Smart Energy

The first power grids emerged in the 1890s, driven by pioneers such as Thomas Edison and George Westinghouse and companies such as General Electric and Westinghouse Electric Corporation. These grids were highly centralized, isolated systems. Over the following decades, local grids became more and more interconnected. By the 1960s, electric grids had matured into highly interconnected systems. These grids were dominated by large power plants (usually based on fossil fuels such as coal, gas, and oil) that used high-capacity power lines to connect with the centers of electricity consumption, from which lower-capacity lines delivered electricity to the end users. Due to limitations in metering technologies, fixed tariffs were often used to bill end users.

From the very beginning, a key challenge faced by electric power grids involved dealing with varying energy demand. The main problems here related to very limited energy storage capacities and high costs for adding additional power-generation units on demand. Adding peak-time generators such as gas turbines with low start-up times is a relatively costly solution, for example. Initial attempts were made to solve the problem on the demand side. For instance, dual tariffs were introduced to encourage customers to increase their usage of electric power at night, when demand is generally lower. Demand-aware devices (such as air conditioners, refrigerators, and heaters) were also introduced; these can sense the load in the grid using analog technologies, by monitoring changes in the power supply frequency, for example.

Nowadays, distribution networks accommodate an increasing share of (decentralized) generation, from sources such as small- and mid-sized wind power plants, combined heat and power plants, or solar power plants. This puts pressure on the power grids, making grid stability more difficult to maintain. Digitization is able to make the grid “smart” enough to deal with these new requirements. In the following, the impact of digitization on the energy sector is described in more detail.

Additionally, the last decade has seen a considerable increase in power trading both in national wholesale markets and across national borders, leading to new requirements for the transmission grid. Nowadays, distribution networks accommodate an increasing share of (decentralized) generation, from sources such as small- and mid-sized wind power plants, combined heat and power plants, or solar power plants. The majority of consumers are connected to the distribution network, and metering services are used to record both consumption and feed-in.

Influence of Digitization

In recent years, the phenomenon of digitization has evolved into a genuine megatrend of cross-industry relevance. Although the energy industry initially lagged behind other industries, it is now rushing to catch up, with digital elements affecting more and more aspects of this market. The term “digitization” itself, however, is actually quite vague, so we need to define what we mean by it. Bernhard Schaefer, Senior Manager at m3 management consulting, defines digitization as “the comprehensive transformation of an energy company’s products and services, its value chain (from generation to consumption), as well as its internal support functions (controlling, purchasing, HR, etc.) making use of new information and communication technologies and fulfilling related customer expectations.” The result is a “smart” utility, part of a “smart” energy world that will be embedded into the wider Internet of Things to an ever-greater extent.

A smart energy system will differ from the traditional system in many ways.

Data-led intelligence is entering more or less all steps of the energy value chain, with a pronounced focus on generation, distribution, metering, and consumption. This is illustrated in more detail in the figure below, which maps IoT applications to the extended energy value chain. (Note that the value chain is different in some EU countries; for example, smart meters come under retail in the UK). Below, we will examine each step in more detail.

Figure: Extended energy value chain and IoT use cases

Generation

Power generation is a field in which digitization can undoubtedly bring significant benefits. While in the past, the traditional electricity system was primarily fed by large, centralized power stations, today a huge and ever-increasing number of small, decentralized installations inject power into the grid. Most of these additional capacities are renewable generation sources like wind, solar, and biomass. However, production from wind and solar energy sources is highly volatile, and production forecasts – which rely on weather forecasts – are prone to inaccuracy. This presents a challenge for network operators, who must keep their networks stable and secure by ensuring, inter alia, a constant balance between power injection and power consumption. Energy expert Bernard Kryszak says: “One option for meeting this challenge is to bundle a variety of small electricity generators into a coordinated ‘Virtual Power Plant’ (VPP). Production of power by the VPP’s individual entities (which may utilize solar, wind, biomass, small-scale water, or CHP energy sources) is centrally optimized using a single IP network to collect and analyze production data from the decentralized units, local weather forecasts, demand forecasts, and so on. This synergy-based approach makes it possible to minimize generation costs for required output. Furthermore, a VPP is able to provide a more stable overall power output in comparison to the individual sources, for example by ramping up biomass power at times when the sun is not shining and solar power cannot be generated. Particularly in markets with a high degree of volatile power generation, VPPs are economically attractive if the regulatory framework provides sufficient market signals to incentivize the provision of reserve capacity, which contributes to system stability.”

Another field of application for a digital infrastructure concerns monitoring and diagnostics for remote generation assets. Imagine a large offshore wind farm far off the coast. Simple time-based maintenance would not be appropriate here, as the costs involved in traveling to this location are very high. Instead, operational data from sensors connected to components in each wind turbine (such as the blades, gear box, oil pumps, controls, motors, and generator parts) are continuously fed to a central control center. Here, the data is stored in a data historian in a structured way and made available for analysis. This makes it possible to monitor the condition of each part and tailor maintenance plans to address the turbines’ actual condition.

In addition, digitization can make support processes for generation more effective and efficient. The concept of a “digital supply chain” describes close data integration with external suppliers. This helps to ensure that suppliers of essential spare parts, for example, can have access to transparent online information about a generator’s additional supply requirements. As a result, spare parts are delivered subject to demand and warehouses are well stocked (with minimal excess), which leads to increased reliability for power plants.

Turning now to smart applications in the field of private power generation, we shift our focus to energy management solutions. These can be helpful for private energy “prosumers”, i.e. private consumers who also produce energy by means of photovoltaics (PV) or micro CHP (combined heat and power). Imagine a household with a PV installation on the roof. With the help of weather forecasts, the prosumer can forecast their installation’s electricity output and – taking into account the variable power prices offered by their utility – can optimize their consumption accordingly. This increases the share of self-consumed solar electricity while at the same time reducing usage of the external distribution network.

With fully integrated trading platforms and automated trading processes, energy trading is also becoming a lot smarter. Whereas market participants used to trade power on a day-ahead basis only, trading today can be conducted via system-to-system messaging without manual intervention. This enables real-time trading and short-term optimization, thus ensuring sufficient agility in the new volatile energy markets.

Transmission

Today, Transmission System Operators (TSOs) are faced with the mounting challenge of maintaining stable and secure grid operation despite the increasing pressure caused by the growing share of intermittent renewable energies. As grid stability is always paramount, TSOs are permitted to ensure load balancing (frequency and voltage) by obliging power producers to temporarily ramp up or down their generation assets, with the aim of achieving the necessary constant balance between production and consumption (the latter being more difficult to influence). In this context, generation assets that are directly connected to the TSO’s transmission grid are directly and remotely controlled by the TSO’s control center via dedicated data interfaces.

Distribution and Metering

Looking to the area of distribution and metering, we encounter what is probably the best-known element in the new intelligent energy landscape – the “smart meter”. A smart meter is a metering device that is capable of recording power consumption at short intervals, such as every 15 minutes, and then transmitting this data to the supplying utility for monitoring and billing purposes. Smart meters also enable two-way communication between the utility and the metered electric load, allowing electrical appliances to be controlled so that their energy consumption is highest at times when energy prices are low due to high renewable energy production.

When smart meters and other sensors and actuators are installed at a large number of relevant load points and network assets (such as controllable transformer stations), with collected data being fed into a central Distribution Management System, this facilitates real-time analysis and control of the network. The resulting “smart grid” can be monitored in detail and controlled remotely by the network operator. Network operation can in fact be automated to a significant extent, which enables fast reactions to network events like local supply disruptions. As in the case of remote generation, this also greatly facilitates condition-based maintenance of grid assets based on real-time information about asset condition. Maintenance activities can be further optimized through predictive analytics, i.e. the forecasting of asset condition based on historic data and learning analysis algorithms.

As Bernhard Schaefer, Senior Manager at m3 management consulting, points out: “There are a number of benefits associated with making a grid ‘smarter': electricity flows are optimized, system security is improved, network capacities are better utilized, and network assets are better protected from faults. Also, renewable energy sources are more easily integrated into the network. This reduces the amount of required investment in networks and cuts operating expenses for asset servicing, while at the same time minimizing network faults and – in the event of supply disruptions – allowing supply to be restored faster.”

Nevertheless, when viewed from the network operator’s perspective, installing and operating smart metering devices is in many cases not cost-effective in comparison to the use of conventional meters. Indeed, in Europe, only Sweden and Italy have undertaken a full rollout of smart meters. In other European countries, the installation of smart meters is still under debate and will generally need to be preceded by legislation and regulation. In the long term, however, the costs of communication and hardware should decrease significantly, which will improve the business case for smart meters.

Storage

Electricity storage is an option for accommodating excess energy produced by renewables at times of low demand. In addition to large, central storage installations like pumped-storage hydroelectricity, there is a lot of development activity around bringing small- and medium-scale storage to the market, with examples such as lithium-ion batteries or power-to-gas technology. One particularly interesting form of potential electricity storage is the “vehicle-to-grid” concept. With a growing fleet of plug-in electric vehicles, a digital infrastructure could be used to control the vehicles’ battery charging process and release energy back into the grid for stability in the event of surplus energy or demand peaks.

Although most of these storage technologies have not (yet) become profitable in most market environments, it is evident that added benefit is to be derived from integrating distributed, small-scale storage devices into a smart grid. Dispatch is thus based on actual overall system needs and takes network and geographic constraints into account, in an effort to achieve overall economic optimization.

Marketing, Sales, and Service

Up to this point, we have addressed purely technical solutions. However, smart energy is not restricted to machine-to-machine communication (M2M). The digital customer interface is another important element in the evolving smart energy world. Firstly, new products can be designed by bundling energy and information services. Secondly, sales and/or margins associated with existing products and services can be improved through analytics-based optimization of customer segmentation and pricing, using the newly available data to better target customer needs. Thirdly, after-sales support can be improved by leveraging customer insight obtained through an analysis of smart metering data, and also by introducing user-friendly mobile services and online self-service portals for billing, submitting meter readings, on-demand support, and energy efficiency initiatives. And fourthly, suppliers can expect to gain additional information about customer behavior and key marketing data, which can be provided to third parties for targeted sales and advertisements.

Customers

In terms of the end customer, at the end of the value chain, numerous initiatives center on reducing energy consumption through increased energy efficiency and reducing costs through intelligent load scheduling. This is typically done by means of flexible scheduling according to real-time market price changes. This goes so far as to offer customers financial incentives for increasing their electrical load during situations in which network stability is endangered due to excess power production.

Energy management solutions primarily apply to industrial and commercial customers, for whom energy bills (at least in Europe) represent a dominant part of most cost structures. Demand response management is used to monetize existing flexibilities in production processes, for instance by reducing cooling and heating processes or other electrical loads during times at which higher prices are charged. Internal synergies between different power plants and/or different energy forms are also exploited (power versus natural gas, for example).

In the private sector, the issues of energy efficiency, personal comfort, personal health, and security are addressed by the “Smart Home” concept. In a smart home, integrated home-automation controls optimize the use of lighting, heating, electrical appliances like refrigerators and washing machines, as well as motorized blinds and security systems. Learning algorithms help to reduce energy consumption, for instance by turning down the heating when the resident goes to work. Sensors can monitor movement and send alerts, for example in a situation where an elderly person falls or doesn’t move for an extended period of time. Smartphone apps allow users to monitor their home’s security features remotely. Furthermore, e-mobility applications can be integrated into a smart home by incorporating charging and discharging processes into the overall home energy management concept.

When is All This Going to Happen?

The adoption of smart electricity meters will be a key driver for the widespread adoption of smart home and other downstream devices. In several markets worldwide, this is subject to government intervention and regulatory decisions, while in other markets, network efficiency and “non-technical losses” (i.e. theft) can be a significant factor influencing the adoption of smart electricity meters. The potential beneficial environmental impact of adopting smart electricity metering is a near-global driver.

The net result is that smart metering and smart grid management solutions will be among the first technologies to be adopted in the coming IoT era. A vast array of other solutions will subsequently gain traction as a result of the introduction of these technologies. One category of solutions will include applications and devices that are effectively part of the smart grid infrastructure and support low-voltage power generation, for instance. We will also see a range of devices that are part of the wider smart grid ecosystem, which will be used where there is potential to increase efficiency by modifying power consumption. This second category would include many smart home devices.

The figure below illustrates the total number of smart grid IoT connections represented by these two different categories of devices. It also illustrates the share of the total number of smart grid IoT connections that is contributed by smart grid infrastructure devices, and highlights the share of the total number of IoT devices that is in some way related to the emerging smart grid. There are three clear messages to be drawn from these figures. Firstly, the total number of devices included in the IoT smart grid can be expected to grow quickly, approaching a total of 12 billion in 2024 from a base of less than a billion today.  Secondly, and perhaps unsurprisingly, devices related to the smart grid infrastructure will be adopted prior to devices that depend on smart grid functionality. Thirdly, devices related to the smart grid will make up an increasing share of IoT-connected devices. These forecasts show that by 2024, more than 40% of all IoT device-cloud connections (excluding PCs, tablets and handsets) are forecast to belong to the smart grid ecosystem, which places smart grid and VPP concepts firmly at the core of the IoT.

Conclusions

Most of the trends described in this chapter are still in the pilot or early commercialization phase. Yet energy expert Bernard Kryszak emphasizes: “We are confident that a large number of use cases will turn out to be profitable. However, the success of many smart energy applications depends on both an integrated perspective in terms of the resulting benefits and a fair distribution of costs among those profiting from this smart technology – generators, network operators, sales entities, and customers. A common infrastructure is an important part of this integrated perspective.”

Energy Case Studies

In the following sections, we will delve into some of these topics in more detail. To do so, we have collected a number of very interesting case studies on the use of IoT technologies in different segments of the smart energy value chain as discussed above:

• Power generation
  • Smart Monitoring and Diagnostics Systems at Major Power Plants
• Metering
  • UK Smart Metering Implementation Programme (SMIP)
• Microgrids and Virtual Power Plants
  • Smart City Rheintal
  • Smart Energy in the Chemical Industry
• Cross Energy Management
  • CEM in farms
  • CEM in steel production