This paper will analyse Demand Side Management (DSM), an approach to include the demand side in the electricity system and consequently increase flexibility. The demand side should be regarded as a variable and not as fixed, as Gellings put it beautifully. With renewable energy, energy production becomes a variable source, and the demand has to adapt accordingly. The concept of DSM has been used in the USA since the 1980s but remains a novel subject in the European Union (EU). In 2009, the European Commission set the target to increase the capacities of DSM because only a tenth of the actual potential was used within the EU in 2014.
A significant obstacle regarding DSM is the deviating estimation of DSM Potentials. There exist multiple studies, which estimate the potential in Germany, but due to the high deviations that exist between them, making it is dicult to estimate the real potential. For investments, this is an important measure, and it should have high accuracy. These deviations potentially come from a missing definition for DSM, which is often entangled with Demand Response (DR), sector coupling and load management. As no clear definition or distinction to other terms is visible, it seems necessary to define the term DSM. For that, chapter 2 analyses the initial objective, modern approach and relative terms of DSM. Eventually, this leads to a more precise distinction, which helps to calculate the DSM potential from Germany with a broad analysis of multiple studies. These studies are typically based on different assumptions and were conducted in different years, therefore having different annual electricity consumption and consequently di↵erent potentials (Ladwig, 2018). A normalisation with regard to the annual electricity consumption will be performed and followed by a distinction of the assumed restrictions. Here, it helps to look at the DSM groups, which can implement DSM measures and point out their individual restrictions and challenges that would limit their potential or increase the complexity of integration. Scientific modelling helps to estimate the effects of novel approaches on the electricity system. As DSM has many interdependencies and is very decentralised through many individual participants, different approaches for a framework of DSM will be analysed.
At a glance
- Grade: 1.3 (1.0 is the best grade)
- Compares seven studies estimating demand-side management potential
- Compares eight scientific papers with differing DSM definitions and focuses
- Normalises study estimates to 2019 electricity consumption
- 16 figures and 11 tables
Contents
1 Fundamentals of the Electricity System
2 Definition of Demand Side Management
2.1 History
2.2 Modern Approach
2.2.1 Current Debates
2.2.2 Analysis of Demand Side Management Concepts & Activities
2.2.2.1 Demand Response
2.2.2.2 Strategic Energy Efficiency
2.2.2.3 Strategic Energy Conservation
2.2.2.4 Strategic Load Growth
2.2.3 Discussion and Result Analysis
2.3 Proposed Definition
3 Classification of Demand Side Management Potentials in Germany
3.1 Categorisation by Demand Side Management Groups
3.1.1 Industrial Processes
3.1.1.1 Normalisation of Impact Parameters
3.1.1.2 Restrictions for Industrial Processes
3.1.2 Commercial
3.1.3 Residential
3.2 Challenges of Implementation
3.3 Analysis of Potentials
4 Modeling Approaches
4.1 dsmlib - region4FLEX
4.1.1 Mathematical Framework
4.1.2 Methodology of dsmlib
4.1.3 Discussion of dsmlib
4.2 REMix
4.2.1 Methodology of REMix
4.2.2 Discussion of REMix
4.3 Conclusion of Modelling Approaches
5 Outlook
Objectives and Topics
This thesis investigates how demand side management can be defined and how its potential can be classified and compared for a German electricity system with increasing shares of renewable energy. It examines why estimates differ between studies, considers sector-specific constraints and analyses approaches to modelling demand-side flexibility.
- The history and changing definitions of demand side management
- Demand response, load shifting and other load-shaping activities
- Comparison and normalisation of German potential estimates
- Industrial, commercial and residential flexibility
- Technical, economic and regulatory implementation challenges
- Storage-equivalent modelling in dsmlib and REMix
Excerpt from the Book
Fundamentals of the Electricity System
For a better understanding of the benefits of DSM in regard to other flexibility measures, it is important to understand the electricity system and its constraints. The underlying concept of electricity systems is to match supply with demand. An electricity system operator must balance the supply and demand during all time frames to keep the frequency and network stable. This constraint distinguishes the electricity system from other markets as it can lead to power outages if not fulfilled (P. Konstantin and M. Konstantin, 2018). When consumption surpasses production, we speak of peak demand, while output with lower consumption is called off-peak demand. The production through generators is split into dispatchable and non-dispatchable output. Operators can adjust the power output from dispatchable generators according to the power system’s needs. Examples are coal- or gas-based power plants. The power output from non-dispatchable generators, such as photovoltaic and wind power, can not be adjusted in that way as it is dependent on external factors such as wind or sunlight (Hirschhausen et al., 2018). There exist typically fluctuations between demand and supply, but these are increased through RES (Zerrahn et al., 2018). Renewable energy generation depends on the season and weather, which results in fluctuating output. Next to that, the output is only weakly correlated to hourly load profiles and with increased RES, the residual load falls and can result in higher shares of negative residual (Ladwig, 2018). The residual load is the share of demand after eliminating the non-dispatchable power generation (Schill, 2014). To increase dispatchable generation the electricity system needs to adapt. In 2020, 46% of electricity were generated by RES for Germany, which is set to increase in the future (Hayn et al., 2022). In the past, the demand was met through variable energy generation. With higher shares of RES, the electricity transmission network needs to convert, from a design driven by large-scale technologies, to a network that can manage distributed generation (Strbac, 2008).
Due to the constraint of a constant balance between demand and supply, different immediate approaches exist to solve a potential mismatch. Measures to solve a mismatch between demand and supply fall under the term balance management or frequency control, which refers to real-time balancing to ensure the security of supply during all time frames (Lampropoulos et al., 2013).
Chapter Summaries
1 Fundamentals of the Electricity System: Introduces electricity-system balancing, renewable generation, the merit order and alternatives for managing mismatches between supply and demand.
2 Definition of Demand Side Management: Traces the development of DSM, compares definitions and associated activities, and proposes a definition oriented towards electricity-system flexibility.
3 Classification of Demand Side Management Potentials in Germany: Compares estimates across industrial, commercial and residential users, normalises selected values and discusses restrictions and implementation challenges.
4 Modeling Approaches: Examines the mathematical frameworks and assumptions behind dsmlib and REMix, including storage-equivalent representations of flexible demand.
5 Outlook: Considers regulatory, technological and economic challenges for implementing DSM in Germany.
Keywords
demand side management, demand response, renewable energy, Germany, electricity systems, flexibility potential, load shifting, peak clipping, energy conservation, industrial processes, residential demand, electricity market regulation, dsmlib, REMix
Frequently Asked Questions
What is this thesis about?
It examines demand side management and its potential role in a German electricity system with growing shares of renewable energy.
What are the central themes?
The central themes are DSM definitions, demand-side flexibility, comparisons of potential estimates, implementation constraints and modelling approaches.
What is the main research objective?
The thesis aims to clarify the meaning of DSM and develop a basis for comparing estimates of its potential in Germany.
What research method does the author use?
The author compares published studies and definitions, normalises selected potential estimates against annual electricity consumption and analyses two modelling frameworks.
What does the main analysis cover?
It examines industrial, commercial and residential demand, the assumptions behind potential estimates, barriers to implementation and representations of flexibility in electricity-system models.
Which keywords characterise the thesis?
Key terms include demand side management, demand response, renewable energy, load shifting, peak clipping and flexibility potential.
Why are potential estimates normalised?
The studies use different years and electricity-consumption assumptions. Normalisation to a common consumption basis helps make selected estimates more comparable.
Which industrial processes are examined?
The analysis discusses electric arc furnaces, aluminium electrolysis, cement mills, chlorine-alkali electrolysis, glass production and wood pulp production.
Which modelling approaches are discussed?
The thesis examines dsmlib in the region4FLEX context and the REMix model, focusing on how they represent demand-side flexibility and its constraints.
- Quote paper
- Lukas Schirren (Author), 2022, A Classification of Demand Side Management Concepts. Their Potentials for a Renewable Energy System in Germany, Munich, GRIN Verlag, https://www.grin.com/document/1342677
