scholarly journals Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1249 ◽  
Author(s):  
Kuk Bae ◽  
Han Jang ◽  
Bang Jung ◽  
Dan Sung
Keyword(s):  
Machine Learning ◽  
Energy Storage ◽  
Output Power ◽  
Storage System ◽  
Weather Conditions ◽  
Energy Storage System ◽  
Energy Markets ◽  
Prediction Errors ◽  
Power Prediction ◽  
Power Trading

Photovoltaic (PV) output power inherently exhibits an intermittent property depending on the variation of weather conditions. Since PV power producers may be charged to large penalties in forthcoming energy markets due to the uncertainty of PV power generation, they need a more accurate PV power prediction scheme in energy market operation. In this paper, we characterize the effect of PV power prediction errors on energy storage system (ESS)-based PV power trading in energy markets. First, we analyze the prediction accuracy of two machine learning (ML) schemes for the PV output power and estimate their error distributions. We propose an efficient ESS management scheme for charging and discharging operation of ESS in order to reduce the deviations between the day-ahead (DA) and real-time (RT) dispatch in energy markets. In addition, we estimate the capacity of ESSs, which can absorb the prediction errors and then compare the PV power producer’s profit according to ML-based prediction schemes with/without ESS. In case of ML-based prediction schemes with ESS, the ANN and SVM schemes yield a decrease in the deviation penalty by up to 87% and 74%, respectively, compared with the profit of those schemes without ESS.

scholarly journals Modelling and region stability analysis of wind turbines with battery energy storage system based on switched system with multi-equilibriums

2019 ◽  
Vol 41 (6) ◽  
pp. 1519-1527 ◽  
Author(s):  
Xiaokun Dai ◽  
Yang Song ◽  
Taicheng Yang
Keyword(s):  
Energy Storage ◽  
Wind Turbine ◽  
Output Power ◽  
Storage System ◽  
Energy Storage System ◽  
Stable Region ◽  
Switched Linear System ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Battery Energy

This paper deals with the modelling and control for wind turbine combined with a battery energy storage system (WT/BESS). A proportional-integral (PI) controller of pitch angle is applied to adjust the output power of WT, and a method for battery scheduling is presented for maintaining the state of charging (SOC) of BESS. When the battery level is below the lower limit, we increase the expected output power of wind turbine through raising the operation point to charge the battery. Considering the effect of charging/discharging, a switched linear system model with two equilibriums is presented firstly for such WT/BESS system. The region stability is analyzed and an approach for estimating the corresponding stable region is also given. The effectiveness of the proposed results is demonstrated by a numerical example.

scholarly journals Forecast Error Sensitivity Analysis for Bidding in Electricity Markets with a Hybrid Renewable Plant Using a Battery Energy Storage System

2020 ◽  
Vol 12 (9) ◽  
pp. 3577 ◽  
Author(s):  
Jon Martinez-Rico ◽  
Ekaitz Zulueta ◽  
Unai Fernandez-Gamiz ◽  
Ismael Ruiz de Argandoña ◽  
Mikel Armendia
Keyword(s):  
Sensitivity Analysis ◽  
Energy Storage ◽  
Electricity Markets ◽  
Electricity Market ◽  
Forecast Error ◽  
Storage System ◽  
Energy Storage System ◽  
Prediction Errors ◽  
Electricity Grid ◽  
Battery Energy

Deep integration of renewable energies into the electricity grid is restricted by the problems related to their intermittent and uncertain nature. These problems affect both system operators and renewable power plant owners since, due to the electricity market rules, plants need to report their production some hours in advance and are, hence, exposed to possible penalties associated with unfulfillment of energy production. In this context, energy storage systems appear as a promising solution to reduce the stochastic nature of renewable sources. Furthermore, batteries can also be used for performing energy arbitrage, which consists in shifting energy and selling it at higher price hours. In this paper, a bidding optimization algorithm is used for enhancing profitability and minimizing the battery loss of value. The algorithm considers the participation in both day-ahead and intraday markets, and a sensitivity analysis is conducted to check the profitability variation related to prediction uncertainty. The obtained results highlight the importance of bidding in intraday markets to compensate the prediction errors and show that, for the Iberian Electricity Market, the uncertainty does not significantly affect the final benefits.

scholarly journals Multi-Objective Sizing of Hybrid Energy Storage System for Large-Scale Photovoltaic Power Generation System

2019 ◽  
Vol 11 (19) ◽  
pp. 5441 ◽  
Author(s):  
Chao Ma ◽  
Sen Dong ◽  
Jijian Lian ◽  
Xiulan Pang
Keyword(s):  
Power Generation ◽  
Energy Storage ◽  
Large Scale ◽  
Yellow River ◽  
Storage System ◽  
Weather Conditions ◽  
Energy Storage System ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Power Stations

Hybrid energy storage systems (HESS) are an effective way to improve the output stability for a large-scale photovoltaic (PV) power generation systems. This paper presents a sizing method for HESS-equipped large-scale centralized PV power stations. The method consists of two parts: determining the power capacity by a statistical method considering the effects of multiple weather conditions and calculating the optimal energy capacity by employing a mathematical model. The method fully considers the characteristics of PV output and multiple kinds of energy storage combinations. Additionally, a pre-storage strategy that can further improve stability of output is proposed. All of the above methods were verified through a case study application to an 850 MW centralized PV power station in the upstream of the Yellow river. The optimal hybrid energy storage combination and its optimization results were obtained by this method. The results show that the optimal capacity configuration can significantly improve the stability of PV output and the pre-storage strategy can further improve the target output satisfaction rate by 8.28%.

scholarly journals Experimental Verification of a Battery Energy Storage System for Integration with Photovoltaic Generators

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Rajkiran Singh ◽  
Seyedfoad Taghizadeh ◽  
Nadia M. L. Tan ◽  
Saad Mekhilef
Keyword(s):  
Energy Storage ◽  
Output Power ◽  
Experimental Verification ◽  
Storage System ◽  
Energy Storage System ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Battery Energy ◽  
Photovoltaic Generators ◽  
Pv Generator

This paper presents the experimental verification of a 2 kW battery energy storage system (BESS). The BESS comprises a full-bridge bidirectional isolated dc-dc converter and a PWM converter that is intended for integration with a photovoltaic (PV) generator, resulting in leveling of the intermittent output power from the PV generator at the utility side. A phase-shift controller is also employed to manage the charging and discharging operations of the BESS based on PV output power and battery voltage. Moreover, a current controller that uses the d-q synchronous reference frame is proposed to regulate the dc voltage at the high-voltage side (HVS) to ensure that the voltage ratio of the HVS with low-voltage side (LVS) is equivalent to the transformer turns ratio. The proposed controllers allow fast response to changes in real power requirements and results in unity power factor current injection at the utility side. In addition, the efficient active power injection is achieved as the switching losses are minimized. The peak efficiency of the bidirectional isolated dc-dc converter is measured up to 95.4% during battery charging and 95.1% for battery discharging.

scholarly journals Solar Photovoltaic Panels Combined with Energy Storage in a Residential Building: An Economic Analysis

2018 ◽  
Vol 10 (9) ◽  
pp. 3117 ◽  
Author(s):  
Federica Cucchiella ◽  
Idiano D’Adamo ◽  
Massimo Gastaldi ◽  
Vincenzo Stornelli
Keyword(s):  
Energy Storage ◽  
Net Present Value ◽  
Storage System ◽  
Residential Building ◽  
Weather Conditions ◽  
Energy Storage System ◽  
Economic Feasibility ◽  
Pv System ◽  
Pv Systems ◽  
Profitability Analysis

Renewable energy is a wide topic in environmental engineering and management science. Photovoltaic (PV) power has had great interest and growth in recent years. The energy produced by the PV system is intermittent and it depends on the weather conditions, presenting lower levels of production than other renewable resources (RESs). The economic feasibility of PV systems is linked typically to the share of self-consumption in a developed market and consequently, energy storage system (ESS) can be a solution to increase this share. This paper proposes an economic feasibility of residential lead-acid ESS combined with PV panels and the assumptions at which these systems become economically viable. The profitability analysis is conducted on the base of the Discounted Cash Flow (DCF) method and the index used is Net Present Value (NPV). The analysis evaluates several scenarios concerning a 3-kW plant located in a residential building in a PV developed market (Italy). It is determined by combinations of the following critical variables: levels of insolation, electricity purchase prices, electricity sales prices, investment costs of PV systems, specific tax deduction of PV systems, size of batteries, investment costs of ESS, lifetime of a battery, increases of self-consumption following the adoption of an ESS, and subsidies of ESS. Results show that the increase of the share of self-consumption is the main critical variable and consequently, the break-even point (BEP) analysis defines the case-studies in which the profitability is verified.

scholarly journals Learning Spatiotemporal Dynamics in Wholesale Energy Markets with Dynamic Mode Decomposition

2020 ◽  
Author(s):  
Clay T. Elmore ◽  
Alexander Dowling
Keyword(s):  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Markets ◽  
Dynamic Mode Decomposition ◽  
Machine Learning Techniques ◽  
Dynamic Mode ◽  
Forecasting Methods ◽  
Mode Decomposition ◽  
Storage Technologies

Energy markets facilitate the balancing of electricity generation (supply) and demand while ensuring non-discriminatory access. Understanding energy market dynamics is essential to improving grid efficiency and resilience and optimizing the development of new energy conversion and storage technologies. Accurate energy price forecasts are essential for many energy storage technologies to be profitable from price arbitrage. In this paper, we apply the novel spatial-temporal dimensionality reduction method of Dynamic Mode Decomposition (DMD) to forecast 6587 locational marginal prices in the California Independent System Operator (CAISO) on the Day-Ahead Market (DAM). DMD is a promising equation-free modeling technique in systems with inherent periodic tendencies in time such as financial markets and fluid dynamics. Yet we show, for the first time, that DMD cannot reliably forecast day-ahead energy prices due to the so-called standing wave problem. Instead, we find Augmented DMD (ADMD) overcomes these limitations is a fast and accurate price forecaster. We benchmark DMD, ADMD, and backcasting forecasting methods for optimal price arbitrage with an energy storage system. We find, using ADMD, a market-connected energy storage system can capture up to 92% of allowable revenues in rolling horizon simulations. Lastly, we show ADMD is up to 1000-times faster than time-series forecasting methods (i.e., ARIMA) which requires orders of magnitude less data than deep/machine learning techniques.

Output Power Leveling of Wind Turbine Generator by EDLC Energy Storage System

2004 ◽  
Vol 124 (8) ◽  
pp. 1059-1066 ◽  
Author(s):  
Tatsuto Kinjyo ◽  
Tomonobu Senjyu ◽  
Katsumi Uezato ◽  
Hideki Fujita
Keyword(s):  
Energy Storage ◽  
Wind Turbine ◽  
Output Power ◽  
Storage System ◽  
Energy Storage System ◽  
Turbine Generator ◽  
Wind Turbine Generator
Sign in / Sign up

Export Citation Format

Share Document