Hierarchical system model for the energy management in the smart grid: A game theoretic approach

Alsalloum, Hala; Merghem-Boulahia, Leïla; Rahim, Rana

doi:10.1016/j.segan.2020.100329

Cited by 18 publications

(10 citation statements)

References 25 publications

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“…In [23], a linear programming based mechanism is introduced for energy optimization of the microgrid in grid-connected. In [24], a gametheoretic method is proposed for energy management in smart grid. A hierarchical system model is developed for energy optimization, where numerous providers and prosumers work together to define the simplest pricing value and requests.…”

Section: A Energy Optimization Microgrid Centric Techniques In Grid-c...mentioning

confidence: 99%

Real-Time Scheduling for Optimal Energy Optimization in Smart Grid Integrated With Renewable Energy Sources

et al. 2022

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Load scheduling, battery energy storage control, and improving user comfort are critical energy optimization problems in smart grid. However, system inputs like renewable energy generation process, conventional grid generation process, battery charging/discharging process, dynamic price signals, and load arrival process comprise controller performance to accurately optimize real-time battery energy storage scheduling, load scheduling, energy generation, and user comfort. Thus, in this work, the virtual queue stability based Lyapunov optimization technique (LOT) is adopted to investigate real-time energy optimization in a grid-connected sustainable smart home with a heating, ventilation, and air conditioning (HVAC) load considering unknown system inputs dynamics. The main goal is to minimize overall time average energy cost and thermal discomfort cost in a long time horizon for sustainable smart home accounting for changes in home occupancy state, the most comfortable temperature setting, electrical consumption, renewable generation output, outdoor temperature, and the electricity costs. The employed algorithm creates and regulates four queues for indoor temperature, electric vehicle (EV) charging, and energy storage system (ESS). Extensive simulations are conducted to validate the employed algorithm. Simulation results illustrate that the proposed algorithm performs real-time energy optimization and reduces the time average energy cost of 20.15% while meeting the user's energy and comfort requirement. INDEX TERMSReal-time, optimization, convex optimization, dynamic pricing, renewable energy generation, scheduling, energy storage, user comfort. NOMENCLATURE Abbreviations Explanation AI Artificial intelligence. ACO Ant colony optimization. ABC Artificial bee colony. AEP Annual energy production. BES Battery energy storage. DR Demand response.

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Section: A Energy Optimization Microgrid Centric Techniques In Grid-c...mentioning

confidence: 99%

Real-Time Scheduling for Optimal Energy Optimization in Smart Grid Integrated With Renewable Energy Sources

et al. 2022

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“…The authors in [18] introduce a hierarchical system model where multiple providers and prosumers interact to define the best price and demands. They highlight the capacity of a prosumer to produce energy and minimize the dependency on the providers in the overall proposed energy management.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Data Center Management Algorithm Based on the Cooperative Game Approach

Kim

2021

IEEE Access

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Recently, data centers (DCs) have become an indispensable part of modern computing infrastructures. However, DCs often consume a significant amount of energy and lead to the workload unbalance with increasing service requests. Keeping focus on this point, in this paper, we propose a novel energy-aware DC management scheme. To design an efficient DC control algorithm, the main challenge is uncertainties such as uncertain energy price and unpredictable users' demands. In response to these uncertainties, we adopt the idea of cooperative game theory, and introduce a new two-phase bargaining model to get the mutual advantage. To decide the energy price, we formulate the Stackelberg bargaining game while adapting the current system situation. To balance the workloads among DCs, the migration bargaining game is developed. These two game models are tightly coupled to achieve greater and reciprocal advantages during dynamic DC operations. The main novelty of our proposed two-phase bargaining approach is to handle comprehensively contradictory requirements for the DC management. Finally, extensive experiment results validate the efficiency of our proposed algorithm by comparing with the existing state-of-the-art DC management protocols in terms of average payoff of all DCs, system throughput and fairness among DCs.

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“…However, the distribution network evaluation with the nodal pricing approach was not discussed. A hierarchical system model was introduced in [14], where multiple providers and prosumers interacted to set the best price and demands for the electricity market. The purpose of the model for overall energy management was to increase a prosumer's capacity to produce more energy and to reduce the dependency on the utility electricity service providers.…”

Section: Introductionmentioning

confidence: 99%

“…Dynamic pricing strategy for the commercial and residential sector is proposed in [12] Game-theoretic model Peak load is reduced for the residential and commercial sectors Impact of dynamic pricing on short-term PEDs for the long term is not catered DR model for trading electricity between utility companies are developed in [13] Stackelberg game Balance the supply and demand by smoothing the load demand curve Distribution network evaluation with the nodal pricing approach is not discussed A hierarchical system model is introduced in [14] Stackelberg game Increased a prosumer's capacity to produce more energy and reduce the dependency on the utility electricity service providers…”

Section: Introductionmentioning

confidence: 99%

An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid

et al. 2021

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Due to rapid population growth, technology, and economic development, electricity demand is rising, causing a gap between energy production and demand. With the emergence of the smart grid, residents can schedule their energy usage in response to the Demand Response (DR) program offered by a utility company to cope with the gap between demand and supply. This work first proposes a novel optimization-based energy management framework that adapts consumer power usage patterns using real-time pricing signals and generation from utility and photovoltaic-battery systems to minimize electricity cost, to reduce carbon emission, and to mitigate peak power consumption subjected to alleviating rebound peak generation. Secondly, a Hybrid Genetic Ant Colony Optimization (HGACO) algorithm is proposed to solve the complete scheduling model for three scenarios: without photovoltaic-battery systems, with photovoltaic systems, and with photovoltaic-battery systems. Thirdly, rebound peak generation is restricted by using Multiple Knapsack Problem (MKP) in the proposed algorithm. The presented model reduces the cost of using electricity, alleviates the peak load and peak-valley, mitigates carbon emission, and avoids rebound peaks without posing high discomfort to the consumers. To evaluate the applicability of the proposed framework comparatively with existing frameworks, simulations are conducted. The results show that the proposed HGACO algorithm reduced electricity cost, carbon emission, and peak load by 49.51%, 48.01%, and 25.72% in scenario I; by 55.85%, 54.22%, and 21.69% in scenario II, and by 59.06%, 57.42%, and 17.40% in scenario III, respectively, compared to without scheduling. Thus, the proposed HGACO algorithm-based energy management framework outperforms existing frameworks based on Ant Colony Optimization (ACO) algorithm, Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA), Hybrid Genetic Particle swarm Optimization (HGPO) algorithm.

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Hierarchical system model for the energy management in the smart grid: A game theoretic approach

Cited by 18 publications

References 25 publications

Real-Time Scheduling for Optimal Energy Optimization in Smart Grid Integrated With Renewable Energy Sources

Real-Time Scheduling for Optimal Energy Optimization in Smart Grid Integrated With Renewable Energy Sources

Adaptive Data Center Management Algorithm Based on the Cooperative Game Approach

An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid

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