Real-time management of distributed multi-energy resources in multi-energy networks

Coelho, António Manuel Flores Romão de Azevedo Gonçalves; Iria, José; Soares, Filipe; Lopes, Jorge

doi:10.1016/j.segan.2023.101022

Cited by 17 publications

(11 citation statements)

References 28 publications

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“…The microgrid system comprises wind turbines (W), photovoltaic devices (P), gas turbines (G), and energy storage (E). Power network constraints [24]: The power grid is modeled using the non-convex formulation of the branch flow model. Constraints (67)-(70) represent the branch power flow equations.…”

Section: Case Study and Resultsmentioning

confidence: 99%

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Robust Collaborative Scheduling Strategy for Multi-Microgrids of Renewable Energy Based on a Non-Cooperative Game and Profit Allocation Mechanism

Gao,

Zhang

2024

Energies

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The Multiple Microgrid System (MMG) facilitates synergistic complementarity among various energy sources, reduces carbon emissions, and promotes the integration of renewable energy generation. In this context, we propose a two-stage robust cooperative scheduling model for MMGs based on non-cooperative game theory and a benefit allocation mechanism. In the first stage, considering electricity price fluctuations and uncertainties in wind and solar power outputs, a robust optimization approach is applied to establish an electric energy management model for MMGs. This model enables point-to-point energy sharing among microgrids. In the second stage, addressing the benefit allocation problem for shared electric energy, we introduce a Cost Reduction Ratio Distribution (CRRD) model based on non-cooperative game theory. The generalized Nash equilibrium is utilized to determine the benefit distribution for shared electric energy. Finally, through case studies, the proposed model is validated, ensuring fair returns for each microgrid. The results indicate that the proposed model optimizes the operational states of individual microgrids, reduces operational costs for each microgrid, and lowers the overall total operational costs of the MMG system. Additionally, an investigation is conducted into the impact of electricity price uncertainty coefficients and confidence levels of wind and solar uncertainties on the operational costs of microgrids.

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Section: Case Study and Resultsmentioning

confidence: 99%

“…Constraints ( 71) and ( 72) impose square limits on the voltage and current magnitudes. Power network constraints [24]: The power grid is modeled using the non-convex formulation of the branch flow model. Constraints (67)-(70) represent the branch power flow equations.…”

Section: Case Study and Resultsmentioning

confidence: 99%

Robust Collaborative Scheduling Strategy for Multi-Microgrids of Renewable Energy Based on a Non-Cooperative Game and Profit Allocation Mechanism

Gao,

Zhang

2024

Energies

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“…The stochastic model is proposed on the basis of the energy market facing stochastic demands with varied prices. A network optimization mechanism [40,41] is applied in the proposed model under an uncertain environment. The network optimization involves stochastic programming formulation to capture the randomness of the unknown demand.…”

Section: Contributions and Advantages Of The Proposed Modelmentioning

confidence: 99%

A Robust Optimization Approach for Smart Energy Market Revenue Management

Zhang,

Sun,

Yang

et al. 2023

Energies

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We propose a network optimization model for smart energy market management in the context of an uncertain environment. The network optimization considers the stochastic programming approach to capture the randomness of the unknown demands. We utilize the particle swarm optimization technique in the proposed model to solve the proposed optimization problem. The present research is based on the inclusion of stochastic demands and uncertain energy prices. Optimizing produced energy is crucial for efficient usage and meeting the targets. The proposed model also focuses on addressing sustainability concerns by minimizing energy consumption in the scheduling process. An improved particle swarm optimization technique is implemented for energy-efficient production. Parameters such as number of particles, iterations, and energy usage specification are customized. A fitness function is taken that considers both completion time and energy consumption. The optimal of energy consumption is also visualized. The decision makers employ risk aversion in the objective function of the optimization problem to measure the risk deviation of the expected energy management.

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“…However, most of the revised literature considers the interaction of a unique EH with at least one external network and energy market. In [129,130], the EH through the aggregator role interacts with more than one different operator and provides multi-energy bids to participate in multiple energy markets.…”

Section: Interaction Of Ehs With Multiple Markets and Networkmentioning

confidence: 99%

“…In this first network approach, the optimization models have to impose technical constraints associated with the electrical and gas networks, such as [19,23,30,36,49,96,97,116,123], and load balancing to guarantee the stability, operational security, and energy balance of the EHs. On top of these, in [129,130], technical constraints for heat networks are also considered. These constraints include power and energy balance at the electrical point of common coupling (PCC), an admissible voltage range, transmission line capacity, and operational limits related to the gas sub-network.…”

Section: Involvement Of Ehs In Multiple Wholesale Marketsmentioning

confidence: 99%

A Comprehensive Review of the Design and Operation Optimization of Energy Hubs and Their Interaction with the Markets and External Networks

et al. 2023

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The European Union’s vision for energy transition not only foresees decarbonization of the electricity sector, but also requires commitment across different sectors such as gas, heating, and cooling through an integrated approach. It also sets local energy communities at the center of the energy transition as a bottom-up approach to achieve these ambitious decarbonization goals. The energy hub is seen as a promising conceptual model to foster the optimization of multi-carrier energy systems and cross-sectoral interaction. Especially in the context of local energy communities, the energy hub concept can enable the optimal design, management, and control of future integrated and digitalized networks where multiple energy carriers operate seamlessly and in complementarity with each other. In that sense, the optimal design and operation of energy hubs are of critical importance, especially under the effect of multiple objectives taking on board not only technical, but also other aspects that would enable the sustainability of local energy communities, such as economic and environmental. This paper aims to provide an in-depth review of the literature surrounding the existing state-of-the-art approaches that are related to the design and operation optimization of energy hubs by also exploring their interaction with the external network and multiple markets. As the planning and operation of an energy hub is a multifaceted research topic, this paper covers issues such as the different optimization methods, optimization problems formulation including objective functions and constraints, and the hubs’ optimal market participation, including flexibility mechanisms. By systematizing the existing literature, this paper highlights any limitations of the approaches so far and identifies the need for further research and enhancement of the existing approaches.

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Real-time management of distributed multi-energy resources in multi-energy networks

Cited by 17 publications

References 28 publications

Robust Collaborative Scheduling Strategy for Multi-Microgrids of Renewable Energy Based on a Non-Cooperative Game and Profit Allocation Mechanism

Robust Collaborative Scheduling Strategy for Multi-Microgrids of Renewable Energy Based on a Non-Cooperative Game and Profit Allocation Mechanism

A Robust Optimization Approach for Smart Energy Market Revenue Management

A Comprehensive Review of the Design and Operation Optimization of Energy Hubs and Their Interaction with the Markets and External Networks

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