Model-free non-invasive health assessment for battery energy storage assets

Increasing penetration of renewable energy generation in the modern power network introduces uncertainty about the energy available to maintain a balance between generation and demand due to its time-fluctuating output that is strongly dependent on the weather. With the development of energy storage technology, there is the potential for this technology to become a key element to help overcome this intermittency in a generation. However, the increasing penetration of battery energy storage within the power network introduces an additional challenge to asset owners on how to monitor and manage battery health. The accurate estimation of the health of this device is crucial in determining its reliability, power-delivering capability and ability to contribute to the operation of the whole power system. Generally, doing this requires invasive measurements or computationally expensive physics-based models, which do not scale up cost-effectively to a fleet of assets. As storage aggregation becomes more commonplace, there is a need for a health metric that will be able to predict battery health based only on the limited information available, eliminating the necessity of installation of extensive telemetry in the system. This work develops a solution to battery health prognostics by providing an alternative, a non-invasive approach to the estimation of battery health that estimates the extent to which a battery asset has been maloperated based only on the battery-operating regime imposed on the device. The model introduced in this work is based on the Hidden Markov Model, which stochastically models the battery limitations imposed by its chemistry as a combination of present and previous sequential charging actions, and articulates the preferred operating regime as a measure of health consequence. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios. The effectiveness of the proposed battery maloperation model as a proxy for actual battery degradation for lithium-ion technology was also tested against lab tested battery degradation data, showing that the proposed health measure in terms of maloperation level reflected that measured in terms of capacity fade. The developed model can support condition monitoring and remaining useful life estimates, but in the wider context could also be used as the policy function in an automated scheduler to utilise assets while optimising their health.Increasing penetration of renewable energy generation in the modern power network introduces uncertainty about the energy available to maintain a balance between generation and demand due to its time-fluctuating output that is strongly dependent on the weather. With the development of energy storage technology, there is the potential for this technology to become a key element to help overcome this intermittency in a generation. However, the increasing penetration of battery energy storage within the power network introduces an additional challenge to asset owners on how to monitor and manage battery health. The accurate estimation of the health of this device is crucial in determining its reliability, power-delivering capability and ability to contribute to the operation of the whole power system. Generally, doing this requires invasive measurements or computationally expensive physics-based models, which do not scale up cost-effectively to a fleet of assets. As storage aggregation becomes more commonplace, there is a need for a health metric that will be able to predict battery health based only on the limited information available, eliminating the necessity of installation of extensive telemetry in the system. This work develops a solution to battery health prognostics by providing an alternative, a non-invasive approach to the estimation of battery health that estimates the extent to which a battery asset has been maloperated based only on the battery-operating regime imposed on the device. The model introduced in this work is based on the Hidden Markov Model, which stochastically models the battery limitations imposed by its chemistry as a combination of present and previous sequential charging actions, and articulates the preferred operating regime as a measure of health consequence. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios. The effectiveness of the proposed battery maloperation model as a proxy for actual battery degradation for lithium-ion technology was also tested against lab tested battery degradation data, showing that the proposed health measure in terms of maloperation level reflected that measured in terms of capacity fade. The developed model can support condition monitoring and remaining useful life estimates, but in the wider context could also be used as the policy function in an automated scheduler to utilise assets while optimising their health

Energy storage day-ahead scheduling to reduce grid energy export and increase self-consumption for micro-grid and small power park applications

Developments in energy storage technology will start to play a prominent role in overcoming the problems of generation intermittency by providing the ability to shift demand to times when generation is available. However, exploiting the potential of this technology requires the design of an optimal charging and discharging schedule to allow its integration with the energy network that brings maximum advantage to both the system and the user. This paper introduces a mathematical model for generation and demand forecasting with energy storage scheduling that can be used for micro-grid and small power park applications. The proposed solution models the physical limitations associated with the energy storage technology used, which will constrain charge and discharge schedules beyond what can be forecast for them. A case study of a community feeder with large PV installations is presented to demonstrate the effectiveness of the model. Day-ahead charge and discharge schedules were produced that increased self-consumption within the system and reduced energy export to the grid. The main contribution of this work is the design of a generic parametrized forecasting and energy storage scheduling tool that will be a platform for further development to specialized storage technology and its potential scalability

Model-free non-invasive health assessment for battery energy storage assets

With the increasing application of battery energy storage in buildings, networks and transportation, an emerging challenge to overall system resilience is in understanding the constituent asset health. Current battery energy storage considerations focus on adhering to the technical specification of the service in the short term, rather than the long-term consequences to battery health. However, accurately determining battery health generally requires invasive measurements or computationally expensive physics-based models which do not scale up to a fleet of assets cost-effectively. This paper alternatively proposes capturing cumulative maloperation through a physics model-free proxy for cell health, articulated via the strong influence misuse has on the internal chemical state. A Hidden Markov Chain approach is used to automatically recognize violations of chemistry specific usage preferences from sequences of observed charging actions. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios

Analysis and evaluation of the photovoltaic market in Poland and the Baltic States

The household, industrial, and service sectors in Poland and the Baltic States have been facing ever-higher bills for their electricity consumption at a time when a number of them have been hit hard financially by the pandemic. Rising inflation, the border crisis with its set of restrictions, or the spread of the fourth wave of the COVID-19 coronavirus pandemic, is causing strong concerns in the social and economic sphere, with significant increases in electricity prices. Many countries are implementing measures to reduce the adverse effects of rising electricity prices in response to this complex situation. The main orientation is towards obtaining energy from renewable sources, such as the sun. The current situation in the energy market determines the price per 1 KW. Among the countries under study, the price of electricity has increased the most in Poland. On the other hand, the development of the photovoltaic segment in Poland is undergoing a strong, upward trend. The above inspired the authors to explore the energy market situation in Poland and the Baltic States in the current economic conditions, along with an analysis of its development potential in light of the coronavirus pandemic. The main research problem of this study is an attempt to answer the question of what should be changed in the development of the renewable energy market in Poland, with particular emphasis on photovoltaics, to accelerate the process of reducing CO2 emissions, leading to a reduction in dramatically rising electricity prices. Which solutions implemented in the Baltic countries can inspire strengthening Poland’s energy market development? Keywords: energy market; renewable energy; electricity prices; photovoltaics; renewable energy