Optimal Day-ahead Dynamic Pricing of Grid-connected Residential Renewable Energy Resources Under Different Metering Mechanisms

Nowadays, grid-connected renewable energy resources have widespread applications in the electricity market. However, providing household consumers with photovoltaic (PV) systems requires bilateral interfaces to exchange energy and data. In addition, residential consumers' contribution requires guaranteed privacy and secured data exchange. Day-ahead dynamic pricing is one of the incentive-based demand response methods that has substantial effects on the integration of renewable energy resources with smart grids and social welfare. Different metering mechanisms of renewable energy resources such as feed-in tariffs, net metering, and net purchase and sale are important issues in power grid operation planning. In this paper, optimal condition decomposition method is used for day-ahead dynamic pricing of grid-connected residential renewable energy resources under different metering mechanisms: feed-in-tariffs, net metering, and net purchase and sale in conjunction with carbon emission taxes. According to the stochastic nature of consumers' load and PV system products, uncertainties are considered in a two-stage decision-making process. The results demonstrate that the net metering with the satisfaction average of 68% for consumers and 32% for the investigated electric company leads to 28% total load reduction. For the case of net purchase and sale mechanism, a satisfaction average of 15% for consumers and 85% for the electric company results in 11% total load reduction. In feed-in-tariff mechanism, in spite of increased social welfare, load reduction does not take place.

Automated summary

Nowadays, grid-connected renewable energy resources are widely used in the electricity market. To provide household consumers with photovoltaic (PV) systems, bilateral interfaces are required for energy and data exchange. Day-ahead dynamic pricing is an effective method for integrating renewable energy resources with smart grids and ensuring social welfare. Different metering mechanisms such as feed-in tariffs, net metering, and net purchase and sale play important roles in power grid operation planning. In this paper, optimal condition decomposition method is used to analyze the day-ahead dynamic pricing of grid-connected residential renewable energy resources under different metering mechanisms and carbon emission taxes. The results show that net metering and net purchase and sale mechanisms can significantly reduce total load while feed-in tariff mechanism increases social welfare without load reduction.