available and when deployed can limit the RoCoF as per the required standard. The proposed management algorithm selects the non-synchronous power reserve from different resources, i.e., RES (solar PV, wind turbine), energy storage, and EV, depending on the availability and system loading conditions. Simulation results show the effectiveness of the proposed algorithm.

Security constrained generation scheduling for grids incorporating wind, photovoltaic and thermal power

The expanding use of photovoltaic (PV) systems as an alternative green source for electricity presents many challenges, one of which is the timely diagnosis of faults to maintain the quality and high productivity of such systems. In recent years, various studies have been conducted on the fault diagnosis of PV systems. However, very few instances of fault diagnostic techniques could be implemented on integrated circuits, and these techniques require costly and complex hardware. This work presents a novel and effective, yet small and implementable, fault diagnosis algorithm based on an artificial intelligent nonlinear autoregressive exogenous (NARX) neural network and Sugeno fuzzy inference. The algorithm uses Sugeno fuzzy inference to isolate and classify faults that may occur in a PV system. The fuzzy inference requires the actual sensed PV system output power, the predicted PV system output power, and the sensed surrounding conditions. An artificial intelligent NARX-based neural network is used to obtain the predicted PV system output power. The actual output power of the PV system and the surrounding conditions are obtained in real-time using sensors. The algorithm is proven to be implementable on a low-cost microcontroller. The obtained results indicate that the fault diagnosis algorithm can detect multiple faults such as open and short circuit degradation, faulty maximum power point tracking (MPPT), and conditions of partial shading (PS) that may affect the PV system. Moreover, radiation and temperature, among other non-linear associations of patterns between predictors, can be captured by the proposed algorithm to determine the accurate point of the maximum power for the PV system.

Based on the nonlinear dynamic modeling of photovoltaic power generation system output time sequences, parsing the kinetic characteristics of the output power of photovoltaic power generation system, the chaos characteristics of the photovoltaic power generation system output power is put forward. According to the phase space reconstruction theory, adding weight first order local prediction method is used for photovoltaic output power chaotic prediction. The actual measurement data of the output power acquired from the photovoltaic power generation system of a printing plant roof is used to verify the prediction method. Results show that the photovoltaic power generation system output power has chaotic characteristics, and it is feasible that the chaos prediction method apply to photovoltaic output power prediction, the method has a high accuracy for photovoltaic power ultra-short term prediction, it provide a new way for photovoltaic output power prediction.

Accurate photovoltaic (PV) production forecasting is necessary for the optimal integration of this technology into existing power systems and is important for both grid and plant operators. The purpose of this work is to assess the performance of different machine learning models for predicting the power output of PV systems. Specifically, a variety of methods were explored including artificial neural networks (ANNs), support vector regression (SVR) and regression trees (RT), with varied hyper-parameters and features. The output power prediction performance of each model was tested on actual PV production data-sets acquired over a period of a year and compared against an existing persistence model (PM). The comparative analysis between the different optimally devised models showed that the ANN outperformed the other models, achieving the lowest prediction mean absolute percentage error (MAPE) and normalised root mean square error (nRMSE) of 0.6 % and 0.76 %, respectively. The prediction capabilities of the SVR and RT models can be considered equivalent for this case since the nRMSE results were 1.13 % and 1.33 %, respectively. Finally, all the models achieved higher relative improvement over the PM since the skill score (SS) ranged from 86 % to 92 %.

Customer baseline load (CBL) estimation is very important in demand response (DR) program. Due to the increasing installation of distributed photovoltaic system (DPVS), the load patterns of residential customers become more complex and random. The actual load power of the customer is coupled with the DPVS output power, which makes it more difficult to estimate CBL. Since the electricity meter can only measure the net load data, this article proposes a PV-load decoupling approach to improve the CBL estimation accuracy in the presence of DPVS. CBL is the difference between actual load power and DPVS output power, so the CBL estimate is converted into two sub-problems: the estimation of actual load power and the estimation of DPVS output power. First, the actual load power of DR customers is estimated based on the load power of the control group customers. Then, the DPVS output during DR period is obtained based on the DPVS output estimation model. Finally, CBL is estimated based on the actual load power and DPVS output power. In order to verify the effectiveness and feasibility of the approach, two real datasets from Sydney and Austin are used to simulate the CBL estimation. Compared with the net load directly estimating the CBL, the comparison results indicate that the proposed method shows better accuracy performance.

Wind diesel hybrid systems (WDHS) are isolated power systems combining diesel generators with wind turbine generators. In this paper it is presented the modeling and the dynamic simulation of a WDHS composed by a Diesel Generator (DG), a Wind Turbine Generator (WTG), the consumer Load, a Ni-Cd Battery based Energy Storage System (BESS), a Dump Load and a Distributed Control System (DCS). This WDHS is simulated for a negative wind speed step which leads the WDHS to a DG overload where another DG is necessary to supply the system. It is shown how the DCS orders the BESS to supply active power temporary to the system in order to sustain system frequency until other DG gets connected to the isolated grid. Simulation results with graphs for system frequency and voltage and active powers of each component of the WHDS are presented.

Demand side management is an important part of the smart grid for alleviating the uncertainty in renewable energy generation including such as wind power generation and photovoltaic generation. The nuclear accident caused by the 2011 Tohoku Earthquake forced a re-evaluation of the future power generation system in Japan. As a result, demand side management has become critical for future Japanese power systems with its large integration of renewable energy sources, and the evaluation of the impacts of this management is needed for planning purposes. This paper proposes a new energy system model that expands the function of MARKAL (Market Allocation) and considers the characteristics of the total power system in more detail. The relationship between demand side management and the future power generation mix was evaluated using energy system analysis as part of the proposed model. The electric water heaters and heat pump water heaters are conceived as the controllable loads in the demand side management.

Droop control, inertia control, and PD control are three typical frequency response (FR) strategies of grid-tied inverters interfaced renewable energy sources (RES) to restrain the power system frequency indicators, i.e., frequency deviation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta \omega $ </tex-math></inline-formula> ) and the rate of change of frequency (RoCoF). Due to the highly limited inverter capacity, the FR performance in power systems with high RES penetration should be optimized. To this end, this paper first analyzes the impact of high-penetration renewable energy injection on power system frequency stability, and derives the system frequency expressions in the absence of an FR scheme, and in the presence of droop/inertia/PD controls that are representative for various virtual synchronous generator-based FR schemes. Under the same constraint of available FR capacity, control parameter ranges of these FR schemes are obtained, and their optimal conditions for restraining the maxima of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta \omega $ </tex-math></inline-formula> and RoCoF are identified and compared analytically. Comparison conclusions are finally proved by the provided experiment and simulation results.

Performance investigation of linear Fresnel concentrating photovoltaic systems with mini-channel heat sink using R141b/R245fa mixture enhanced by electric field

In microgrids, battery energy storage systems can be used in combination with renewable energy sources as a way to mitigate the adverse effects of the mismatch between renewable energy output and load demand on microgrid operation. To address the configuration of renewable energy generation units and battery energy storage systems in zero-carbon microgrids, the paper proposes a multi-objective optimal configuration method from the perspective of microgrid operation. Minimizing the life cycle cost of the system and minimizing the renewable curtailment are taken as objective functions. Based on actual wind and solar power output and load data, the multi-objective problem is solved by the normalized normal constraint method to obtain the configuration results. The validation study of typical days in three seasons is carried out respectively. Through the analysis of the validation results, it can be found that the proposed method can promote the cooperation between generation equipment and battery energy storage system, which makes the microgrid more economical. In addition, the stability of the model for load changes is proved by the sensitivity analysis.

Fast and accurate estimation of active power imbalance during frequency contingency events is crucial for effective power system monitoring and frequency control. Conventional power imbalance estimation methods are typically based on the swing equation, where both system inertia and the Rate of Change of Frequency (RoCoF) are required for the calculation. However, accurate real-time system inertia level is often unavailable, and measuring RoCoF reliably remains challenging. As a result, the conventional methods are not effective, particularly with the increasing penetration of renewables, where system uncertainties have increased with power quality degraded. This paper explores the feasibility of applying a data-driven approach to address the aforementioned challenges, where the Extreme Gradient Boosting (XGBoost) algorithm is used to train a model that only requires frequency and system operating conditions as inputs for accurate estimation of power imbalance. Through appropriate training, the proposed method does not require the accurate system inertia data and avoids the need for RoCoF measurement. Case studies are presented, which demonstrate that the proposed approach can achieve a highly satisfactory accuracy, providing an effective solution for estimating the power imbalance size during contingency events.