• Energy Research

Despite the significant merits of plugin electric vehicles (PEVs) and fuel cell electric vehicles (FCEVs), their enormous rise has posed serious challenges in terms of energy supply. This research proposes a sustainable energy platform to integrate PEVs and FCEVs under the concept of microgrid (MG) with power and green hydrogen as energy carriers, so-called integrated power, and hydrogen MG (IPHMG). In addition, a decentralized market clearing model is developed to enable the nearby IPHMG to interact in a local energy market (LEM). The LEM is run by a central manager for both power and hydrogen. To do so, the mobile edge computing (MEC) system is proposed to move the computation from a central cloud server to decentralized edge servers, causing to reducing the computational burden and increasing data security and privacy. Moreover, the fast alternating direction method of multipliers (fast-ADMM) is employed to decompose the problem. Further, this work advances the state-of-the-art in uncertainty management by introducing a tri-level uncertainty controlling approach to fully consider the uncertain parameters. According to the obtained results, the community of IPHMGs could reduce 5.86% of total cost by trading energy in the LEM and the presented hybrid uncertainty management model could protect the community against uncertainties by reducing the total cost by 8.08%.

Despite the significant merits of plugin electric vehicles (PEVs) and fuel cell electric vehicles (FCEVs), their enormous rise has posed serious challenges in terms of energy supply. This research proposes a sustainable energy platform to integrate PEVs and FCEVs under the concept of microgrid (MG) with power and green hydrogen as energy carriers, so-called integrated power, and hydrogen MG (IPHMG). In addition, a decentralized market clearing model is developed to enable the nearby IPHMG to interact in a local energy market (LEM). The LEM is run by a central manager for both power and hydrogen. To do so, the mobile edge computing (MEC) system is proposed to move the computation from a central cloud server to decentralized edge servers, causing to reducing the computational burden and increasing data security and privacy. Moreover, the fast alternating direction method of multipliers (fast-ADMM) is employed to decompose the problem. Further, this work advances the state-of-the-art in uncertainty management by introducing a tri-level uncertainty controlling approach to fully consider the uncertain parameters. According to the obtained results, the community of IPHMGs could reduce 5.86% of total cost by trading energy in the LEM and the presented hybrid uncertainty management model could protect the community against uncertainties by reducing the total cost by 8.08%.

In a power system with high penetration of renewable power sources, gas-fired units can be considered as a back-up option to improve the balance between generation and consumption in short-term scheduling. Therefore, closer coordination between power and natural gas systems is anticipated. This article presents a novel hybrid information gap decision theory (IGDT)-stochastic cooptimization problem for integrating electricity and natural gas networks to minimize total operation cost with the penetration of wind energy. The proposed model considers not only the uncertainties regarding electrical load demand and wind power output, but also the uncertainties of gas load demands for the residential consumers. The uncertainties of electric load and wind power are handled through a scenario-based approach, and residential gas load uncertainty is handled via IGDT approach with no need for the probability density function. The introduced hybrid model enables the system operator to consider the advantages of both approaches simultaneously. The impact of gas load uncertainty associated with the residential consumers is more significant on the power dispatch of gas-fired plants and power system operation cost since residential gas load demands are prior than gas load demands of gas-fired units. The proposed framework is a bilevel problem that can be reduced to a one-level problem. Also, it can be solved by the implementation of a simple concept without the need for Karush–Kuhn–Tucker conditions. Moreover, emerging flexible energy sources such as the power to gas technology and demand response program are considered in the proposed model for increasing the wind power dispatch, decreasing the total operation cost of the integrated network as well as reducing the effect of system uncertainties on the total operating cost. Numerical results indicate the applicability and effectiveness of the proposed model under different working conditions.

In a power system with high penetration of renewable power sources, gas-fired units can be considered as a back-up option to improve the balance between generation and consumption in short-term scheduling. Therefore, closer coordination between power and natural gas systems is anticipated. This article presents a novel hybrid information gap decision theory (IGDT)-stochastic cooptimization problem for integrating electricity and natural gas networks to minimize total operation cost with the penetration of wind energy. The proposed model considers not only the uncertainties regarding electrical load demand and wind power output, but also the uncertainties of gas load demands for the residential consumers. The uncertainties of electric load and wind power are handled through a scenario-based approach, and residential gas load uncertainty is handled via IGDT approach with no need for the probability density function. The introduced hybrid model enables the system operator to consider the advantages of both approaches simultaneously. The impact of gas load uncertainty associated with the residential consumers is more significant on the power dispatch of gas-fired plants and power system operation cost since residential gas load demands are prior than gas load demands of gas-fired units. The proposed framework is a bilevel problem that can be reduced to a one-level problem. Also, it can be solved by the implementation of a simple concept without the need for Karush–Kuhn–Tucker conditions. Moreover, emerging flexible energy sources such as the power to gas technology and demand response program are considered in the proposed model for increasing the wind power dispatch, decreasing the total operation cost of the integrated network as well as reducing the effect of system uncertainties on the total operating cost. Numerical results indicate the applicability and effectiveness of the proposed model under different working conditions.

In this study, a general framework for implementing a retail energy market based on the Nikaido–Isoda/relaxation algorithm is proposed as an electricity market structure with large distributed energy resources (DERs) penetration and demand side management of consumers. Moreover, the consumers are able to participate in the market as prosumers (i.e. producer and consumer at the same time). By considering the related uncertainties, the DERs can maximise their expected payoff or profit by undertaking strategies through the price bidding strategy, based on the proposed structure, considering Nash equilibrium. The results show the effectiveness and accuracy of the proposed framework in determining the optimal power set‐points of players participating in the market to achieve the objectives.

In this study, a general framework for implementing a retail energy market based on the Nikaido–Isoda/relaxation algorithm is proposed as an electricity market structure with large distributed energy resources (DERs) penetration and demand side management of consumers. Moreover, the consumers are able to participate in the market as prosumers (i.e. producer and consumer at the same time). By considering the related uncertainties, the DERs can maximise their expected payoff or profit by undertaking strategies through the price bidding strategy, based on the proposed structure, considering Nash equilibrium. The results show the effectiveness and accuracy of the proposed framework in determining the optimal power set‐points of players participating in the market to achieve the objectives.

With the increasing penetration of renewable energy sources (RESs), the necessity for employing smart methods to control and manage energy has become undeniable. This study introduces a real-time energy management system based on a multi-agent system supervised by a smart contract, employing a bottom-up approach for a grid-connected DC micro-grid equipped with solar photovoltaic panels (PV), wind turbines (WT), microturbines (MT), and battery energy storage (BES). Each unit is controlled and managed through a distributed decision-making structure. The BES agent is governed by an intelligent structure based on a reinforcement learning model. To facilitate interaction and coordination among agents, a tendering process is employed, where each agent, under its supervised control structure, presents its offer for the tendered item at each time period. The tendering organization allocates demand using the first-price sealed-bid algorithm among bidders to optimize energy cost in the Microgrid. The proposed approach offers a real-time intelligent system capable of ensuring fault tolerance, stability, and reliability in the Microgrid. The main achievement of this study is the development of a robust real-time energy management system that integrates various renewable energy sources and battery storage while ensuring efficient operation and resilience in the face of faults or disruptions. This work was supported by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya under 2021 SGR 00392. In addition, This research work was funded by Institutional Fund Projects under grant no. (IFPIP: 1217- 135-1443). The authors gratefully acknowledge technical and financial support provided by the Ministry of Education and King Abdulaziz University (KAU), DSR, Jeddah, Saudi Arabia. Peer Reviewed