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Various residential electricity pricing strategies provide diverse methods for calculating consumption costs. Due to the existence of electricity company monopolies and single residential electricity pricing systems, residents of certain areas have no option but to accept the electricity pricing offered to them. Based on local residential electricity pricing strategies, a virtual electricity retailer (VER) mechanism is put forward. The proposed VER mechanism includes a pricing package plan (PPP), a consumption-based plan, an add-on plan, and an exclusive plan. A PPP optimization pricing model was established to maximize VER profits when taking into account income, allowances from sponsors, expenditures and customer savings. Finally, payment processes were designed under a fixed pricing system and a time-of-use pricing environment. This case study shows the impact of PPPs and the allowance and demonstrates that the model helps customers save electricity while maximizing VER profits.

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Demand response (DR) has received much attention for its ability to balance the changing power supply and demand with flexibility. DR aggregators play an important role in aggregating flexible loads that are too small to participate in electricity markets. In this work, a DR operation framework is presented to enable local management of customers to participate in electricity market. A novel optimization model is proposed for the DR aggregator with multiple objectives. On one hand, it attempts to obtain the optimal design of different DR contracts as well as the portfolio management so that the DR aggregator can maximize its profit. On the other hand, the customers'welfare should be maximized to incentivize users to enroll in DR programs which ensure the effective and flexible load control. The consumer psychology is introduced to model the consumers'behavior during contract signing. Several simulation studies are performed to demonstrate the feasibility of the proposed model. The results illustrate that the proposed model can ensure the profit of the DR aggregator whereas the customers'welfare is considered.

Summary In China, the continuous increase of peak load has posed a significant challenge to the safety and stability of the power grid. By reasonable control of the air‐conditioning (AC) load, peak load could be reduced, and balance could be achieved between supply and demand at lower cost without affecting customers' comfort. In this paper, a baseline model of the aggregated AC load is introduced and simplified to describe the relationship between temperature setpoint and AC power. The relationship between the electricity price and the temperature setpoint of AC is described based on the consumer psychology theory. Then, a new demand response project called progressive time‐differentiated peak pricing for the AC load is designed. Finally, case study proves the feasibility of the optimal pricing mechanism proposed in this paper. The influences of different price shapes and different compositions of consumers on simulation results are analyzed, providing a theoretical basis and reference data for the practical implementation of progressive time‐differentiated peak pricing.

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With the development of power-to-gas (P2G) technology and demand-response (DR) technology, new ideas have been proposed for research into the scheduling strategy for integrated energy systems (IER). Focusing on wind power consumption, this paper proposes a day-ahead scheduling strategy for IER with P2G equipment, taking into consideration DR. On the energy consumption side, a demand elasticity matrix is introduced to describe the user’s participation in DR. On the energy supply side, P2G equipment is introduced to improve the coupling of electricity and natural gas, and scenario generation and reduction techniques are introduced to describe the uncertainty of renewable energy output. The maximum net income of the IER is set as the objective function. The optimal scheduling scheme of the system was obtained by solving the scheduling model. The results indicate that the proposed strategy outperforms the traditional operation and can achieve peak cutting and valley filling, maximize the net income of the IER operators, promote the consumption of renewable energy and improve the energy utilization rate of the system.

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