PV Forecast Research Articles

FCM Clustering‐ANFIS‐based PV and wind generation forecasting agent for energy management in a smart microgrid

This paper proposes a PV and wind output power generation forecasting agent for a multi-agent-based energy management system (EMS) in a smart microgrid. The microgrid EMS requires both generation forecast and load forecast to provide effective dispatch strategies. The efficiency of the EMS significantly relies on its forecasting accuracy. Firstly, this paper develops an adaptive neuro-fuzzy inference system (ANFIS)-based forecasting model and then utilise it for the development of wind and PV generation forecasting agent for microgrid energy management. ANFIS adopt the self-learning capability from the neural network and linguistic expression function from fuzzy logic inference and stands at the top of both the technologies in performance. The proposed model has been tested using two data sets, i.e., PV historical data and historical wind data. The fuzzy c means clustering (FCM) with hybrid optimisation algorithm-based ANFIS model shows better forecasting accuracy with both PV and wind forecast, therefore, implemented as PV and wind forecasting agent for microgrid EMS.

Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting

Over the past decade, PV power plants have increasingly contributed to power generation. However, PV power generation widely varies due to environmental factors; thus, the accurate forecasting of PV generation becomes essential. Meanwhile, weather data for environmental factors include many missing values; for example, when we estimated the missing values in the precipitation data of the Korea Meteorological Agency, they amounted to ~16% from 2015–2016, and further, 19% of the weather data were missing for 2017. Such missing values deteriorate the PV power generation prediction performance, and they need to be eliminated by filling in other values. Here, we explore the impact of missing data imputation methods that can be used to replace these missing values. We apply four missing data imputation methods to the training data and test data of the prediction model based on support vector regression. When the k-nearest neighbors method is applied to the test data, the prediction performance yields results closest to those for the original data with no missing values, and the prediction model’s performance is stable even when the missing data rate increases. Therefore, we conclude that the most appropriate missing data imputation for application to PV forecasting is the KNN method.

Robust Stochastic Dynamic Load Dispatch Against Uncertainties

At present, electric power systems face difficulties in system operations due to rapid increase in uncontrollable renewable energy sources (RESs), such as photovoltaic power generations (PVs). Reduction of controllable resources also yields concerns about system reliability issues. This paper focuses on a new dynamic load dispatch method for mitigating the irregularity associated with RES. The developed load dispatch method is to schedule the committed generating units outputs so as to meet required irregular load demand estimation which is frequently updated in real-time operation circumstance. A new algorithm is proposed for unit commitment and stochastic dynamic economic load dispatch as an efficient solution for a day-ahead and a real-time generation schedule (GS), respectively, that fully utilizes the limited resources of a power system under uncertainties. Day-ahead and real-time PV forecasts with the co-relations of PV forecasting errors are effectively treated in the proposed method. Special feature of the proposed method different from the existing works lies in a frequent real-time update of GS by using a fast algorithm to maximize robustness against uncertainties.

Photovoltaic generation forecast for power transmission scheduling: A real case study

The increased penetration of photovoltaic power introduces new challenges for the stability of the electrical grid, both at the local and national level. Many different effects are caused by high solar power injection into the electric grid. Among them, the increased risk of imbalance between the actual and scheduled power transmission is of particular relevance. The consequence is the need to exchange larger amounts of dispatchable power on the balancing energy market.The aim of this work is to analyze and quantify the effects of PV penetration in a target region and to evaluate the energy and economic benefits of using day-ahead PV forecast for power transmission scheduling.For this purpose, we developed several data-driven methods for transmission scheduling that include day-ahead PV power forecasts. We compared the resulting operational imbalances from these new models against two reference models currently used by the local grid operators.In the case of no PV generation in the target area, the more accurate reference model leads to an imbalance of 3.6% of the peak power transmission while more accurate data-driven method reduces the imbalance to 3.2%. When the distributed PV capacity is not zero, the imbalance of the reference model grows from 5.15% (at the current penetration of 7%) to 9.8% (at the maximum planned regional penetration of 45%). When we apply the new scheduling model, imbalances are reduced to respectively 3.5% and 5.8% at 7% and 45% of penetration.Since in Italy the costs of imbalances resulting from distributed PV are borne by ratepayers, these costs are estimated to be respectively 2.3% and 15% of the average electricity bill at 7% and 45% penetration if the reference scheduling is used. When applying the new model these costs are respectively reduced to 1.2% and 8.5%.

Day-ahead thermal and renewable power generation scheduling considering uncertainty

Abstract This paper proposes a multi-objective optimization (MOO) based optimal day-ahead scheduling of thermal and renewable (wind and solar photovoltaic) power generation problem considering the intermittent/uncertain nature of renewable energy sources, load demands and thermal generators. System operating cost (i.e., cost of thermal, wind, solar PV and battery powers), reliability and emission cost are considered to be optimized simultaneously. The uncertainties due to generator outages, wind, solar PV and load demand forecast errors are incorporated in the proposed optimization problem using Expected Unserved Energy (EUE) and Loss Of Load Probability (LOLP) reliability indices. In the proposed approach, the amount of spinning reserves (SRs) required are scheduled based on the desired level of system reliability. The proposed multi-objective optimization problem is solved using NSGA-II algorithm. Different case studies are performed considering two or three different objective functions that may be selected by the system operator (SO) based on his/her preference. The simulation results obtained on a sample test system validate the benefits of solving the hybrid power system scheduling problem as a transparent and realistic MOO problem considering the uncertainty.

Impact of probabilistic small-scale photovoltaic generation forecast on energy management systems

Demand-side Management (DSM) algorithms are exposed to several uncertainties due to their dependency on renewable energy generation forecasts. On the large scale, generation and load forecasts can be relatively accurate, yet on the residential scale, forecasting errors increase due to higher uncertainties. One potential solution is to incorporate a probabilistic PV forecast into an optimal DSM algorithm instead of the existing deterministic PV forecasting algorithms. Hence, in this contribution, a numerical analysis that compares the potential of using a probabilistic PV forecast instead of the conventional deterministic algorithms in a DSM algorithm, is presented. Results show that under different household energy system configurations, the DSM algorithm with the probabilistic PV generation forecast leads to an increase in self-sufficiency and self-consumption by 24.2% and 17.7%, respectively, compared to the conventional deterministic algorithms. These results indicate that probabilistic PV forecasting algorithms may indeed have a higher potential compared to the conventional deterministic ones.

Development of a Robust EMS against Large Amount of PV Installation

Abstract At present, electric power systems face difficulties in system operations due to rapid increase in uncontrollable renewable energy sources (RESs), such as photovoltaic power generations (PVs). Reduction of controllable resources also yields concerns about system reliability issues. This paper focuses on a new dynamic load dispatch method for mitigating the irregularity associated with RES. The developed load dispatch method is to schedule the committed generating unit’s outputs so as to meet required irregular load demand estimation which is frequently updated in real-time operation circumstance. A new algorithm is proposed for unit commitment and stochastic dynamic economic load dispatch as an efficient solution for a day-ahead and a real-time generation schedule (GS), respectively, that fully utilizes the limited resources of a power system under uncertainties. Day-ahead and real-time PV forecasts with the co-relations of PV forecasting errors are effectively treated in the proposed method. Special feature of the proposed method different from the existing works lies in a frequent real-time update of GS by using a fast algorithm to maximize robustness against uncertainties.

美国光伏市场调研及前景研究

美国光伏市场调研及前景研究

Optimal power flow with renewable energy resources including storage

The incorporation of renewable energy resources (RERs) into electrical grid is very challenging problem due to their intermittent nature. This paper solves an optimal power flow (OPF) considering wind–solar–storage hybrid generation system. The primary components of the hybrid power system include conventional thermal generators, wind farms and solar photovoltaic modules with batteries. The main critical problem in operating the wind farm or solar PV plant is that these RERs cannot be scheduled in the same manner as conventional generators, because they involve climate factors such as wind velocity and solar irradiation. This paper proposes a new strategy for the optimal power flow problem taking into account the impact of uncertainties in wind, solar PV and load forecasts. The simulation results for IEEE 30 bus system with genetic algorithm (GA) and two-point estimate method have been obtained to test the effectiveness of the proposed optimal power flow strategy. Results for a sample system with GA and two-point estimate OPF, and GA and Monte Carlo simulation have been obtained to ascertain effectiveness of the proposed method.

BESS 기반 마이크로그리드 운영전략

Small islands are quite adequate places where microgrid system with renewable sources can replace diesel engines as operation costs of diesel engine in most small islands are very high. To get the large amount of renewable energy, the microgrid system has very large capacity of renewable sources. The system with large capacity of renewable sources can meet the case when supplied energy is greater than the load and the fluctuation of source output is very large. The battery energy storage system can be a solution to stabilize the system with large capacity of renewable sources. In this case, BESS can be utilized as a master source for the synchronous operation of all sources including diesel engine, wind turbine and PV. The diesel generators can be used as a backup in case the BESS SOC goes below a certain level. In this paper, we suggest a novel unit commitment of diesel generators and operation schedule of pump for water supply service with the information of wind forecast, PV forecast, and load forecast. The proposed methods has been implemented and tested at the test bed in Gasa-Island.

Forecasting the PV Panel Operating Conditions Using the Design of Experiments Method

The aim of this study is to show how the Design of Experiments (DoE) Method can be put into use as a practical method to model an operating photovoltaic PV generator. Experiments were carried out and the whole current- voltage characteristics (I-V) were drawn. A mathematical model describing the variations of the Open-circuit voltage (Voc) and Short-circuit current (Isc) versus solar irradiance level and temperature was obtained with the DoE Method. This model is a predictive model. With the help of the HIDE software we can simulate the PV panel behavior and forecast, in real time, the variations of Voc and Isc anywhere in the surveyed experimental domain.The basic concepts of the DoE Method that makes it possible to properly define and establish the model are presented. Then a complete analysis of measurements is performed in order to interpret the results and optimize the method.