Deep learning-based forecasting of the automatic Frequency Reserve Restoration band price in the Iberian electricity market

The replacement of conventional and dispatchable generation technologies by intermittent renewable energy sources increases the need for ancillary services. New agents, such as batteries, may join frequency regulation markets but they require accurate information about future market prices and service demand trends in order to make their participation profitable. This paper proposes and analyzes the accuracy of various deep learning-based models to estimate the secondary reserve marginal band price in the automatic frequency restoration reserves service of the Iberian electricity market. First, a correlation analysis allows determining various subsets of market variables used as model inputs. These subsets include some highly correlated variables together with different combinations of others whose influenced is analyzed. Next, three different neural network techniques are considered: feedforward, convolutional and recurrent networks. For each of them, a random search is performed to obtain the best set of hyperparameters. The analysis of the results shows how the LSTM model returns the best performance metrics (63.22 % of mean absolute scaled error), clearly improving the state-of-the-art in the domain.Funding for open access charge: CRUE-Universitat Jaume

Battery Energy Storage Emulation for Power System Applications

The concept of energy storage for power systems has received increasingly more attention in recent decades, and the growing penetration of renewable energy sources has only escalated demand for it. Energy storage systems are excellent for balancing generation and load, for suppressing power fluctuations, and for providing other ancillary services to the grid. The Hardware Testbed (HTB) is a novel converter-based grid emulator created for studying the needs associated with high renewable penetration, but the system currently lacks a battery storage emulator. Thus, this work documents the development of a battery energy storage system (BESS) emulator for the HTB. The BESS emulator includes internal battery models for Lithium Ion, Lead Acid, and Vanadium redox flow battery technologies. The emulated BESS contains a two-stage power electronics interface using a DC-DC converter and a boost rectifier separated by a DC link. Controllers for active power output, reactive power output, and DC link voltage are designed for the power electronics interface, and application-specific control loops for primary frequency regulation, inertia emulation, and voltage support are also added. The models and control for this emulated BESS are implemented on a digital signal processor that controls one voltage source inverter on the HTB as if it were the BESS’s boost rectifier. Consequently, the voltage source inverter mimics the behavior of a BESS at its point of common coupling with the HTB’s power system. The BESS emulator is simulated and then tested experimentally on the HTB, and all of its control functions demonstrate correct operation. The BESS emulator’s primary frequency regulation and inertia emulation functions nearly eliminate the system frequency swing following a step change in load, and the voltage support keeps the BESS terminal voltage at a safer level following the disturbances. These three support functions are concluded to be capable of simultaneous operation, which allows the BESS emulator to support the HTB’s power system in multiple ways at the same time. In the future, the BESS emulator can be used on the HTB to study how battery storage can be used to support renewables and other dynamic power system needs

Environmental and economic assessments of electric vehicle battery end-of-life business models

Paper I is excluded from the dissertation until it is published.The number of electric vehicles is rapidly and continuously increasing due to the transport sector’s electrification to reduce emissions such as greenhouse gases. Each electric vehicle is powered by a battery that can contain remaining capacity after first use and several potentially valuable materials. The demand for energy storage systems accelerates the need for these batteries. Considering the upcoming volumes of used electric vehicle batteries, a circular economy for batteries is crucial to enhance environmental and economic sustainability. Circular economy business models aim to strategically reduce the use of resources by closing, narrowing, and slowing material loops, enabling economically and environmentally sustainable business. However, the potential environmental benefits of such circular economy efforts are not explicit. The aim of this work is to provide recommendations for global economic and environmental sustainability of used electric vehicles batteries by considering a circular economy. This objective requires an interdisciplinary approach, building on existing research fields and methods within business and engineering sciences. This interdisciplinary approach prevents problem shifting between environmental and economic sustainability performance of the circular business models identified and assessed. In order to address the main thesis aim, four research questions were developed, and four corresponding publications were produced as a result. Paper I explores market opportunities and limitations for used electric vehicle batteries in Norway, a country with a high market share of electric cars in new car sales. The work qualitatively models the used electric vehicle batteries business ecosystem based on interviews with the industrial ecosystem actors. The globally relevant findings from paper I identify realistic end-of-life alternatives for paper II. Paper II identifies and discusses the globally recommended circular business model to enhance a circular economy for batteries from electric vehicles. The Delphi panel viii method enables a battery expert panel to elaborate on a suitable circular business model for the upcoming volumes of used electric vehicle batteries. Paper III assesses the identified circular business model from paper II to discuss how such a business model can be economically viable and realistic. The techno-economic assessment considers multiple scenarios to detect economic factors for circular business model success. Paper IV assesses the identified circular business models from paper II to discuss how such a business model can benefit the climate and natural environment. Life cycle assessment methodology can calculate the environmental impacts of decisions between business models. Life cycle assessment can detect problems shifting between ecological impact categories, such as greenhouse gas emissions and contamination of the natural environment. The research reveals that repurposing electric vehicle batteries in appropriate second-life applications can reduce their environmental impact and extend their useful lifespan. Eventually, the materials must be recycled to the extent possible. This circular business model’s key environmental benefit is the potential reduction in the demand for new batteries, which could help displace primary production and avoid emissions and other environmental impacts from these industrial processes. However, there is a risk this circular business model may be economically unviable. Several factors must be considered and combined to improve profitability and realistic commercial operations, including the state of health, ageing, lifetime of the battery after its first life, price of used batteries, ownership model, location, second-life application, potential grid connection, and electricity profile of the battery system.publishedVersio

Demand Management Storage Project (DMSP) – an application of grid scale battery energy storage systems

Grid scale BESS (battery energy storage system) has been identified as one of the key technologies in the utility network of the future. There are significant benefits associated with their ability to store energy. This study aims to use economic models to evaluate grid scale BESS benefits and to sum them up into value propositions. DMSP project is planning to install one of the largest BESS systems at a 22kV distribution feeder in Australia. According to (Eyer & Corey, 2010) guide, energy storage systems could have 17 electric grid related applications which across 5 categories: electrical supply, ancillary services, grid system, end user/utility customer and renewable integration. Among all the applications, DMSP project focuses on two major applications: using grid scale BESS for energy time-shift and feeder construction deferral applications. In order to quantify the economic feasibility of the DMSP BESS system, studies were done to analyse the distribution system, energy market and BESS system. Two data models had been created to quantify the two BESS applications with the factors such as energy prices, feeder load data and battery parameters. With the data models, methods were found out about how to simulate electrical and economic performance of the battery energy storage system and quantify these performances into market value. The simulation results had been presented and analysed in the document. From the simulation, it concluded that economic feasibility of BESS energy time-shift application is depended on active level of energy market and also the BESS system cost; Feeder construction deferral application can bring significant benefits if the feeder upgrade construction costs are high. Further in the research an optimal battery control scheme was developed using the forward dynamic programming approach. Based on the data models, this scheme provided the optimal battery control strategy to achieve the maximum benefits from BESS application. The research shows that BESS can bring positive benefits for combined energy storage applications. The potentials of using BESS systems in Australian utility network shall be extended specially with the system costs decreased in the future

Technical and Economic Analysis of Battery Energy Storage System – Voltage Management Solution in Power System

This paper proposes a strategy for sizing and optimal operation planning of a centralized Battery Energy Storage System (BESS) which is installed in the distribution power grid to solve voltage violations as the main result of high penetration of solar generation. The strategy is implemented in two steps. The first step is to determine the minimum size of the BESS considering the change of load demand and the solar generation profile over a year. The second step is optimal operation planning of the BESS for a day with cost minimization while satisfying the voltage requirements of the power grid. The efficiency of the method is simulated on a typical low voltage grid of Thailand, using Matlab 2016a and Matpower 6.0 software. In addition, the simulation results also compare the economic efficiency of two popular battery types, Lithium-ion and Lead-Acid batteries

Techno-economic Analysis Of Battery Energy Storage System in Grid-connected Photovoltaic (PV) System

Solar generation is a prominent source of renewable energy and a rooftop Photovoltaic (PV) system has been deployed in shipyard to generate electricity from the highest irradiance, which is in Tuas, West Region of Singapore. This is based on industrial rooftop PV system with bigger capacity of Battery Energy Storage System (BESS) in grid-connected PV system, is able to achieve greater cost saving for electrifying to load. This whole system is then modelled with simulation to demonstrate using discharging characteristics of the BESS under six different connection scenarios. The transient stability of PV with greater capacity of BESS can improve quality of the power flow to load when there is a sudden loss of power from PV. In addition, result shows that power quality will vary if there is more capacity in BESS integrated with PV