Optimal Scheduling for Integrated Energy System Considering Scheduling Elasticity of Electric and Thermal Loads

Wang, Can; Chen, Sirui; Mei, Shiyi; Chen, Ran; Yu, Hongliang

doi:10.1109/access.2020.3035585

Cited by 16 publications

(4 citation statements)

References 40 publications

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“…The cuttable loads are that users reduce the consumption of certain energy sources according to price signals or incentive mechanisms, including air-conditioning, heating equipment, etc., which are abundant in household users and commercial buildings. The shiftable loads are some energy-consuming devices that can be translated and adjusted, including washing machines, electric vehicles, batteries, etc., in which the total energy consumption is approximately unchanged during the control time [12,39].…”

Section: Energy Usermentioning

confidence: 99%

Transaction Model Based on Stackelberg Game Method for Balancing Supply and Demand Sides of Multi-Energy Microgrid

et al. 2022

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To improve the coordination and complementarity of multiple energy sources, balancing the interests of different participants in a multi-energy system is of great importance. However, traditional centralized optimization can hardly reflect the game relationship between supply side and demand sides. A trading model based on the Stackelberg game model is proposed in this paper to balance the interests of the supply side and demand side and reduce the carbon emissions. First of all, the process of trading between the supply side and demand side based on smart contracts is described. A contractual consensus is obtained through an internal game, and the transaction is completed automatically. Secondly, a bilevel optimization model is established to coordinate the benefits of both parties based on the Stackelberg game model. The energy operator acts as a leader, and considers the two objectives, i.e., maximizing net income and minimizing carbon emissions, and uses the linear weighting method to convert the dual objectives into single objective. Users act as followers and aim to increase the comprehensive benefits, including energy cost and comfort. Then, Karush–Kuhn–Tucker optimality condition is used to transform the bilevel optimization model into an equivalent single-level model. Finally, simulation results show that the proposed method can coordinate the economic interests of both sides of supply and demand and effectively reduce the carbon emissions of the energy operator.

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Section: Energy Usermentioning

confidence: 99%

Transaction Model Based on Stackelberg Game Method for Balancing Supply and Demand Sides of Multi-Energy Microgrid

et al. 2022

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“…To maximize the long-term reward for the emergency load shedding strategy while accounting for the stable power supply under high priority load, we recruit the MDP [29]. MDP model provides a systematic framework and decision making in a complex environment.…”

Section: Problem Formulationmentioning

confidence: 99%

Emergency Load Shedding Strategy for Microgrids Based on Dueling Deep Q-Learning

Wang

Chai

et al. 2021

IEEE Access

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The rapid drop of frequency under the disturbance is a major threat to the safe and stable operation of a microgrid (MG) system. Emergency load shedding is the main measure to prevent continuous frequency drop and power outage. The existing load shedding strategies have poor adaptability to deal with the problem of MG load shedding under different disturbance situations, and it is difficult to ensure the safe and stable operation of an MG in different operating environments. To address this problem, this paper proposes a data-driven load shedding strategy. First, considering the importance of the load and the frequency recovery time of the system, a load shedding contribution indicator is designed that takes into account the load frequency adjustment effect and the load shedding priority. This contribution indicator is introduced as a load shedding criterion into the reward value function of dueling deep Q learning. Second, considering the suddenness and uncertainty of emergency load shedding, a MG emergency load shedding strategy (ELSS) based on dueling deep Q-learning is proposed. On this basis, the dueling deep Q learning algorithm is used to obtain the load shedding decision with the maximum cumulative reward. Finally, taking the MG load shedding cases in two different scenarios as examples, a simulation study is carried out on a modified IEEE-25 bus MG. The simulation results show that, compared with the model-driven implicit enumeration strategy (IES), the proposed ELSS has superiority in maintaining stable power supply for important loads and reducing load shedding decision-making time and frequency fluctuations.

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“…An approach to mitigate the problems occurring from increased renewable penetration in the domestic sector has been proposed by Gong et al [25]. The scheduling flexibility of different type of loads are required to be considered to move towards the goal of efficient operation of integrated systems [26]. However, these proposed solutions are designed for particular consumer categories.…”

Section: Introductionmentioning

confidence: 99%

Development of regional load management system based on rural, semi urban and urban loads-a critical analysis

Ganguly

Sil

2022

Bulletin EEI

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The sharp rise in population during the last half century has created immense pressure on the resources required for generation of energy essential to lead a comfortable and healthy lifestyle. The drive towards 100% electrification in developing countries like India has also contributed to this increase in demand. Till recently, fossil fuel was use to supply the bulk of this power. Now, the world is moving more and more towards renewable energy. This paper presents a model where several regions are combined together based on the demand profile of the regions segregated as urban, semi urban and rural along with the flexibility to schedule loads on the basis of availability of renewable energy sources within the area of the regions. The main focus is on detailed neural-networking based load forecasting and developing a load management system to manage load based on availability of distributed generation capacity and available tariff system. A solution is proposed in this paper based on a new approach to answer load management on the basis of region, population demographics and per capita energy consumption. A considerable amount of improvement to manage demand is intended to be attained and has been demonstrated in this research work.

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Optimal Scheduling for Integrated Energy System Considering Scheduling Elasticity of Electric and Thermal Loads

Cited by 16 publications

References 40 publications

Transaction Model Based on Stackelberg Game Method for Balancing Supply and Demand Sides of Multi-Energy Microgrid

Transaction Model Based on Stackelberg Game Method for Balancing Supply and Demand Sides of Multi-Energy Microgrid

Emergency Load Shedding Strategy for Microgrids Based on Dueling Deep Q-Learning

Development of regional load management system based on rural, semi urban and urban loads-a critical analysis

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