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Energies
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Energy Management of Prosumer Communities
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Energy Management of Prosumer Communities

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 13855

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Seppo Sierla

E-Mail Website
Guest Editor
Department of Electrical Engineering and Automation, Aalto University, 02150 Aalto, Finland
Interests: simulation; digital twin; virtual power plant; demand response; industry 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Mahdi Pourakbari-Kasmaei

E-Mail Website
Guest Editor
Department of Electrical Engineering and Automation, Aalto University, Maarintie 8, 02150 Espoo, Finland
Interests: power and energy systems; electric vehicle; high voltage; community energy systems; electricity markets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The penetration of distributed generation, energy storages and smart loads has resulted in the emergence of prosumers: entities capable of adjusting their electricity production and consumption in order to meet environmental goals and to participate profitably on the available electricity markets. Significant untapped potential remains in the exploitation and coordination of small and medium sized distributed energy resources. However, such resources usually have a primary purpose, which imposes constraints on the exploitation of the resource; for example, the primary purpose of an electric vehicle battery is for driving, so the battery could be used as a temporary storage for excess photovoltaic energy only if the vehicle is available for driving when the owner expects it to be. The aggregation of several distributed energy resources is a solution for coping with the unavailability of one resource. Solutions are needed for managing the electricity production and consumption characteristics of diverse distributed energy resources in order to obtain prosumers with more generic capabilities and services for electricity production, storage and consumption.

The special issue studies such prosumers and the emergence of prosumer communities for the integration, aggregation, orchestration and coordination of prosumers. Virtual power plants are one solution that has demonstrated its feasibility and viability, but the special issue is open for discussion on alternative concepts for prosumer communities.

Dr. Seppo Sierla
Dr. Mahdi Pourakbari
Guest Editors

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

  • prosumer
  • virtual power plant
  • demand response
  • distributed energy resource
  • energy storage

Published Papers (6 papers)

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Research

20 pages, 4523 KiB  
Article
A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage
by Harri Aaltonen, Seppo Sierla, Rakshith Subramanya and Valeriy Vyatkin
Energies 2021, 14(17), 5587; https://doi.org/10.3390/en14175587 - 06 Sep 2021
Cited by 6 | Viewed by 3191
Abstract
Battery storages are an essential element of the emerging smart grid. Compared to other distributed intelligent energy resources, batteries have the advantage of being able to rapidly react to events such as renewable generation fluctuations or grid disturbances. There is a lack of [...] Read more.
Battery storages are an essential element of the emerging smart grid. Compared to other distributed intelligent energy resources, batteries have the advantage of being able to rapidly react to events such as renewable generation fluctuations or grid disturbances. There is a lack of research on ways to profitably exploit this ability. Any solution needs to consider rapid electrical phenomena as well as the much slower dynamics of relevant electricity markets. Reinforcement learning is a branch of artificial intelligence that has shown promise in optimizing complex problems involving uncertainty. This article applies reinforcement learning to the problem of trading batteries. The problem involves two timescales, both of which are important for profitability. Firstly, trading the battery capacity must occur on the timescale of the chosen electricity markets. Secondly, the real-time operation of the battery must ensure that no financial penalties are incurred from failing to meet the technical specification. The trading-related decisions must be done under uncertainties, such as unknown future market prices and unpredictable power grid disturbances. In this article, a simulation model of a battery system is proposed as the environment to train a reinforcement learning agent to make such decisions. The system is demonstrated with an application of the battery to Finnish primary frequency reserve markets. Full article
(This article belongs to the Special Issue Energy Management of Prosumer Communities)
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17 pages, 1013 KiB  
Article
Decentralized Prosumer-Centric P2P Electricity Market Coordination with Grid Security
by Duarte Kazacos Winter, Rahul Khatri and Michael Schmidt
Energies 2021, 14(15), 4665; https://doi.org/10.3390/en14154665 - 01 Aug 2021
Cited by 10 | Viewed by 1994
Abstract
The increasing number of prosumers and the accompanying greater use of decentralised energy resources (DERs) bring new opportunities and challenges for the traditional electricity systems and the electricity markets. Microgrids, virtual power plants (VPPs), peer-to-peer (P2P) trading and federated power plants (FPPs) propose [...] Read more.
The increasing number of prosumers and the accompanying greater use of decentralised energy resources (DERs) bring new opportunities and challenges for the traditional electricity systems and the electricity markets. Microgrids, virtual power plants (VPPs), peer-to-peer (P2P) trading and federated power plants (FPPs) propose different schemes for prosumer coordination and have the potential of becoming the new paradigm of electricity market and power system operation. This paper proposes a P2P trading scheme for energy communities that negotiates power flows between participating prosumers with insufficient renewable power supply and prosumers with surplus supply in such a way that the community welfare is maximized while avoiding critical grid conditions. For this purpose, the proposed scheme is based on an Optimal Power Flow (OPF) problem with a Multi-Bilateral Economic Dispatch (MBED) formulation as an objective function. The solution is realized in a fully decentralized manner on the basis of the Relaxed Consensus + Innovations (RCI) algorithm. Network security is ensured by a tariff-based system organized by a network agent that makes use of product differentiation capabilities of the RCI algorithm. It is found that the proposed mechanism accurately finds and prevents hazardous network operations, such as over-voltage in grid buses, while successfully providing economic value to prosumers’ renewable generation within the scope of a P2P, free market. Full article
(This article belongs to the Special Issue Energy Management of Prosumer Communities)
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20 pages, 3957 KiB  
Article
Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing
by Sachin Kahawala, Daswin De Silva, Seppo Sierla, Damminda Alahakoon, Rashmika Nawaratne, Evgeny Osipov, Andrew Jennings and Valeriy Vyatkin
Energies 2021, 14(14), 4378; https://doi.org/10.3390/en14144378 - 20 Jul 2021
Cited by 7 | Viewed by 1894
Abstract
Real-time electricity pricing mechanisms are emerging as a key component of the smart grid. However, prior work has not fully addressed the challenges of multi-step prediction (Predicting multiple time steps into the future) that is accurate, robust and real-time. This paper proposes a [...] Read more.
Real-time electricity pricing mechanisms are emerging as a key component of the smart grid. However, prior work has not fully addressed the challenges of multi-step prediction (Predicting multiple time steps into the future) that is accurate, robust and real-time. This paper proposes a novel Artificial Intelligence-based approach, Robust Intelligent Price Prediction in Real-time (RIPPR), that overcomes these challenges. RIPPR utilizes Variational Mode Decomposition (VMD) to transform the spot price data stream into sub-series that are optimized for robustness using the particle swarm optimization (PSO) algorithm. These sub-series are inputted to a Random Vector Functional Link neural network algorithm for real-time multi-step prediction. A mirror extension removal of VMD, including continuous and discrete spaces in the PSO, is a further novel contribution that improves the effectiveness of RIPPR. The superiority of the proposed RIPPR is demonstrated using three empirical studies of multi-step price prediction of the Australian electricity market. Full article
(This article belongs to the Special Issue Energy Management of Prosumer Communities)
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24 pages, 6204 KiB  
Article
Intelligent Energy Management in a Prosumer Community Considering the Load Factor Enhancement
by Fernando V. Cerna, Mahdi Pourakbari-Kasmaei, Luizalba S. S. Pinheiro, Ehsan Naderi, Matti Lehtonen and Javier Contreras
Energies 2021, 14(12), 3624; https://doi.org/10.3390/en14123624 - 18 Jun 2021
Cited by 14 | Viewed by 1748
Abstract
In prosumers’ communities, the use of storage batteries (SBs) as support for photovoltaic (PV) sources combined with coordination in household appliances usage guarantees several gains. Although these technologies increase the reliability of the electricity supply, the large-scale use of home appliances in periods [...] Read more.
In prosumers’ communities, the use of storage batteries (SBs) as support for photovoltaic (PV) sources combined with coordination in household appliances usage guarantees several gains. Although these technologies increase the reliability of the electricity supply, the large-scale use of home appliances in periods of lower solar radiation and low electricity tariff can impair the performance of the electrical system. The appearance of new consumption peaks can lead to disturbances. Moreover, the repetition of these events in the short term can cause rapid fatigue of the assets. To address these concerns, this research proposes a mixed-integer linear programming (MILP) model aiming at the optimal operation of the SBs and the appliance usage of each prosumer, as well as a PV plant within a community to achieve the maximum load factor (LF) increase. Constraints related to the household appliances, including the electric vehicle (EV), shared PV plant, and the SBs, are considered. Uncertainties in consumption habits are simulated using a Monte Carlo algorithm. The proposed model was solved using the CPLEX solver. The effectiveness of our proposed model is evaluated with/without the LF improvement. Results corroborate the efficient performance of the proposed tool. Financial benefits are obtained for both prosumers and the energy company. Full article
(This article belongs to the Special Issue Energy Management of Prosumer Communities)
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21 pages, 7497 KiB  
Article
Implementation and Simulation of Real Load Shifting Scenarios Based on a Flexibility Price Market Strategy—The Italian Residential Sector as a Case Study
by Francesco Mancini, Jacopo Cimaglia, Gianluigi Lo Basso and Sabrina Romano
Energies 2021, 14(11), 3080; https://doi.org/10.3390/en14113080 - 25 May 2021
Cited by 9 | Viewed by 1912
Abstract
This work aims to evaluate the Flexibility Potential that a residential household can effectively provide to the public grid for participating in a Demand Response activity. In detail, by using 14 dwellings electrical data collection, an algorithm to simulate the Load Shifting activity [...] Read more.
This work aims to evaluate the Flexibility Potential that a residential household can effectively provide to the public grid for participating in a Demand Response activity. In detail, by using 14 dwellings electrical data collection, an algorithm to simulate the Load Shifting activity over the daytime is implemented. That algorithm is applied to different scenarios having considered the addition of several technical constraints on the end users’ devices. In such a way, more realistic demand-side management actions are implemented in order to assess the Flexibility Potential deriving from the loads shifting. Basically, by performing simulations it is possible to investigate how the household appliances real operating conditions can reduce the theoretical Flexibility Potential extent. Starting from a Flexibility Price-Market-based Strategy, this work simulates the shifting over the day and night-time of some flexible loads, i.e., the shiftable and the storable ones. Specifically, all instants where load curtailments and enhancements occur over the typical day, the flexibility strategy effectiveness in terms of percentage, the power and energy that are potentially flexible, are evaluated. All the simulations are performed only for residential consumers to evaluate the actual dwellings Flexibility Potential in the absence of any electrical storage and production systems. The outcomes of these simulations show an average Theoretical Flexibility reduction, which is calculated as the fraction of appliances’ cycles shifting over the total ones, equal to 53%, instead of 66%; in a single dwelling, a maximum variation equal to 29% has been registered. In the end, the monthly average shifted energy per dwellings decreases from 27 to 18 kWh, entailing 32.5% off. Full article
(This article belongs to the Special Issue Energy Management of Prosumer Communities)
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20 pages, 7715 KiB  
Article
Optimizing Power and Heat Sector Coupling for the Implementation of Carbon-Free Communities
by Arslan Ahmad Bashir, Andreas Lund, Mahdi Pourakbari-Kasmaei and Matti Lehtonen
Energies 2021, 14(7), 1911; https://doi.org/10.3390/en14071911 - 30 Mar 2021
Cited by 6 | Viewed by 1965
Abstract
To achieve a successful integration of fluctuating renewable power generation, the power-to-heat (P2H) conversion is seen as an efficient solution that remedies the issue of curtailments as well as reduces carbon emissions prevailing in the district heating (DH) sector. Concurrently, the need for [...] Read more.
To achieve a successful integration of fluctuating renewable power generation, the power-to-heat (P2H) conversion is seen as an efficient solution that remedies the issue of curtailments as well as reduces carbon emissions prevailing in the district heating (DH) sector. Concurrently, the need for storage is also increasing to maintain a continuous power supply. Hence, this paper presents a MILP-based model to optimize the size of thermal storage required to satisfy the annual DH demand of a community solely by P2H conversion employing renewable energy. The DH is supplied by the optimal operation of a novel 2-km deep well heat pump system (DWHP) equipped with thermal storage. To avoid computational intractability, representative time steps with varying time duration are chosen by employing hierarchical agglomerative clustering that aggregates adjacent hours chronologically. The value of demand response and the effect of interannual weather variability are also analyzed. Numerical results from a Finnish case study show that P2H conversion utilizing small thermal storage in tandem with the DWHP is able to cover the annual DH demand, thus leading to a carbon-neutral DH system and, at the same time, mitigating the curtailment of excessive wind generation. Compared with the annual DH demand, an average thermal storage size of 29.17 MWh (2.58%) and 13.99 MWh (1.24%) are required in the business-as-usual and the demand response cases, respectively. Full article
(This article belongs to the Special Issue Energy Management of Prosumer Communities)
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