JA
José A. Aguado
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4 records found
1
Integrating energy communities with electric vehicle fleets into flexibility markets
A privacy-preserving hierarchical optimization model
The increasing penetration of renewable energy and electric vehicle (EV) fleets intensifies the need for system flexibility to ensure reliable and economical operation. Energy communities (ECs), equipped with distributed energy resources (DERs), thermostatically controlled loads (TCLs), and vehicle-to-grid (V2G) enabled EV fleets, represent a promising source of distributed flexibility. However, fully harnessing this potential requires coordinated interaction across multiple system layers while preserving the autonomy of local actors. Hence, this paper proposes a tri-level hierarchical optimization framework that coordinates the operation of ECs, distribution system operators (DSOs), and transmission system operators (TSOs) in day-ahead energy and intra-day flexibility markets. The proposed framework preserves privacy in an operational sense by limiting information exchange to boundary variables, while explicitly capturing the contribution of EV fleets alongside TCLs, batteries, and renewable generation. Case studies on a coupled 57-bus transmission and 33-node distribution system with ten ECs demonstrate that EV fleets significantly enhance local resilience and cost efficiency by reducing reliance on transmission-level reserves. Results across six case studies show that fully activating distributed flexibility, including EV fleets, reduces DSO operational costs by 13.13% and lowers total system flexibility expenditures, while shifting the provision of services from centralized units to decentralized resources. The findings highlight EV fleets as a cornerstone of distributed flexibility and confirm the effectiveness of the proposed three-level hierarchical market coordination framework in renewable-rich power systems.
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The increasing penetration of renewable energy and electric vehicle (EV) fleets intensifies the need for system flexibility to ensure reliable and economical operation. Energy communities (ECs), equipped with distributed energy resources (DERs), thermostatically controlled loads (TCLs), and vehicle-to-grid (V2G) enabled EV fleets, represent a promising source of distributed flexibility. However, fully harnessing this potential requires coordinated interaction across multiple system layers while preserving the autonomy of local actors. Hence, this paper proposes a tri-level hierarchical optimization framework that coordinates the operation of ECs, distribution system operators (DSOs), and transmission system operators (TSOs) in day-ahead energy and intra-day flexibility markets. The proposed framework preserves privacy in an operational sense by limiting information exchange to boundary variables, while explicitly capturing the contribution of EV fleets alongside TCLs, batteries, and renewable generation. Case studies on a coupled 57-bus transmission and 33-node distribution system with ten ECs demonstrate that EV fleets significantly enhance local resilience and cost efficiency by reducing reliance on transmission-level reserves. Results across six case studies show that fully activating distributed flexibility, including EV fleets, reduces DSO operational costs by 13.13% and lowers total system flexibility expenditures, while shifting the provision of services from centralized units to decentralized resources. The findings highlight EV fleets as a cornerstone of distributed flexibility and confirm the effectiveness of the proposed three-level hierarchical market coordination framework in renewable-rich power systems.
Internet data centers and industrial parks as flexibility providers in modern power systems
An ADMM-based coordination mechanism
Journal article
(2026)
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Seyed Amir Mansouri, Emad Nematbakhsh, Andrés Ramos, Jose Pablo Chaves-Avila, Javier García-González, José A. Aguado
Smart prosumers with Distributed Generation (DGs) and controllable loads can provide cost-effective grid services. However, realizing this potential requires distributed optimization mechanisms that ensure market efficiency, participant privacy, and compliance with electricity market regulations. This paper presents a bi-level distributed optimization mechanism to maximize flexibility services from industrial parks and Internet Data Centers (IDCs) in distribution-level Congestion Management (CM) markets. The upper-level models the Distribution System Operator (DSO), which identifies congested lines using linear AC power flow analysis on pre-settled energy market results and sends corrective signals to prosumers. The lower level allows prosumers to adjust their operations accordingly and communicate updated transactions back to the DSO. A novel proxy-driven algorithm is proposed to facilitate service-sharing among geo-distributed IDCs, considering congestion issues. Additionally, an adaptive Alternating Direction Method of Multipliers (ADMM) algorithm enables decentralized coordination among market agents, achieving 74.52 % faster convergence than the standard ADMM. A real-world case study from Spain demonstrates that the proposed mechanism enables the grid operator to maximize grid services from prosumers, reducing congestion alleviation costs by 35.27 %. Moreover, IDCs reduced daily costs by 11.07 % through service-sharing and task-shifting aligned with CM market signals, while industrial parks achieved a 13.68 % cost reduction by aligning material production processes with CM market signals, both enabled by the proposed bi-level mechanism.
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Smart prosumers with Distributed Generation (DGs) and controllable loads can provide cost-effective grid services. However, realizing this potential requires distributed optimization mechanisms that ensure market efficiency, participant privacy, and compliance with electricity market regulations. This paper presents a bi-level distributed optimization mechanism to maximize flexibility services from industrial parks and Internet Data Centers (IDCs) in distribution-level Congestion Management (CM) markets. The upper-level models the Distribution System Operator (DSO), which identifies congested lines using linear AC power flow analysis on pre-settled energy market results and sends corrective signals to prosumers. The lower level allows prosumers to adjust their operations accordingly and communicate updated transactions back to the DSO. A novel proxy-driven algorithm is proposed to facilitate service-sharing among geo-distributed IDCs, considering congestion issues. Additionally, an adaptive Alternating Direction Method of Multipliers (ADMM) algorithm enables decentralized coordination among market agents, achieving 74.52 % faster convergence than the standard ADMM. A real-world case study from Spain demonstrates that the proposed mechanism enables the grid operator to maximize grid services from prosumers, reducing congestion alleviation costs by 35.27 %. Moreover, IDCs reduced daily costs by 11.07 % through service-sharing and task-shifting aligned with CM market signals, while industrial parks achieved a 13.68 % cost reduction by aligning material production processes with CM market signals, both enabled by the proposed bi-level mechanism.
Journal article
(2026)
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Mohammad Nasir, José A. Aguado, Sebastian Martin, Seyed Amir Mansouri, Pedro Rodríguez
Coordinated operation of Smart Buildings (SBs), Energy Communities (ECs), and Distribution Systems (DSs) requires efficient market structures that preserve the privacy of participants while considering risks introduced by uncertain demand, prices, and renewable generation. Therefore, this paper proposes a decentralized risk-aware tri-level optimization framework that integrates Renewable Energy Resources (RERs) such as Photovoltaic (PV) and Wind Turbine (WT), Vehicle-to-Grid (V2G) enable Electric Vehicles (EVs) parking lots, Energy storage systems (ESSs) and Flexible Loads (FLs), enabling privacy-preserving and hierarchical scheduling across SBs, ECs, and the DS while managing uncertainties. The levels are solved sequentially, one optimization problem for each level, the results of one level feed into the problem of the next level. SBs perform day-ahead scheduling to minimize electricity costs in the first level. At the second level, ECs aggregate SBs schedules and operate in a decentralized framework. At the third level, the Distribution System Operator (DSO) integrates EC schedules into day-ahead operational planning. The risk-averse scheduling approach employs Conditional Value-at-Risk (CVaR) as a risk metric to manage the risk arising from uncertainties on generation, demand and price. The model is formulated as a Mixed-Integer Linear Programming (MILP) problem and is tested on an IEEE 33-bus distribution network under two modes: deterministic (just a single scenario) and stochastic (several scenarios at the same time). The simulation results indicate that the proposed framework can reduce SBs operation costs by up to 45.65% and increases ECs profit by 21.8% under uncertainty.
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Coordinated operation of Smart Buildings (SBs), Energy Communities (ECs), and Distribution Systems (DSs) requires efficient market structures that preserve the privacy of participants while considering risks introduced by uncertain demand, prices, and renewable generation. Therefore, this paper proposes a decentralized risk-aware tri-level optimization framework that integrates Renewable Energy Resources (RERs) such as Photovoltaic (PV) and Wind Turbine (WT), Vehicle-to-Grid (V2G) enable Electric Vehicles (EVs) parking lots, Energy storage systems (ESSs) and Flexible Loads (FLs), enabling privacy-preserving and hierarchical scheduling across SBs, ECs, and the DS while managing uncertainties. The levels are solved sequentially, one optimization problem for each level, the results of one level feed into the problem of the next level. SBs perform day-ahead scheduling to minimize electricity costs in the first level. At the second level, ECs aggregate SBs schedules and operate in a decentralized framework. At the third level, the Distribution System Operator (DSO) integrates EC schedules into day-ahead operational planning. The risk-averse scheduling approach employs Conditional Value-at-Risk (CVaR) as a risk metric to manage the risk arising from uncertainties on generation, demand and price. The model is formulated as a Mixed-Integer Linear Programming (MILP) problem and is tested on an IEEE 33-bus distribution network under two modes: deterministic (just a single scenario) and stochastic (several scenarios at the same time). The simulation results indicate that the proposed framework can reduce SBs operation costs by up to 45.65% and increases ECs profit by 21.8% under uncertainty.
Journal article
(2025)
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Seyed Amir Mansouri, Andrés Ramos, José Pablo Chaves Ávila, Javier García-González, José A. Aguado
This article presents a four-level hierarchical model to incorporate decentralized energy communities (ECs) into local electricity markets. The model utilizes an innovative distribution system operator (DSO)-driven algorithm to maximize grid services from ECs, monetize their energy surplus, and adapt market exchanges to network security constraints. In level 1, EC members determine their internal scheduling and power exchanges. A decentralized peer-to-peer (P2P) structure embedded in level 1 enables power sharing with dynamic pricing and limited data sharing among EC members. Levels 2 and 3 involve the EC operators and the retailer company determining their market strategies. In level 4, a DSO-driven algorithm is deployed to evaluate security constraints and the feasibility of exchanges between market players. Implemented on a modified 594-node distribution network in Victoria, Australia, the model optimally integrates ECs with local electricity markets. By preserving agents' privacy and keeping exchange details confidential, the proposed model ensures next-day contracts adhere to network security restrictions, maximizes grid services from ECs, and reduces members' electricity bills by 7.5%.
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This article presents a four-level hierarchical model to incorporate decentralized energy communities (ECs) into local electricity markets. The model utilizes an innovative distribution system operator (DSO)-driven algorithm to maximize grid services from ECs, monetize their energy surplus, and adapt market exchanges to network security constraints. In level 1, EC members determine their internal scheduling and power exchanges. A decentralized peer-to-peer (P2P) structure embedded in level 1 enables power sharing with dynamic pricing and limited data sharing among EC members. Levels 2 and 3 involve the EC operators and the retailer company determining their market strategies. In level 4, a DSO-driven algorithm is deployed to evaluate security constraints and the feasibility of exchanges between market players. Implemented on a modified 594-node distribution network in Victoria, Australia, the model optimally integrates ECs with local electricity markets. By preserving agents' privacy and keeping exchange details confidential, the proposed model ensures next-day contracts adhere to network security restrictions, maximizes grid services from ECs, and reduces members' electricity bills by 7.5%.