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Impacts of Distributed Energy Resources on Power Systems
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Impacts of Distributed Energy Resources on Power Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F2: Distributed Energy System".

Deadline for manuscript submissions: 21 July 2025 | Viewed by 3878

Special Issue Editor

Dr. Chong Wang

E-Mail Website
Guest Editor
College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Interests: power system resilience; uncertainty analysis and control of power system; integrated energy power system modeling and optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Distributed energy resources have been promoted aggressively by the search for clean energy. For example, the aim of attaining the carbon emission peak and carbon neutrality in China has promoted the development of a large number of distributed energy resources, e.g., wind power, photovoltaic power, energy storage, hydrogen energy, and active loads, all integrated into power systems. However, the high penetration of these distributed energy resources can pose significant challenges to power system planning, operation, and control. The spatial distribution and time sequence of these distributed energy resources can severely complicate their impact on power systems. The stochastic and intermittent characteristics of distributed renewables require rapid responses and flexibility to guarantee a power balance. Extensive behind-the-meter distributed energy resources further alter users’ behavior, leading to a change power system pattern. In addition, many distributed energy resources are associated with the external environment, which further complicates the impacts of distributed energy resources on power system planning, operation, and control.

This Special Issue aims to present and disseminate the most recent advances related to the theory, modelling, application, optimizaion, and control of distributed energy resources integrated into power systems, particularly including, but not limited to, the following fields:

(1) Modeling of distributed energy resources;

(2) Planning, operation, and control of distributed energy resources;

(3) Clustering analysis of distributed energy resources;

(4) Stochastic optimization with distributed energy resources;

(5) Uncertainty analysis of distributed energy resources;

(6) Demand-side responses as distributed energy resources;

(7) System resilience in consideration of distributed energy resources.

Prof. Dr. Chong Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • distributed energy resources
  • uncertainty analysis
  • stochastic optimization
  • renewables
  • resilience
  • demand-side resources

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Published Papers (3 papers)

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Research

21 pages, 2729 KiB  
Article
Fast Charging Guidance and Pricing Strategy Considering Different Types of Electric Vehicle Users’ Willingness to Charge
by Huachun Han, Huiyu Miu, Shukang Lv, Xiaodong Yuan, Yi Pan and Fei Zeng
Energies 2024, 17(18), 4716; https://doi.org/10.3390/en17184716 - 22 Sep 2024
Cited by 2 | Viewed by 1071
Abstract
As the penetration rate of electric vehicles (EVs) increases, how to reasonably distribute the ensuing large charging load to various charging stations is an issue that cannot be ignored. This problem can be solved by developing a suitable charging guidance strategy, the development [...] Read more.
As the penetration rate of electric vehicles (EVs) increases, how to reasonably distribute the ensuing large charging load to various charging stations is an issue that cannot be ignored. This problem can be solved by developing a suitable charging guidance strategy, the development of which needs to be based on the establishment of a realistic EV charging behaviour model and charging station queuing system. Thus, in this paper, a guidance and pricing strategy for fast charging that considers different types of EV users’ willingness to charge is proposed. Firstly, the EVs are divided into two categories: private cars and online ride-hailing cars. These categories are then used to construct charging behaviour models. Based on this, a charging decision model for EV users is constructed. At the same time, a first-come-first-served (FCFS) charging station queuing system is constructed to model the real-time charging situation in the charging station in a more practical way. Finally, a dynamic tariff updating model is used to obtain the optimal time-of-use tariff for each charging station, and then the tariffs are used to guide the fast-charging demand. By comparing the spatial and temporal distribution of charging demand loads at charging stations under different scenarios and considering whether the tariffs at each charging station play a guiding role, it is verified that the proposed strategy effectively optimises the balanced distribution of EV charging loads and alleviates the congestion at charging stations. Full article
(This article belongs to the Special Issue Impacts of Distributed Energy Resources on Power Systems)
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30 pages, 5440 KiB  
Article
Bi-Level Planning of Electric Vehicle Charging Stations Considering Spatial–Temporal Distribution Characteristics of Charging Loads in Uncertain Environments
by Haiqing Gan, Wenjun Ruan, Mingshen Wang, Yi Pan, Huiyu Miu and Xiaodong Yuan
Energies 2024, 17(12), 3004; https://doi.org/10.3390/en17123004 - 18 Jun 2024
Cited by 1 | Viewed by 1127
Abstract
With the increase in the number of distributed energy resources (DERs) and electric vehicles (EVs), it is particularly important to solve the problem of EV charging station siting and capacity determination under the distribution network considering a large proportion of DERs. This paper [...] Read more.
With the increase in the number of distributed energy resources (DERs) and electric vehicles (EVs), it is particularly important to solve the problem of EV charging station siting and capacity determination under the distribution network considering a large proportion of DERs. This paper proposes a bi-level planning model for EV charging stations that takes into account the characteristics of the spatial–temporal distribution of charging loads under an uncertain environment. First, the Origin–Destination (OD) matrix analysis method and the real-time Dijkstra dynamic path search algorithm are introduced and combined with the Larin Hypercube Sampling (LHS) method to establish the EV charging load prediction model considering the spatial and temporal distribution characteristics. Second, the upper objective function with the objective of minimizing the cost of EV charging station planning and user charging behavior is constructed, while the lower objective function with the objective of minimizing the cost of distribution network operation and carbon emission cost considering the uncertainty of wind power and photovoltaics is constructed. The constraints of the lower-layer objective function are transformed into the upper-layer objective function through Karush–Kuhn–Tucker (KKT) conditions, the optimal location and capacity of charging stations are finally determined, and the model of EV charging station siting and capacity determination is established. Finally, the validity of the model was verified by planning the coupled IEEE 33-node distribution network with the traffic road map of a city in southeastern South Dakota, USA. Full article
(This article belongs to the Special Issue Impacts of Distributed Energy Resources on Power Systems)
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23 pages, 3895 KiB  
Article
Adjustable Capability Evaluation of Integrated Energy Systems Considering Demand Response and Economic Constraints
by Yang Li, Rongqiang Li, Linjun Shi, Feng Wu, Jianhua Zhou, Jian Liu and Keman Lin
Energies 2023, 16(24), 8048; https://doi.org/10.3390/en16248048 - 13 Dec 2023
Cited by 5 | Viewed by 1125
Abstract
The coupling between multiple energy sources such as electricity, gas, and heat is strengthened in an integrated energy system (IES), and this, in turn, improves the operational flexibility of the IES. As an upper-level energy supply system, an IES can play a role [...] Read more.
The coupling between multiple energy sources such as electricity, gas, and heat is strengthened in an integrated energy system (IES), and this, in turn, improves the operational flexibility of the IES. As an upper-level energy supply system, an IES can play a role as virtual energy storage, which can provide regulating power to smooth out the volatility from large-scale renewable energy generation. The establishment of an aggregating virtual energy storage model for IESs has become an important issue. Under this background, a multi-objective optimization-based adjustable capacity evaluation method is proposed in this paper. Firstly, the mathematical model of an IES considering the coupling of multiple kinds of energy forms is proposed. Then, an aggregating model considering demand response and economic constraints is established to demonstrate the adjustable capacity of the IES. In addition, multi-objective optimization is used to identify parameters in the proposed model, and the normal boundary intersection (NBI) method is used to solve the problem. Finally, a simulation example is provided to verify the effectiveness and feasibility of the proposed method. The external energy demand boundary of the IES can be modeled as virtual energy storage, and the coupling relations of electricity and gas can be presented. Case studies demonstrate that economic constraints narrow the adjustable capacity of the IES while the demand response extends it. Full article
(This article belongs to the Special Issue Impacts of Distributed Energy Resources on Power Systems)
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