scholarly journals A Learning-Based Bidding Approach for PV-Attached BESS Power Plants

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiang Gao ◽  
Haomin Ma ◽  
Ka Wing Chan ◽  
Shiwei Xia ◽  
Ziqing Zhu
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Large Scale ◽  
Storage System ◽  
Energy Storage System ◽  
Hill Climbing ◽  
Computational Performance ◽  
Proposed Model ◽  
Bid Price ◽  
Battery Energy

Large-scale renewable photovoltaic (PV) and battery energy storage system (BESS) units are promising to be significant electricity suppliers in the future electricity market. A bidding model is proposed for PV-integrated BESS power plants in a pool-based day-ahead (DA) electricity market, in which the uncertainty of PV generation output is considered. In the proposed model, we consider the market clearing process as the external environment, while each agent updates the bid price through the communication with the market environment for its revenue maximization. A multiagent reinforcement learning (MARL) called win-or-learn-fast policy-hill-climbing (WoLF-PHC) is used to explore optimal bid prices without any information of opponents. The case study validates the computational performance of WoLF-PHC in the proposed model, while the bidding strategy of each participated agent is thereafter analyzed.

scholarly journals Efficiency Comparison of DC and AC Coupling Solutions for Large-Scale PV+BESS Power Plants

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4823
Author(s):  
Francesco Lo Franco ◽  
Antonio Morandi ◽  
Pietro Raboni ◽  
Gabriele Grandi
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Storage System ◽  
Energy Storage System ◽  
Operating Conditions ◽  
Frequency Regulation ◽  
Regulation Services ◽  
Primary Power ◽  
Efficiency Comparison ◽  
Battery Energy

In large-scale photovoltaic (PV) power plants, the integration of a battery energy storage system (BESS) permits a more flexible operation, allowing the plant to support grid stability. In hybrid PV+BESS plants, the storage system can be integrated by using different power conversion system (PCS) layouts and different charge–discharge strategies. In the AC-coupling layout, the BESS is connected to the ac-side of the system through an additional inverter. In the DC-coupling layout, the BESS is connected to the dc-side, with or without a dedicated dc–dc converter, and no additional inverter is needed. Referring to a 288 MWp PV plant with a 275 MWh BESS, this paper compares the PCS efficiency between AC- and DC-coupling solutions. The power injected into the grid is obtained considering providing primary power-frequency regulation services. A charging and discharging strategy of the BESS is proposed to ensure cyclic battery energy shifting. The power flows in the different components of the system that are obtained under realistic operating conditions, and total energy losses and annual average efficiency are calculated accordingly. Finally, results show a higher efficiency of DC-coupling compared to the AC-coupling layout.

scholarly journals Turning Base Transceiver Stations into Scalable and Controllable DC Microgrids Based on a Smart Sensing Strategy

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1202
Author(s):  
Miguel Tradacete ◽  
Carlos Santos ◽  
José A. Jiménez ◽  
Fco Javier Rodríguez ◽  
Pedro Martín ◽  
...  
Keyword(s):  
Electricity Market ◽  
Low Cost ◽  
Storage System ◽  
Environmental Parameters ◽  
Weather Conditions ◽  
Energy Storage System ◽  
Electricity Grid ◽  
Dc Microgrids ◽  
Smart Sensing ◽  
Battery Energy

This paper describes a practical approach to the transformation of Base Transceiver Stations (BTSs) into scalable and controllable DC Microgrids in which an energy management system (EMS) is developed to maximize the economic benefit. The EMS strategy focuses on efficiently managing a Battery Energy Storage System (BESS) along with photovoltaic (PV) energy generation, and non-critical load-shedding. The EMS collects data such as real-time energy consumption and generation, and environmental parameters such as temperature, wind speed and irradiance, using a smart sensing strategy whereby measurements can be recorded and computing can be performed both locally and in the cloud. Within the Spanish electricity market and applying a two-tariff pricing, annual savings per installed battery power of 16.8 euros/kW are achieved. The system has the advantage that it can be applied to both new and existing installations, providing a two-way connection to the electricity grid, PV generation, smart measurement systems and the necessary management software. All these functions are integrated in a flexible and low cost HW/SW architecture. Finally, the whole system is validated through real tests carried out on a pilot plant and under different weather conditions.

scholarly journals Battery Storage-Based Frequency Containment Reserves in Large Wind Penetrated Scenarios: A Practical Approach to Sizing

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3065 ◽  
Author(s):  
Monika Sandelic ◽  
Daniel-Ioan Stroe ◽  
Florin Iov
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Power Plants ◽  
Storage System ◽  
Economic Assessment ◽  
Energy Storage System ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Battery Energy ◽  
Large Wind

This paper focuses on the sizing of a battery energy storage system providing frequency containment reserves in a power system with a large wind power penetration level. A three-stage sizing methodology including the different aspect of battery energy storage system performance is proposed. The first stage includes time-domain simulations, investigating battery energy storage system dynamic response and its capability of providing frequency reserves. The second stage involves lifetime investigation. An economic assessment of the battery unit is carried out by performing the last stage. The main outcome of the proposed methodology is to choose the suitable battery energy storage system size for providing frequency containment reserve from augmented wind power plants while fulfilling relevant evaluation criteria imposed for each stage.

scholarly journals Stochastic scheduling of battery energy storage system for large-scale wind power penetration

2019 ◽  
Vol 2019 (18) ◽  
pp. 5028-5032
Author(s):  
Pranda Prasanta Gupta ◽  
Prerna Jain ◽  
Suman Sharma ◽  
Kailash Chand Sharma ◽  
Rohit Bhakar
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Large Scale ◽  
Storage System ◽  
Stochastic Scheduling ◽  
Energy Storage System ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Battery Energy

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 Optimizing Battery Energy Storage System Data in the Presence of Wind Power Plants: A Comparative Study on Evolutionary Algorithms

2020 ◽  
Vol 12 (24) ◽  
pp. 10257
Author(s):  
Ramin Sakipour ◽  
Hamdi Abdi
Keyword(s):  
Energy Storage ◽  
Evolutionary Algorithms ◽  
Wind Power ◽  
Power Plants ◽  
Storage System ◽  
Energy Storage System ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Wind Power Plants ◽  
Battery Energy

This study deals with the optimization of battery energy storage system (BESS) data in terms of significant characteristics of life and efficiency, and their positive impacts on power system efficiency in the presence of wind power plants in a microgrid. To this end, a permanent magnet synchronous generator (PMSG) is used to convert the wind energy by connecting a three-phase dynamic load to the grid. The main novelty of the proposed method is designing a smart backup battery branch to improve the efficiency of the wind farm by maintaining the operating constraints even during the occurrence of harsh faults in the generation section. Additionally, for the first time, the characteristics of the BESS are optimized using nine evolutionary algorithms, including the genetic algorithm (GA), teaching–learning-based optimization (TLBO), particle swarm optimization (PSO), gravitational search algorithm (GSA), artificial bee colony (ABC), differential evolution (DE), grey wolf optimizer (GWO), moth–flame optimization algorithm (MFO), and sine cosine algorithm (SCA), and the results are compared with each other. The simulation results of a case study confirm the robustness of the proposed control strategy for the BESS.

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