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1 |
An internet of energy framework with distributed energy resources, prosumers and small-scale virtual power plants: An overview
اینترنت چارچوب انرژی با منابع انرژی توزیع شده ، پیشرانها و نیروگاه های مجازی در مقیاس کوچک: یک مرور کلی-2020 Current power networks and consumers are undergoing a fundamental shift in the way traditional energy systems
were designed and managed. The bidirectional peer-to-peer (P–P) energy transactions pushed passive
consumers to be prosumers. The future smart grid or the internet of energy (IoE) will facilitate the coordination
of all types of prosumers to form virtual power plants (VPP). The paper aims to contribute to this growing area of
research by accumulating and summarizing the significant ideas of the integration of distributed prosumers and
small-scale VPP to the internet of energy (IoE). The study also reports the characteristics of IoE in comparison to
the traditional grid and offers some valuable insights into the control, management and optimization strategies of
prosumers, distributed energy resources (DERs) and VPP. As bidirectional P–P energy transaction by the prosumers
is a crucial element of IoE, their management strategies including various demand-response approach at
the customers’-levels are systematically summarized. The integration of DERs and prosumers to the VPP
considering their functions, infrastructure, type, control objectives are also reviewed and summarized. Various
optimization techniques and algorithm, and their objectives functions and the types of mathematical formulation
that are used to manage the DERs and VPP are discussed and categorized systematically. Finally, the factors
which affect the integration of DERs and prosumers to the VPP are identified. Keywords: Bidirectional energy transactions | Distributed energy resources | Energy management | Internet of energy | Optimization techniques | Prosumers | Virtual power plant |
مقاله انگلیسی |
2 |
A quasi-optimal energy resources management technique for low voltage microgrids
یک روش مدیریت انرژی شبه بهینه برای ریز شبکه های ولتاژ پایین-2020 The increasing penetration of Distributed Energy Resources (DERs) in modern power systems is introducing new
challenges for system stability and control. The stochasticity of Renewable Energy Sources (RESs) and the unpredictable
behaviour of demand, make it necessary to develop intelligent Energy Resources Management (ERM)
methodology, able to actively regulate the actors of the electrical network. This paper proposes a new quasioptimal
control algorithm, designed for LV breaker devices, aiming to manage DERs in a distributed and scalable
approach. The developed ERM technique is able: (1) to control the energy consumption at the Point of Common
Coupling (PCC) (with the possibility to join Active Demand Response (ADR) programs or to provide flexibility/
energy reserve), when the microgrid itself is connected to the main grid; (2) to control the frequency profile
when the microgrid is operating in islanded mode, using information provided by real time measures. Keywords: Energy resources management | Distributed energy resources | Microgrid | Microgrid optimization | Demand response | Flexibility provision | Frequency control |
مقاله انگلیسی |
3 |
A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading
یک سیستم مدیریت انرژی مسکونی با استراتژی مناقصه مبتنی بر بهینه سازی دو سطح برای تجارت برق دو طرفه پیش رو-2020 Bi-directional electricity trading of demand response (DR) and transactive energy (TE) frameworks allows the
traditionally passive end-users of electricity to play an active role in the local power balance of the grid.
Appropriate building energy management systems (BEMSs), coupled with an optimized bidding strategy, can
provide significant cost savings for prosumers (consumers with on-site power generation and/or storage facility)
when they participate in such bi-directional trading. This paper presents a BEMS with an optimization-based
scheduling and bidding strategy for small-scale residential prosumers to determine optimal day-ahead energyquantity
bids considering the expected cost of real-time imbalance trading under uncertainty. The proposed
scheduling and bidding strategy is formulated as a stochastic bi-level minimization problem that determines the
day-ahead energy-quantity bids by minimizing the energy cost in the upper level considering expected cost of
uncertainty, whereas a number of lower-level sub-problems ensure optimal operation of building loads and
distributed energy resources (DERs) for comfort reservation, minimization of consumers’ inconveniences and
degradation of residential storage units. A modified decomposition method is used to reformulate the nonlinear
bi-level problem as a mixed-integer linear programming (MILP) problem and solved using ‘of the shelf’ commercial
software. The effectiveness of the proposed BEMS model is verified via case studies for a residential
prosumer in Sydney, Australia with real measurement data for building energy demand. The efficacy of the
proposed method for overall financial savings is also validated by comparing its performance with state-of-theart
day-ahead scheduling strategies. Case studies indicate that the proposed method can provide up to 51% and
22% cost savings compared to inflexible non-optimal scheduling strategies and deterministic optimization-based
methods respectively. Results also indicate that the proposed method offers better economic performance than
standard cost minimization models and multi-objective methods for simultaneous minimization of energy cost
and user inconveniences. Keywords: Demand response | Building energy management system | Distributed energy resources | Mixed-integer programming | Bi-level optimization |
مقاله انگلیسی |
4 |
A distributed Peer-to-Peer energy transaction method for diversified prosumers in Urban Community Microgrid System
روش توزیع انرژی همتا به همتا برای پیشرانهای متنوع در سیستم ریز شبکه جامعه شهری توزیع شده -2020 As massive integration of Distributed Energy Resources (DERs), the role of end-users in the Urban Community
Microgrid System (UCMS) has transformed from traditional consumers into prosumers with capabilities of both
energy production and consumption. The exchange of energy between autonomous microgrid prosumers can be
achieved with the introduction of Peer-to-Peer (P2P) energy transaction, promoting the efficient allocation of
energy in the UCMS. However, the existing centralized P2P energy transaction approaches require microgrid
transaction brokers to obtain prosumers’ private data, including energy resource configuration, operation status,
and energy production/consumption schedule. With the enhancement of prosumers’ awareness of privacy
protection, it will be increasingly more difficult for the brokers to obtain such private data in practical application
scenarios, resulting in obstacles on the implementation of such centralized approach. Thus, a novel
distributed P2P energy transaction method based on the double auction market is proposed in this paper.
Prosumers first generate the information of energy supply and demand autonomously utilizing distributed energy
management model, then set the price targeting profit maximization, and finally initiate P2P energy
transaction mutually in the double auction energy market. Compared with the existing centralized approaches,
the method proposed in this paper can achieve the coordination and complementarity of energy in the UCMS,
promoting economic benefit, energy self-sufficiency, and renewable energy self-consumption without sacrificing
privacy preservation and robustness. Keywords: Urban Community Microgrid System | Distributed Peer-to-Peer (P2P) energy | transaction | Autonomous energy management | Autonomous pricing | Supply-demand coordination |
مقاله انگلیسی |
5 |
An integrated blockchain-based energy management platform with bilateral trading for microgrid communities
یک پلت فرم یکپارچه مدیریت انرژی مبتنی بر بلاکچین با تجارت دوجانبه برای جوامع ریز شبکه -2020 In this paper, an integrated blockchain-based energy management platform is proposed that optimizes energy
flows in a microgrid whilst implementing a bilateral trading mechanism. Physical constraints in the microgrid
are respected by formulating an Optimal Power Flow (OPF) problem, which is combined with a bilateral trading
mechanism in a single optimization problem. The Alternating Direction Method of Multipliers (ADMM) is used to
decompose the problem to enable distributed optimization and a smart contract is used as a virtual aggregator.
This eliminates the need for a third-party coordinating entity. The smart contract fulfills several functions,
including distribution of data to all participants and executing part of the ADMM algorithm. The model is run
using actual data from a prosumer community in Amsterdam and several scenarios of the model are tested to
evaluate the impact of combining physical constraints and trading on social welfare of the community and
scheduling of energy flows. The scenario variants are trade-only, where only a trading mechanism is implemented,
grid-only where only OPF optimization is implemented and a combined scenario where both are
implemented. Results are compared with a baseline scenario. Simulation results show that import costs of the
whole community are reduced by 34.9% as compared to a baseline scenario, and total energy import quantities
are reduced by 15%. Total social welfare is found to be highest without a trading mechanism, however this
platform is only viable when all costs are equally shared between all households. Furthermore, peak imports are
reduced by over 50% in scenarios including grid constraints. Keywords: Microgrids | Distributed energy resources | Decentralized optimization | Optimal power flow | Local electricity markets | Blockchain | Smart contracts |
مقاله انگلیسی |
6 |
Reinforcement learning for whole-building HVAC control and demand response
یادگیری تقویتی برای کنترل HVAC کل و پاسخ به تقاضا-2020 This paper proposes a novel reinforcement learning (RL) architecture for the efficient scheduling and control of the heating, ventilation and air conditioning (HVAC) system in a commercial building while harnessing its de- mand response (DR) potentials. With advances in automated building management systems, this can be achieved seamlessly by a smart autonomous RL agent which takes the best action, for example, a change in HVAC temper- ature set point, necessary to change the electricity usage pattern of a building in response to demand response signals, and with minimal thermal comfort impact to customers. Previous research in this area has tackled only individual aspects of the problem using RL. Specifically, due to the challenges in implementing demand response with whole-building models, simpler analytical models which poorly capture reality have been used instead. And where whole-building models are applied, RL is used for HVAC control mainly to achieve energy efficiency goals while demand response is neglected. Thus, in this research, we implement a holistic framework by designing an efficient RL controller for a whole-building model which learns to optimise and control the HVAC system for improved energy efficiency and thermal comfort levels in addition to achieving demand response goals. Our simulation results show that by applying reinforcement learning for normal HVAC operation, a maximum weekly energy reduction of up to 22% can be achieved compared to a handcrafted baseline controller. Furthermore, by employing a DR-aware RL controller during demand response periods, average power reductions or increases of up to 50% can be achieved on a weekly basis compared to the default RL controller, while keeping occupant thermal comfort levels within acceptable bounds. Keywords: Demand response | Reinforcement learning | Whole-building HVAC control | Distributed energy resources | Optimal HVAC energy scheduling |
مقاله انگلیسی |
7 |
Decentralized multi-agent based energy management of microgrid using reinforcement learning
مدیریت انرژی مبتنی بر چند عامل غیرمتمرکز بر روی ریز شبکه با استفاده از یادگیری تقویتی-2020 This paper proposes a multi-agent based decentralized energy management approach in a grid-connected microgrid
(MG). The MG comprises of wind and photovoltaic resources, diesel generator, electrical energy storage,
and combined heat and power generations to serve electrical and thermal loads at the lower-level of energy
management system (EMS). All distributed energy resources (DERs) and customers are modelled as self-interested
agents who adopt reinforcement learning to optimize their behaviours and operation costs. Based on this
algorithm, agents have the capability to interact with each other in a distributed manner and find the best
strategy in competitive environment. At the upper-level of EMS, there is an energy management agent that
gathers the information of agents of lower-level and clears the MG electrical and thermal energy market in line
with predetermined goals. Utilizing energy availability from different DERs and variety of customers’ consumption
patterns, considering uncertainty of renewable generation and load consumption and taking into account
technical constraint of DERs are the strengths of the presented framework. Performance of the proposed
algorithm is investigated under different conditions of agents learning and using ε-greedy, soft-max and upper
confidence bound methods. The simulation results verify efficacy of the proposed approach. Keywords: Distributed energy resources | Microgrid energy management system | Multi-agent systems | Reinforcement learning |
مقاله انگلیسی |
8 |
AI protection Algorithms for PV-Grid Connection System
الگوریتم های محافظت از هوش مصنوعی برای سیستم اتصال PV-Grid-2020 This paper shows the impact of islanding
phenomenon in case of Grid-Connected Photovoltaic (PV)
Arrays and how to develop some new techniques to detect this
phenomenon. Photovoltaic (PV) is one of the popular choice
among the DGs, which typically establishes in grid-connected
systems. However, for grid-connected systems, the issue of
unintentional islanding remains as a great challenge. In order
to enhance the PV grid connected technologies, the
phenomenon of unintentional islanding is typically avoided by
anti-islanding detection controller. This paper presents the
comparative study of anti-islanding detection techniques which
include passive and active techniques. The principle of
operation for both the passive and active anti-islanding
detection techniques, namely Voltage and frequency Protection
(OVP/UVP and OFP/UFP) and Active Frequency Drift (AFD)
are described. In this work, the performances of the studied
anti-islanding techniques are simulated using
MATLAB/Simulink package. Finally, the results of simulation
show that the passive technique hasnt effect the power quality
of system like active one , but unfortunately it has non
detection zones that isnt found in active technique. Keywords: PV-Grid system | Islanding detection | Distributed energy | ROCOF | Anti-islanding methods | Active method | Voltage and frequency protection |
مقاله انگلیسی |
9 |
Smart contract architecture for decentralized energy trading and management based on blockchains
معماری قرارداد هوشمند برای تجارت و مدیریت انرژی غیر متمرکز بر اساس بلاکچین -2020 A blockchain-based smart contract has the potential to allow the performance of credible transactions
without third parties. This paper presents a universal framework for a blockchain platform that enables
peer-to-peer (P2P) energy trading in the retail electricity market. Focusing attention on seeking energymatching
pairs from the supply and demand sides, and encouraging direct energy trading between
producers and consumers, the P2P energy trading mechanism is proposed. The designed multidimensional
blockchain platform implements a complete energy trading process. As smart contracts strictly
execute the trading and payment rules without human interaction, the security and fairness of energy
trading are significantly enhanced. Case studies in the Ethereum private chain demonstrate that the
proposed mechanism has obvious advantages in reflecting market quotations, balancing profits of
players, and facilitating the utilization of renewables. Based on such characteristics, players are incentivized
to participate in the P2P energy trading. Moreover, the authentic gas consumption and computational
time to the smart contract indicate that this platform is able to achieve an efficient and effective
transaction with multi-player participation. Keywords: Peer-to-Peer energy trading | Distributed energy resources | Double auction | Blockchain | Smart contract |
مقاله انگلیسی |
10 |
Introducing reinforcement learning to the energy system design process
معرفی یادگیری تقویتی به فرآیند طراحی سیستم انرژی-2020 Design optimization of distributed energy systems has become an interest of a wider group of researchers due the
capability of these systems to integrate non-dispatchable renewable energy technologies such as solar PV and
wind. White box models, using linear and mixed integer linear programing techniques, are often used in their
design. However, the increased complexity of energy flow (especially due to cyber-physical interactions) and
uncertainties challenge the application of white box models. This is where data driven methodologies become
effective, as they demonstrate higher flexibility to adapt to different environments, which enables their use for
energy planning at regional and national scale.
This study introduces a data driven approach based on reinforcement learning to design distributed energy
systems. Two different neural network architectures are used in this work, i.e. a fully connected neural network
and a convolutional neural network (CNN). The novel approach introduced is benchmarked using a grey box
model based on fuzzy logic. The grey box model showed a better performance when optimizing simplified
energy systems, however it fails to handle complex energy flows within the energy system. Reinforcement
learning based on fully connected architecture outperformed the grey box model by improving the objective
function values by 60%. Reinforcement learning based on CNN improved the objective function values further
(by up to 20% when compared to a fully connected architecture). The results reveal that data-driven models are
capable to conduct design optimization of complex energy systems. This opens a new pathway in designing
distributed energy systems. Keywords: Distributed energy systems | Energy hubs | Reinforcement learning | Optimization | Data driven models | Machine learning |
مقاله انگلیسی |