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Power Markets and Energy Demand
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Power Markets and Energy Demand

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (24 May 2022) | Viewed by 17536

Special Issue Editors

Prof. Dr. António Gomes Martins

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Guest Editor
Department of Electrical and Computer Engineering, University of Coimbra, 3004-531 Coimbra, Portugal
Interests: energy policy; energy planning; energy efficiency; demand-side management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Humberto M. Jorge

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Guest Editor
Institute for Systems and Computer Engineering of Coimbra (INESCC), 3004-531 Coimbra, Portugal
Interests: energy efficiency; renewable energy sources; demand response; energy storage
Prof. Alvaro Gomes

E-Mail Website
Guest Editor
Faculty of Sciences and Technology - University of Coimbra, Coimbra, Portugal
Interests: Efficient Use of Energy Resources; Smart Grids; DSM/DSF; Metaheuristics in power systems; Energy Market Transformation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In many parts of the world, power systems have evolved towards an unbundled structure, where competition exists at the generation and the retail phases of the value chain. This evolution led to the existence of many actors with a potential for participation in electricity markets.

The current predominant intervention of generators and suppliers in electricity markets is giving place to regulatory allowances to the participation of other market agents such as aggregators, energy communities, and energy service companies. Small scale investments in renewable electricity production are proliferating due to substantial reduction of investment costs.

The current digitization of electricity networks, including the roll-out of smart meters, together with the growing penetration of electric vehicles, provides opportunities to explore the flexibility potential of the demand side on the management of the infrastructure as a smart grid. It also changes the perspective of consumers on the overall cost of electricity supply.

Prosumers may be organized in energy communities and, with or without a contract with an independent aggregator, can participate in the electricity market, sell renewable electricity, and demand flexibility and network services as distributed energy resources (DER). Some experiences show the feasibility of such schemes but the overall impact on power systems’ planning, operation, reliability, and utility revenues is still under research.

Many questions are still open to debate, such as the influence of the new agents on the operation of electricity markets, namely on setting the price of the kWh, the practical influence of energy communities on the costs incurred by the associated consumers, the contribution of the demand side to the balanced operation of the power system, or to the provision of network services, to name just a few.

Additionally, there is one additional global question on the role of the demand side in those situations where vertically integrated utilities prevail in many parts of the world: how the new forms of the demand side intervention, including DER, can be included, for example, in integrated resource planning.

The Special Issue will provide an opportunity to explore these hot topics on energy demand, which has been the source of vibrant and disruptive research.

Prof. Dr. António Gomes Martins
Prof. Dr. Humberto M. Jorge
Prof. Alvaro Gomes
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • demand-side and capacity markets
  • energy efficiency
  • network flexibility management
  • energy communities
  • electricity value chain
  • smart metering
  • demand response
  • behind-the-meter storage
  • distributed energy resources

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Published Papers (3 papers)

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Research

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19 pages, 1195 KiB  
Article
Flexibility Estimation of Residential Heat Pumps under Heat Demand Uncertainty
by Zhengjie You, Michel Zade, Babu Kumaran Nalini and Peter Tzscheutschler
Energies 2021, 14(18), 5709; https://doi.org/10.3390/en14185709 - 10 Sep 2021
Cited by 10 | Viewed by 2657
Abstract
With the increasing penetration of intermittent renewable energy generation, there is a growing demand to use the inherent flexibility within buildings to absorb renewable related disruptions. Heat pumps play a particularly important role, as they account for a high share of electricity consumption [...] Read more.
With the increasing penetration of intermittent renewable energy generation, there is a growing demand to use the inherent flexibility within buildings to absorb renewable related disruptions. Heat pumps play a particularly important role, as they account for a high share of electricity consumption in residential units. The most common way of quantifying the flexibility is by considering the response of the building or the household appliances to external penalty signals. However, this approach neither accounts for the use cases of flexibility trading nor considers its impact on the prosumer comfort, when the heat pump should cover the stochastic domestic hot water (DHW) consumption. Therefore, in this paper, a new approach to quantifying the flexibility potential of residential heat pumps is proposed. This methodology enables the prosumers themselves to generate and submit the operating plan of the heat pump to the system operator and trade the alternative operating plans of the heat pump on the flexibility market. In addition, the impact of the flexibility provision on the prosumer comfort is investigated by calculating the warm water temperature drops in the thermal energy storage given heat demand forecast errors. The results show that the approach with constant capacity reservation in the thermal energy storage provides the best solution, with an average of 2.5 min unsatisfactory time per day and a maximum temperature drop of 2.3 °C. Full article
(This article belongs to the Special Issue Power Markets and Energy Demand)
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Review

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36 pages, 1017 KiB  
Review
Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load
by Ottavia Valentini, Nikoleta Andreadou, Paolo Bertoldi, Alexandre Lucas, Iolanda Saviuc and Evangelos Kotsakis
Energies 2022, 15(14), 5259; https://doi.org/10.3390/en15145259 - 20 Jul 2022
Cited by 11 | Viewed by 3820
Abstract
Climate neutrality is one of the greatest challenges of our century, and a decarbonised energy system is a key step towards this goal. To this end, the electricity system is expected to become more interconnected, digitalised, and flexible by engaging consumers both through [...] Read more.
Climate neutrality is one of the greatest challenges of our century, and a decarbonised energy system is a key step towards this goal. To this end, the electricity system is expected to become more interconnected, digitalised, and flexible by engaging consumers both through microgeneration and through demand side flexibility. A successful use of these flexibility tools depends widely on the evaluation of their effects, hence the definition of methods to assess and evaluate them is essential for their implementation. In order to enable a reliable assessment of the benefits from participating in demand response, it is necessary to define a reference value (“baseline”) to allow for a fair comparison. Different methodologies have been investigated, developed, and adopted for estimating the customer baseline load. The article presents a structured overview of methods for the estimating the customer baseline load, based on a review of academic literature, existing standardisation efforts, and lessons from use cases. In particular, the article describes and focuses on the different baseline methods applied in some European H2020 projects, showing the results achieved in terms of measurement accuracy and costs in real test cases. The most suitable methodology choice among the several available depends on many factors. Some of them can be the function of the Demand Response (DR) service in the system, the broader regulatory framework for DR participation in wholesale markets, or the DR providers characteristics, and this list is not exclusive. The evaluation shows that the baseline methodology choice presents a trade-off among complexity, accuracy, and cost. Full article
(This article belongs to the Special Issue Power Markets and Energy Demand)
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58 pages, 3698 KiB  
Review
Modeling Energy Demand—A Systematic Literature Review
by Paul Anton Verwiebe, Stephan Seim, Simon Burges, Lennart Schulz and Joachim Müller-Kirchenbauer
Energies 2021, 14(23), 7859; https://doi.org/10.3390/en14237859 - 23 Nov 2021
Cited by 33 | Viewed by 10106
Abstract
In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing [...] Read more.
In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling. Full article
(This article belongs to the Special Issue Power Markets and Energy Demand)
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