The importance of trading to smart energy business models
Expertise in energy trading is vital to the success of smart energy business models, which rely on integrating decentralised generation assets with the wholesale energy market. That presents an opportunity for firms with the right skills, argue Jim Fitzgerald, Jochen Schneider and Matthias Raeck
The enormous growth in intermittent and distributed ‘smart' generation assets is creating new commercial opportunities for European energy traders. Smart grid innovations, such as automatic bidirectional control and intelligent linking, enable the technical integration of large numbers of small-scale, distributed power generation, storage and consumption units. A key success factor for smart energy business models is expertise in energy trading and successfully integrating these distributed generation assets with the wholesale energy market. In addition to trading skills, different business models can help realise additional value by optimising the flexibility of these energy assets.
The issue is paramount because of changes to electricity systems that have traditionally been centralised – a trend that is especially prevalent in Europe. The support of renewable energy sources and combined heat and power (CHP) has resulted in a rapid increase in decentralised electricity generated on distribution networks. In both Germany and the UK, power supply from photovoltaic (PV) systems increased between 2001 and 2011 by a growth factor of over 250, albeit from small and significantly different baselines. Onshore wind and bioenergy have experienced smaller but still substantial growth, driven by developments such as the German Renewable Energy Sources Act (EEG), the UK Renewables Obligation and the use of feed-in tariffs (see table 1).
Major challenges
The growth of decentralised production presents major challenges to the planning and control of transmission and distribution networks. Available network capacity is reaching its limits, and financing the expansion of transmission and distribution grids ahead of demand growth can be difficult. Decentralised renewable power plants earning a fixed feed-in tariff are not strongly linked to wholesale power prices, and are therefore not market integrated. Their proliferation exacerbates the difficulty in controlling transmission and distribution networks. The operation of conventional power plants can also be negatively affected by an oversupply of electricity from wind and PV systems, requiring them to be shut down. The result is that conventional plants end up operating inefficiently and investment in new plants becomes unattractive, despite the need to improve security of supply.
Smart energy business models aim to intelligently link consumption patterns with energy production. Through the use of communication and IT in a two-way exchange, a combined optimisation of distributed generation and consumption can be carried out. The distribution network load can be reduced or postponed and the integration of renewable energy power plants can be improved. Consequently, smart energy business models can make a significant contribution to the success of the transition towards low-carbon energy.
Smart energy is a single concept covering a wide array of elements, which can lead to a range of different business models. The key elements of smart energy can be grouped into five segments (see figure 1). The first is the generation of energy in small, decentralised generating units – examples include PV and CHP – in addition to existing large power plants. Energy can be generated on the site where it is consumed (avoiding both transmission use of system (TUoS1) and distribution use of system (DUoS2) charges), offsite within the same distribution zone (avoiding TUoS but incurring DUoS charges) or offsite on a different distribution zone (incurring both TUoS and DUoS charges). The second is decentralised storage technology, through which the fluctuating production profile of intermittent generators can be balanced with consumption. This entails power being stored and made available when needed. The third is ‘smart homes', which covers a range of technologies including intelligent meters, energy saving and load-shifting devices that increase transparency in residential energy consumption. Increasing transparency includes the energy consumption of specific devices, as well as whether demand occurs during peak or off-peak hours. Transparency in energy consumption is an essential condition for consumption efficiency to be measured.
These three infrastructure elements are linked through a fourth element: smart grids. Smart grids are the basis for an overarching management of power generation, storage and consumption units. The fifth element includes various energy services, such as information and advisory, as well as procurement and contracting.
There are exciting businesses that operate within each of the smart energy segments, such as optimising storage performance and classic savings contracting. The focus of this article, however, is the variety of business models that are possible by combining these pieces. We distinguish between business models that are spatially bounded (local level) and non-spatially bounded (system level).
Virtual utility business models combine multiple decentralised consumption and production units that are non-spatially bounded. This geographical and volumetric aggregation helps reach minimum trading volume levels and unlocks combination portfolio effects. A mix of different production technologies – for example, wind, solar and CHP – in flexible operational modes can dampen the fluctuating components of intermittent generation, stabilising energy supply. Virtual storage business models are similar to these, seeking to use storage technology and load-shifting to optimise revenues against wholesale power and capacity markets.
Demand-side management business models focus on the control of consumption, particularly for large consumers such as industrial firms. Through the combination of control and metering devices enabled by a smart grid, changing the time of peak power consumption can reduce network peak loads. The focus is on combining many decentralised units across multiple distribution networks. The more units and types of consumption that are included across a wider geographic area, the higher the revenue potential.
At a local level, the focus of many strategies is on minimising DUoS charges. Business models are emerging that seek to optimise the configurations of network-linked closed systems. For instance, distributed generation and storage systems create a new business model with the purpose of providing dispatchable levels of power and heat, which can be combined. Typical configurations are combinations of PV and battery systems, or small-scale CHP with thermal storage heaters or oversized boilers. By the use of electrical or thermal storage, production and demand can be optimised to maximise revenues by exporting additional energy during times of peak demand. Energy can be stored during periods of low demand in order to boost production and capacity, in anticipation of prices rising. Additionally, network usage fees can be avoided, and by compensating for forecast errors, balancing costs are minimised.
Energy management services business models target the energy and economic optimisation of individual production and consumption units using intelligent measurement and control technology. An integrated view of electricity, heat and cooling is important to this, as well as structured procurement and the optimal marketing of self-generating capacity.
Microgrid business models combine many of the above factors at the local or regional decentralised level – for instance, residential neighbourhoods, industrial zones or municipalities. Generating units and storage portfolios are bundled, with the interaction of the two optimised by intelligently controlled consumption. Microgrids are built on smart grid infrastructures, through which the necessary communication and control information is transported. It should be noted that, according to this definition, microgrids are not isolated solutions, as a connection to the wider distribution network is necessary to ensure security of supply. Also, optional procurement and marketing of electricity on the wholesale market can improve the business case for a microgrid.
The importance of trading
Facilitated through bidirectional digital communication infrastructure, all of the above business models rely on an energy trading function. This is essential in balancing supply and demand and making optimal decisions on when to self-generate, consume, store, purchase or sell on the wholesale market. The reduction of forecasting errors also allows forward contracting and the development of structured products, given sufficient volumes.
Energy trading skills relate to the structured management of production, purchasing and consumer portfolios and include, among other things, the preparation of production and consumption forecasts, know-how relating to price developments in different markets and the development of marketing, procurement and risk hedging strategies. These skills are also required in developing the business case for investment in distributed energy assets. For instance, an understanding of how energy storage and optimisation can compensate consumption profile adjustments to increase revenues can significantly enhance the economics of such investments.
While there can be benefits from the integration of small, decentralised generating units with the wholesale market, implementation provides firms with a challenge. Energy trading for individual plants is typically not possible or economically justified. Only above a certain portfolio size can economies of scale be realised and a positive business case developed. For over-the-counter transactions, the smallest tradable unit is 5 megawatts, and the same applies in the balancing energy market for the provision of primary and secondary reserve power.
Independent renewable energy power plants increasingly require access to commercial energy trading skills. Since 2012, renewable generators in Germany under the EEG have had the option each month of earning revenue either through a traditional fixed feed-in tariff, or by selling output directly onto the wholesale market and receiving a top-up payment. In the UK, top-up payment structures under the planned Electricity Market Reform could result in a similar requirement for independent wind generators.
Energy traders with existing platforms and infrastructure have the ability to provide these services to other market participants – and there is evidence of increasing demand for trading service agreements and other forms of market access and risk-sharing contractual models. By offering these services to sufficient numbers and types of smart-distributed and renewable energy asset owners, existing energy traders can pool customer portfolios to realise the necessary economies of scale. For this to be successful, however, energy traders will require expanded commercial skills and processes to meet the requirements of a wide range of distributed generation, storage and consumption asset types and configurations.
To make these services work, traders and asset owners will require more detailed forecasting models. Among other things, such models should incorporate the level of price optionality under which power consumption may change. Traders will also need to facilitate two-way energy and communication flows to effectively optimise consumption and generation. In the case of surplus production, for instance, traders and asset owners must decide whether to sell the excess power directly on the wholesale market, store it for later consumption or sale, or even bring forward demand to consume it immediately and avoid future consumption at higher prices. In order for this real-time optimisation to take place, energy trader interfaces – including highly automated IT systems, clear contractual roles and responsibilities, and active risk and position management – are required.
The interface between asset owners and energy traders is a key element in realising the value creation potential of decentralised, flexible assets. An IT platform must be created that automates the bidirectional communication with very large numbers of remotely controlled and geographically diverse production, storage and consumption assets. The system will seek to optimise and control the operational profiles of the assets to take advantage of half-hourly forward curves that vary at even shorter time intervals. Contractual structures must be put in place that can allow choices to be taken by one, but not both, of the parties at any point in time. Examples include deciding when power should be purchased on the wholesale market instead of using domestic production, when and with what power plants electricity ought to be generated, how storage should be used, and which parameters should influence the flexibilities of consumption patterns and provision of ancillary services. With investment in upfront planning and design, such interfaces have proven to be possible, and additional revenues have been realised for both traders and asset owners.
First-mover advantage
Smart energy is essential to a successful implementation of the energy transition in Europe and opens up opportunities for new business models at the same time. Energy traders have the necessary skills and processes to meet the special requirements of distributed generation, storage and consumption assets.
The experience in Germany under the EEG is that the direct marketing of renewable energy has been adopted more quickly by local players that followed regulatory developments most closely. The regime is likely to be extended in the future, with additional trading costs incurred by all market participants, but with lower compensation. First-movers therefore realised early windfall profits, which enhanced the investment case for trading activities. Late adopters will incur these costs in future with a less compelling investment case. The lesson learned is that speed is a key success factor.
Jim Fitzgerald, Jochen Schneider and Matthias Raeck are associate partners at The Advisory House, a Zürich-based energy management consultancy
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1. Transmission use of system charges represent the cost paid by generators to access high voltage transmission systems
2. Distribution use of system charges represent the cost paid by generators to access local distribution networks
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