Smart Grid Economics and Management

The energy systems are under tremendous change, driven both by technical change (digitalization) and climate protection policy. Overall, it is expected that the future energy system will be decarbonized, digitalized, distributed—and thus also democratized (the four “Ds”). An increased digitalization and the vast system transformation also bear new risks, some of which are related to cybersecurity.

The question why energy economics was established as a dedicated subdiscipline of economics is typycally answeres by pointing to the many specifies if energy markets, regulation and politics. Likewise, one could ask the question why there is a need for smart grid economics? Put differently, which tools in the toolbox of an energy economist are missing entirely, and which ones have to be adapted or otherwise modified?

To respond to the variability of supply that characterizes RES generation, a range of potentially competing options exists, including DR, grid expansion, energy storage, enhanced flexibility of power generation, and improved operational practices. For better understanding, the set of alternatives that can provide responsive energy over various timescales can be grouped into supply side, demand side, and grid related (Denholm and et al. 2010; IRENA 2018). While supply-side flexibility is closely related to the performance of the technologies comprising the generation units of a power system, demand-side flexibility refers to specific types of demand-side management where the demand pattern could be shifted to better match electricity supply (IRENA 2018).

In January 2001, California experienced rolling blackouts in their energy systems and an average price of electricity of $250 per MWh. This price was nearly ten times the average price of the previous January in the year 2000 (Woo et al. 2003). What had happened? California had liberalized its electricity market with a zonal setup and with it, had introduced various new market components that were intended to reduce grid congestion, market power and risks for consumers and at the same time to drive down wholesale electricity prices. There was little experience with designing electricity markets at that time and some market design choices created strategic incentives for individuals to optimize themselves against the market (see Alaywan et al. (2004) for more details on California’s design flaws). California was not the last trial and error of power market design. There are constant reports on small power market failures all over the world. This is not surprising as the market design for power markets is complex and it needs to integrate various interests. The peculiarities of the power market have been introduced to you in chapter “Smart Grid Economics”. The worldwide transition to more intermittent renewable generation challenges traditional market designs. It leads to generation spikes that can cause congestion (Staudt 2019), reversed power flows on the distribution level (Walling et al. 2008) or even negative electricity spot prices (Kyritsis et al. 2017). The market design needs to be adapted to these changes. At the same time, the Smart Grid introduces new possibilities to measure and control actions at the low voltage level that can help to balance the local infeed of renewable generation. This allows new actors to enter the market, it creates active and price-sensitive consumers and it provides more detailed market information. It is important to note that when we talk about the power market (in this chapter we will use power, electricity or energy market synonymously), we are really talking about a multitude of market stages (i.e., sub-markets) such as the wholesale spot and intraday markets or the market for balancing power, for example. Others might be added in the course of the energy transition, such as redispatch markets (Hirth and Glismann 2018). The Smart Grid potentially impacts all of these sub-markets and it might enable the creation of new sub-markets, for example, in the form of peer-to-peer trading in the distribution grid (Mengelkamp et al. 2018). Such markets as well as changes in the existing markets need to be carefully engineered to avoid market failures as in California and to achieve the intended objectives. In this chapter, we, therefore, introduce the market engineering framework as a way to systematically describe and engineer existing and new markets. We then describe the impact of the Smart Grid on energy markets and discuss how these changes can be classified.

For an economist, the power system value chain consists of four main stages: generation, transmission, distribution and retail. Generation is the production and retail is the sale of electricity to end-users; sometimes, wholesale trade is considered another separate stage in the value chain. These are potentially competitive stages, where commercial companies can unfold market activities. In between, we find the networks; first the high-voltage transmission network and second the medium- and low-distribution networks. These are natural monopolies.

This chapter is motivated by the transformation of the energy system toward a smart grid economy which also necessitates new solutions in the field of decision support tools that are used by system operators and market stakeholders. Trends that can be observed in the management of smart grids are an increasing orientation toward digital and intelligent solutions and a stronger coupling between different energy sectors as well as a growing interaction between different stakeholders. Examples include the electrification of district heating via heat pumps, mobility applications, e.g., electric vehicles, or consumers who provide energy from rooftop photovoltaic systems to grid operators to ensure grid stability enabled by smart grid devices. These developments also have implications for the model-based representation of smart grid systems.

The rapid digitization of the electricity sector has lead to a massive increase of data availability. Smart grids produce large volumes of data, thanks to IoT devices like smart meters. This necessitates appropriate Data Analytics capabilities to tap into the envisioned opportunities (Zhang et al. 2018). Against the backdrop of ubiquitous computing, companies are building up capabilities for data analysis as well as automatic decision-making.

Until a few years ago, the utility companies in the electricity sector followed a rather simple business model: electricity was generated by centrally located power plants and then supplied to as many customers as possible via a network of poles and wires. The key value proposition was to deliver electricity as safely, reliably, and cost-effectively as possible and to keep outages or interruptions to a minimum. Triggered by new technologies and numerous external influences such as new political decisions, customer relations and market structure are changing more and more. In the coming years, power utilities, political decision-makers, authorities, and other stakeholders will have to reinvent themselves to meet emerging customer needs, as well as the issues of sustainability, decarbonization, electrification of transport, the introduction of distributed energy resources, and smart grids. We will look at some of these new trends and developments leading to this reinvention and the resulting challenges and opportunities in the field of Smart Grids.

The changes at the technical, regulatory, and market levels discussed in the book enable the emergence of new business models. In particular, new opportunities arise from the use of advances in digitization for applications in the smart grid. In this chapter, we would like to present and analyze examples of such new business models in two case studies. In recent years, a number of new companies have been established in the market. For example, in the B2B business, the German start-up Entelios and the American start-up EnerNOC successfully developed solutions in the area of demand response/demand-side management and were later acquired by the Italian energy company ENEL after a merger. With a growing share of renewable generation, especially forecasting of generation output of wind and PV plants is gaining importance. The German company Energy & Meteo Systems has specialized in this service. Younicos, a German-American start-up has developed control software for electricity storage, targeting flexible management of electricity supply and demand in the distribution grid. Younicos was acquired in 2017 by Aggreko, a Scottish energy company. Discovergy is a company in the B2C market that offers smart metering services for end customers. These examples show just a sample of new areas where demand for new solutions has emerged. It also shows that new business developed in both sectors, B2B and B2C, and that successful businesses are sometimes overtaken by established players in the market. The transition of the energy system is progressing, and we will certainly see more start-ups and business models emerging.