Uncertainty Of Photovoltaic Output Research Articles

Risk assessment of distribution network operation with scale distributed photovoltaics

In the context of large-scale photovoltaic integration into the distribution network, it is of great significance to comprehensively consider the uncertainty of photovoltaic output and load and scientifically and effectively evaluate the operational risks of the distribution network, in order to ensure the reliable and economic operation of the distribution network. At the same time, with the increase of distributed photovoltaic installed capacity, the small interference stability caused by it has an increasing impact on the operation of the distribution network. In this paper, we first establish a probabilistic model of photovoltaic output and load. Then, we propose a risk calculation method based on the non-sequential Monte Carlo method and probabilistic power flow calculation. Then, a risk analysis of large-scale photovoltaic access to the distribution network was carried out. A new small interference stability risk index was constructed to evaluate the risk of system oscillation caused by photovoltaic access. On this basis, the comprehensive risk value is calculated, and the risk levels are divided based on the voltage over-limit risk and power flow over-limit risk. Finally, the feasibility and effectiveness of the risk assessment model are verified through case analysis.

An FCM based weather type classification method considering photovoltaic output and meteorological characteristics and its application in power interval forecasting

AbstractWith the increasing installation of photovoltaic (PV) systems, the impact of their randomness and volatility on power system has become a significant concern. To effectively quantify the uncertainty of PV output, it is crucial to develop reliable PV power interval forecasting technology. However, the complex relationship between PV output and meteorological factors makes it challenging for a single forecasting model to meet the forecasting demand. To solve this problem, this paper proposes a weather classification method that takes into account both PV output and meteorological characteristics. Initially, the relationship between PV output and meteorological factors is analyzed, and weather types are classified using fuzzy c‐means algorithm (FCM). Then, an extreme learning machine (ELM) is employed to establish point forecasting model. By combining kernel density estimation, a PV power generation interval forecasting model is derived. The results demonstrate that the FCM‐ELM model achieves higher forecasting accuracy and narrower interval width compared to traditional ELM models, with accuracy improvement of more than 2%. Additionally, the proposed method outperforms seasonal models with an accuracy improvement of more than 1%. The contribution of this paper includes identifying the limitations of traditional weather classification methods, proposing a novel multi‐model approach for PV interval forecasting.

A Scenario Generation Method for Typical Operations of Power Systems with PV Integration Considering Weather Factors

Under the background of large-scale PV (photovoltaic) integration, generating typical operation scenarios of power systems is of great significance for studying system planning operation and electricity markets. Since the uncertainty of PV output and system load is driven by weather factors to some extent, using PV output, system load, and weather data can allow constructing scenarios more accurately. In this study, we used a TimeGAN (time-series generative adversarial network) based on LSTM (long short-term memory) to generate PV output, system load, and weather data. After classifying the generated data using the k-means algorithm, we associated PV output scenarios and load scenarios using the FP-growth algorithm (an association rule mining algorithm), which effectively generated typical scenarios with weather correlations. In this case study, it can be seen that TimeGAN, unlike other GANs, could capture the temporal features of time-series data and performed better than the other examined GANs. The finally generated typical scenario sets also showed interpretable weather correlations.

Position-Capacity Optimization Method for Distributed Photovoltaic Accessing to Distribution Network Based on PV Load Uncertainty Quantification

As the proportion of photovoltaic grid-connected systems increases year by year, the uncertainty of photovoltaic output and load has an increasingly significant impact on the stable operation of the distribution network. In this paper, a photovoltaic access location-capacity optimization method in distribution lines based on the quantification of photovoltaic-load uncertainty is proposed, and the uncertainty propagation of the PV load model parameters in the distribution network power flow calculation node voltage output model is analyzed. This paper proposed an improved Sobol’s method based on polynomial development to quantitatively evaluate the uncertain impact of photovoltaic load parameters, constructed a photovoltaic access location-capacity evaluation model for distribution lines based on uncertainty quantitative assessment, optimized the access capacity of nodes by using the goal function of line loss minimization, and finally verified the feasibility and effectiveness of the proposed method by improving the IEEE33 node.

Scheduling Strategy of PV-Storage-Integrated EV Charging Stations considering Photovoltaic Output and User Demand Uncertainty

The PV-Storage-Integrated EV charging station is a typical integration method to enhance the on-site consumption of new energy. This paper studies the optimization of the operation of PV-Storage-Integrated charging stations. Firstly, considering the uncertainty of photovoltaic output and user’s charging demand, a photovoltaic output model and a user charging behavior model were constructed. Secondly, the optimal economic operation model of the PV-Storage-Integrated EV charging stations is proposed based on the cost of purchasing power from the distribution network and the life loss cost model of the battery cycle. The constraints such as the charging and discharging power of the battery and the SOC range of the energy storage battery are considered. Finally, optimal scheduling schemes in different typical scenarios are analyzed, and the results show the effectiveness of the proposed optimal scheduling scheme.

Multidispatch for Microgrid including Renewable Energy and Electric Vehicles with Robust Optimization Algorithm

With the deterioration of the environment and the depletion of fossil fuel energy, renewable energy has attracted worldwide attention because of its continuous availability from nature. Despite this continuous availability, the uncertainty of intermittent power is a problem for grid dispatching. This paper reports on a study of the scheduling and optimization of microgrid systems for photovoltaic (PV) power and electric vehicles (EVs). We propose a mathematical model to address the uncertainty of PV output and EV charging behavior, and model scheduling optimization that minimizes the economic and environmental cost of a microgrid system. A semi-infinite dual optimization model is then used to deal with the uncertain variables, which can be solved with a robust optimization algorithm. A numerical case study shows that the security and stability of the solution obtained by robust optimization outperformed that of stochastic optimization.

Optimization of Integrated Energy System Considering Photovoltaic Uncertainty and Multi-Energy Network

The access of photovoltaics can reduce the carbon emissions of the integrated energy system and can also improve the economics of terminal energy supply, but the uncertainty of photovoltaic output also brings greater challenges to the optimal operation of the system. This paper focuses on coordinated optimization for the multiple energy systems in consideration of demand response. Latin hypercube sampling and the K-means algorithm are used to generate acceptable scenarios to deal with the photovoltaic uncertainty. Demand response based on Time-of-Use (TOU) electricity price is employed to realize the peak load shifting, and in consequence to improve the system operation. The optimization objective is to minimize the operational cost, subject to the constraints of electric grids, natural gas grids, and hot water pipeline grids. Due to the nonconvex constraints of these grids, the constraints are relaxed by means of the mixed integer linear programming approach, and the whole problem is established as a mixed integer linear programming model. Case studies show that demand response in each energy system and the coordinated optimization between the multiple energy systems can reduce the operational cost of the whole system. Even though the photovoltaic uncertainty results in a higher operational cost, the system has a more reliable operating point.

Control strategy for battery-supported photovoltaic systems aimed at peak load reduction

The use of photovoltaic (PV) technologies is one of the key means for achieving the balance between operational power demand and generation in net Zero Energy Buildings (nZEBs). However, direct use of PV power on-site is limited due to wide variability and uncertainty of PV output, the temporal mismatch between PV generation and load and other factors. Consequently, in addition to low self-consumption rates, the problem of peak grid load and peak PV feed into the grid persists. Batteries that are coupled to PV units may partially offer the solution to these problems, if operated under an intelligent control strategy. In this paper we proposed a forecast-based control strategy for battery-to-grid interaction aimed at enhancing selfconsumption and at reducing peak load. Python programming environment was used for data processing and algorithm development. Exemplification was made based on the reported hourly energy demand in one office building of 3000 m2 heated floor area located in Trondheim, Norway. Forecasting of electricity load profiles was based on the seasonal autoregressive integral moving average (SARIMA) model. For PV power forecasting, the algorithm communicated with external service – Solcast API. The search method for optimal scheduling of operational time and the extent of charging/discharging was proposed. The results showed that as opposed to conventional battery use, this control strategy allowed to achieve significantly more consistent grid interaction. It offered highly accurate battery scheduling on a day-ahead basis while utilising minimum historical data and computational resources. The algorithm may be beneficial for end-users and grid operators, and thus, it has a high potential for future integration into building energy supply systems.

Renewable Energy Accommodation Capability Evaluation of Power System with Wind Power and Photovoltaic Integration

Abstract This paper proposes a novel approach to evaluate the renewable energy accommodation capacity (REAC) of power system with wind power and photovoltaic (PV) integration. Based on Copula theory, the wind power output model is proposed considering uncertainty and the spatial correlation of wind speed in different wind farms. The PV output model is presented based on solar irradiance and weather influence factor. The solar irradiance depicts the periodicity and correlation of PV output while the new concept of weather influence factor is introduced to reflect the uncertainty of PV output. And the REAC evaluation model considering reliability of power system with wind power and PV integration is proposed with Monte-Carlo method. The proposed method comprehensively considers the characteristics of renewable energy and evaluates power system REAC more accurately. Furthermore, the influences of multiple factors on REAC of power system are analyzed. Results of case studies based on IEEE Case 39 test system and actual historical renewable energy data of northern China illustrate the feasibility of proposed approach.

Electric Vehicles Charging Scheduling Strategy Considering the Uncertainty of Photovoltaic Output

The rapid development of electric vehicles and distributed generation bring new challenges to security and economic operation of the power system, so the collaborative research of the EVs and the distributed generation have important significance in distribution network. Under this background, an EVs charging scheduling strategy considering the uncertainty of photovoltaic(PV) output is proposed. The characteristics of EVs charging are analysed first. A PV output prediction method is proposed with a PV database then. On this basis, an EVs charging scheduling strategy is proposed with the goal to satisfy EVs users’ charging willingness and decrease the power loss in distribution network. The case study proves that the proposed PV output prediction method can predict the PV output accurately and the EVs charging scheduling strategy can reduce the power loss and stabilize the fluctuation of the load in distributed network.