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The elimination of traditional thermal generators and the increase of renewable energy source access reduced the inertia of the power system, and thermostatically controlled loads have the potential to provide ancillary services to the power system. This paper introduces a decentralized control strategy for thermostatically controlled loads (TCLs), which focuses on assisting large-scale TCLs in providing frequency support to power systems while considering energy efficiency ratio and measurement error correction. Initially, the nonlinear relationship between heat flow and TCLs' power is modeled using a polynomial relationship, and the coefficients are determined through a data-driven method to enhance accuracy in TCLs modeling and assess their regulation potential precisely. Subsequently, a decentralized control strategy tailored for power system frequency regulation is developed. To account for measurement errors in TCLs controllers, a measurement error back-correction method is proposed. Simulation examples demonstrate the effectiveness of the proposed control strategy in achieving accurate modeling and control of TCLs and reducing power system frequency fluctuations. [ABSTRACT FROM AUTHOR]

Scheduling workload is a demand response strategy for data centers to reshape electricity usage, which provides an opportunity for them to utilize renewable energy. Enhancing the flexibility of workload scheduling would promote the data center to integrate renewable energy. Considering that the improvement of flexibility in workload scheduling is tightly related to the Quality of Service (QoS) required by IT consumers (ITCs), it becomes desirable for the data center to collaborate with them to further facilitate renewable energy integration in demand response programs. This paper proposes a QoS‐based bi‐level demand response model for the data center, where game theory is adopted to resolve the conflict of interest in the collaboration between the data center and ITCs. At the upper level, the data center is a leader who provides differential monetary incentives to ITCs while dispatching workload to integrate renewable energy. At the lower level, ITCs act as followers who adjust the workload's QoS to enhance the flexibility of workload scheduling. Simulation results show that the proposed model can decrease the operating cost of the data center while increasing the utilization of renewable energy. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In the context of demand response (DR), formulating rational electricity pricing (EP) and electricity pricing subsidy (EPS) strategies is crucial for the power grid when dealing with a high electricity user (EU), particularly an electrolytic aluminum enterprise (EAE) in an industrial park (IP). In addition, it is difficult to assess the response effectiveness of EU. This paper proposes a method to assess demand response willingness (DRW) by introducing indicators such as demand response economy and demand response potential, while taking into account carbon emission deviation. Then, the EPS is formulated based on the result of the DRW assessment. Second, this paper establishes a two-layer electricity supplier (ES)-EAE game model, in which the ES operates as the leader and EAE operates as the follower. The model takes into account the fluctuation and deviation of loads, constructs utility functions for both the leader and follower, selects dynamic EP scenarios at different time scales, and employs a large-scale global optimization particle swarm algorithm based on cooperative evolution for solving. Finally, the model's effectiveness is validated under three electricity pricing strategies: peak-valley pricing, critical peak pricing (CPP), and real-time pricing (RTP). According to the result of simulations, under the RTP strategy, the DRW of EAE has increased by 12.5% compared to the CPP strategy, and the DR load has increased by 82%. Additionally, there has been some reduction in costs of electricity consumption. This indicates that the ES can effectively guide the EU to reduce peak loads through EP, and the EU can also achieve a reasonable reduction in electricity costs. [ABSTRACT FROM AUTHOR]

The data center operator has diverse options to sell redundant energy generated by its on‐site renewable generators to obtain payoff, where options include the grid and surrounding consumers. Different options and combinations of options mean different transaction modes for the data center operator. However, current works mainly investigate the selling of renewable energy in a fixed transaction mode, which will lead to payoff loss of data center operator. To heighten the payoff of data center operator, this paper designs two frameworks to model two transaction modes (i.e., single‐channel transaction mode and dual‐channel transaction mode) based on the Stackelberg game, where the Stackelberg game is used to address the conflict of interest between data center operator and electric utility company. The Stackelberg equilibrium, the optimal payoff of the data center operator, in two transaction modes is derived. Based on the difference of calculated optimal payoff in two transaction modes, an algorithm is proposed to adaptively choose the transaction mode that can bring more payoff. Simulation results show that the proposed algorithm is able to increase the payoff of the data center operator compared with that in fixed mode. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]