Figure 6 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
With the advent of the Distributed Energy Resources within smart grid systems, traditional demand response management (DRM) models need to be redesigned to capture prosumers’ energy consumption requests and dynamic behavior within the energy market. In this paper, a coalitional DRM model is introduced based on the principles of Game Theory and rein...
Similar publications
According to the necessity of cost‐effectiveness, improving efficiency, and decreasing emissions to supply the various demands, the concept of energy hubs (EHs) has emerged in recent years. This paper aims to ensure the optimal operation of an EH to meet electrical, thermal, and hydrogen demands, considering the issues of pollutant emission and ope...
Citations
... The existence of an environmental friendly transport system is an essential factor of success. For that reason, energy generation has become an important field of scientific research, we cite as example [1,14,16,27]. On the other hand, vehicle characteristics have a great impact on modeling and solving routing problems. ...
... Remember that N c is the number of customers and note that D i [N] is the demand of client i generated for the scenario N, N s is the number of scenarios taken into account during the simulation, and i, N are temporary variables. Algorithm 1 Simulator used to generate the stochastic customer demands normally distributed (16) ...
The goal of this paper is to adapt a Hybrid Ant Colony Optimisation algorithm to solve a new mathematic model for the Capacited Electric Vehicle Routing Problem (CEVRP), with random customer demands being to be served from a depot by electric vehicles of known limited load capacity. The computational results are given for CEVRP benchmarks in the deterministic case, and which shows that this adaptation is competitive with another method of the literature and that new best so far solutions have been obtained. The same benchmarks are used to show the viability of the proposed approach for the CEVRP with stochastic customer demands generated by a known probability distribution.
... Prosumers' primary objective is to reduce their energy expenses and increase their profit while using different update strategies to reduce communication burdens between them [22]. However, a limited number of strategies consider social factors as motivators for encouraging collaboration among prosumers within energy communities and very few apply hedonic games [44] to capture the social preferences of prosumers [43]. Malik et al. [43] describe an energy trading approach based on optimal coalition creation using a fractional hedonic game. ...
... The player's preference in the game and the social welfare (sum of all the player utilities) are considered in the hedonic game algorithm. Kemp et al. [44] propose a community energy management system that is based on a hedonic community formation game. Reinforcement Learning is used to determine prosumers' energy consumption towards their needs while maximizing payoff. ...
Within energy communities, the prosumers can engage in peer-to-peer trading, fostering local collaborations and increasing awareness about energy usage and flexible consumption. However, even under these favorable technological and organizational mechanisms, prosumer engagement levels remain low, requiring trading mechanisms that are aligned with their social values and expectations. In this paper, we introduce an innovative hedonic game coordination and cooperation model for peer-to-peer (P2P) energy trading among prosumers which considers the social relationships within an energy community to create energy coalitions and facilitate energy transactions among them. A heuristic is defined to optimize the prosumers’ coalitions, considering their social and price preferences while balancing the energy demand and supply within the community. The proposed hedonic game model was integrated into a state-of-the-art blockchain-based P2P energy flexibility market and evaluated its performance within an energy community of prosumers. The evaluation results demonstrate the effectiveness of considering social factors when creating coalitions, resulting in a 5% increase in the total amount of energy transacted in a market session compared to other game theory-based solutions and a degree of satisfaction varying between 91-96%. The results underscore the importance of the social dimensions of P2P energy trading, the positive social dynamics within the energy community leading to an even greater increase in energy transacted around 30%, potentially generating daily savings between $34 and $47 and additional carbon savings of approximately 15.4 kgCO2eq.
... End-users' comfort was not considered. [5] The authors used game theory to coalitional demand response management in community energy management systems. ...
The electrical demand and generation in power systems is currently the biggest source of uncertainty for an electricity provider. For a dependable and financially advantageous electricity system, demand response (DR) success as a result of household appliance energy management has attracted significant attention. Due to fluctuating electricity rates and usage trends, determining the best schedule for apartment appliances can be difficult. As a result of this context, the Improved Cockroach Swarm Optimization Algorithm (ICSOA) is combined with the Innovative Apartments Appliance Scheduling (IAAS) framework. Using the proposed technique, the cost of electricity reduction, user comfort maximization, and peak-to-average ratio reduction are analyzed for apartment appliances. The proposed framework is evaluated by comparing it with BFOA and W/O scheduling cases. In comparison to the W/O scheduling case, the BFOA method lowered energy costs by 17.75%, but the ICSA approach reduced energy cost by 46.085%. According to the results, the created ICSA algorithm performed better than the BFOA and W/O scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers.
