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 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 Implementation of Optimal Scheduling Algorithm for Multi-Functional Battery Energy Storage System

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1339 ◽  
Author(s):  
Hee-Jun Cha ◽  
Sung-Eun Lee ◽  
Dongjun Won
Keyword(s):  
Energy Storage ◽  
Electricity Market ◽  
Distribution System ◽  
Wind Farm ◽  
Storage System ◽  
Energy Storage System ◽  
Optimal Scheduling ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Battery Energy

Energy storage system (ESS) can play a positive role in the power system due to its ability to store, charge and discharge energy. Additionally, it can be installed in various capacities, so it can be used in the transmission and distribution system and even at home. In this paper, the proposed algorithm for economic optimal scheduling of ESS linked to transmission systems in the Korean electricity market is proposed and incorporated into the BESS (battery energy storage system) demonstration test center. The proposed algorithm considers the energy arbitrage operation through SMP (system marginal price) and operation considering the REC (renewable energy certification) weight of the connected wind farm and frequency regulation service. In addition, the proposed algorithm was developed so that the SOC (state-of-charge) of the ESS could be separated into two virtual SOCs to participate in different markets and generate revenue. The proposed algorithm was simulated and verified through Matlab and loaded into the demonstration system using the Matlab “Runtime” function.

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 Modelling and Simulation of a Hybrid Renewable/Battery System Powering a Cathodic Protection Unit

2021 ◽  
pp. 203-208
Author(s):  
Cyncol A. Sibiya ◽  
◽  
Bubele P. Numbi ◽  
Kanzumba Kusakana
Keyword(s):  
Hybrid System ◽  
Cathodic Protection ◽  
Storage System ◽  
Synchronous Generator ◽  
Weather Conditions ◽  
Energy System ◽  
Energy Storage System ◽  
Solar Pv ◽  
Hybrid Energy ◽  
Battery Energy

In this paper, the performance of the proposed off-grid wind-solar PV hybrid system powering the cathodic protection unit is simulated and analyzed using MATLAB/SIMULINK. Furthermore, the performance simulation for the battery energy storage system with PV-wind hybrid energy system under variable solar irradiance and wind speed respectively is also conducted. The hybrid system consists of a wind turbine which uses a permanent magnet synchronous generator driven directly from the turbine, a PV array and a battery bank. The simulated results reflect that the designed hybrid system of such capacity can adequately supply a cathodic protection unit with no power shortage at different weather conditions.

scholarly journals Integrated Control and Protection Architecture for Islanded PV-Battery DC Microgrids: Design, Analysis and Experimental Verification

2020 ◽  
Vol 10 (24) ◽  
pp. 8847
Author(s):  
Ali Abdali ◽  
Kazem Mazlumi ◽  
Josep M. Guerrero
Keyword(s):  
Fault Detection ◽  
Integrated Control ◽  
Storage System ◽  
Energy Storage System ◽  
Detection Algorithm ◽  
Dc Microgrid ◽  
Dc Microgrids ◽  
Protection Scheme ◽  
Bus Voltage ◽  
Battery Energy

Direct current (dc) microgrids have gained significant interest in research due to dc generation/storage technologies—such as photovoltaics (PV) and batteries—increasing performance and reducing in cost. However, proper protection and control systems are critical in order to make dc microgrids feasible. This paper aims to propose a novel integrated control and protection scheme by using the state-dependent Riccati equation (SDRE) method for PV-battery based islanded dc microgrids. The dc microgrid under study consists of photovoltaic (PV) generation, a battery energy storage system (BESS), a capacitor bank and a dc load. The aims of this study are fast fault detection and voltage control of the dc load bus. To do so, the SDRE observer-controller—a nonlinear mathematical model—is employed to model the operation of the dc microgrid. Simulation results show that the proposed SDRE method is effective for fault detection and robust against external disturbances, resulting in it being capable of controlling the dc load bus voltage during disturbances. Finally, the dc microgrid and its proposed protection scheme are implemented in an experimental testbed prototype to verify the fault detection algorithm feasibility. The experimental results indicate that the SDRE scheme can effectively detect faults in a few milliseconds.

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