FEATURE Oil & Gas
Driving energy intelligence through distributed energy resources
management Power utilities are at the start of a period of great challenge and great change, with a positive impact of the current advances in connectivity, OT-IT convergence and AI
T
he electricity generation and transmission industry is at the start of an unprecedented period of transformation. Demand is set to
rise exponentially, driven by a move to electric vehicles and away from fossil fuel-based heating and hot water systems in domestic and commercial settings. Whilst traditional generation will have a part in providing base load for the foreseeable future, it is widely accepted that increase in capacity can’t be met by adding more traditional power stations, because: • Traditional power stations are
extremely expensive to build, take a long time to bring into service, and are very unpopular with local residents; • Fossil fuels exasperate global warming, changing climate patterns, and must be signifi cantly reduced, evidenced by initiatives such as the EU’s Green Deal, which aims to cut 55% of CO2
emissions by
2030 and carbon neutrality by 2050; • High-voltage transmission grids
currently have no capacity to transport and distribute the forecast load across long distances.
Hence, the industry is shifting towards
a more decentralised model of local power generation and distribution, based around many smaller, geographically-distributed generation sites that will be driven by renewable energy sources.
New model, new challenges This new model brings some new challenges to those the industry has historically had to address: • Renewable energy sources have, by their very nature, a capacity that is neither fi xed nor entirely predictable. Solar plant outputs are dependent on both the strength of sunlight and the number of daylight hours, which vary between seasons. Wind power capacity depends on wind strength, which also varies wildly. • Both the voltage and frequency of supply
20 October 2022 | Automation
to customers must be controlled within tight limits, irrespective of the load or rate at which it changes. The more generation sites that feed into the grid, the more complex the problem of managing supply voltage and frequency becomes. • A decentralised generation model, with a large number of disparate energy sources, requires much more sophisticated real- time monitoring and control than when generation is based around a single process and site.
• The smaller-scale generation locations are built and operated by diff erent owners, resulting in a variety of equipment and therefore requiring a high level of versatility in the data ingress systems. The fi rst two of these issues will largely be
addressed by the adoption of local storage solutions; i.e., fi lling storage fl exibly when the energy is available to balance load, rather than switching additional generation sites on and off to match demand. This is driving the need for high-capacity battery solutions, adding to the demand already placed by the migration to electric vehicles. There is some synergy here however, with a number of EV manufacturers already adopting a bi-directional (V2H) charging architecture, where the vehicle can act as a power source for the owner’s house to, say, charge during cheaper hours or utility-switchable charging tariff s.
Management through software The third and fourth challenges are being addressed by a new generation of control
software, known as a Distributed Energy Resource Management System (DERMS), coupled with on-site gateways that interface to a diverse range of local equipment, and present the recovered data in a unifi ed and consolidated format to the upstream systems. These gateways are in turn interfaced to an intermediate layer that takes care of the complexities involved in managing, maintaining and operating a large estate of geographically-distributed gateways and then presents curated and validated collected data to the DERMS. When combined, these systems take real- time information from a large number of diverse technologies, generation and storage assets, and monitor, control and aggregate their outputs to provide a virtual power station to a utility company. This brings the added advantage that its endpoints are already geographically widespread, reducing the need for new high-voltage backbone transmission systems. Of course, these new solutions also need new technologies for cost-eff ective operation. Lithium-ion battery capacity for storage solutions, forecast in Europe to rise from 62GWh in 2021 to 664GWh by 2030, is a driving force behind new giga-factories that will heavily depend on high-performance machine-vision and Edge-AI systems, to create the machines used in production and to automate the inspection and quality control processes. Edge-AI will also fi nd its way into the gateways used within the DERMS systems, to reduce the traffi c sent to and from the site, improve overall system responsiveness and proactively detect conditions that may be precursors of performance degradation or failure, enabling intervention to take place before they impact generation capacity.
CONTACT:
Advantech
www.advantech.eu
automationmagazine.co.uk
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