The concept of demand response (DR) continues to evolve, and its various capabilities are being investigated to enhance the efficiency of nowadays electric power industries. To this end, the barriers that limit DR capabilities should be resolved. This paper provides a new efficient decision model for energy service providers in smart distribution networks to make the maximum use of DR potential as the most cost-effective solution. The correct and proper application of the DR problem provides special capabilities for these entities and can lead to more profit. On the other hand, participating in the upstream market and demand allocation in the downstream network are two main tasks of energy providers. These two tasks affect each other, and simultaneous attention to them is needed for more efficiency. Generally, conservative participation in the upstream market is the main problem of these entities due to the uncertainty of load forecasting, especially considering that the DR problem will aggravate this uncertainty. In these conditions, the interactions between the load curve and price changes should also be considered. To better understand, suppose that an energy provider wants to reduce its energy purchase cost by applying DR. This entity initially forecasts its load consumption and participates in the electricity market. After market clearing, the values of locational marginal price (LMP) are determined for the next 24 hours. Now, applying DR and moving the load consumption to the less expensive hours will reduce the final purchase cost. However, moving the load consumption leads to changes in the LMP values in the substation bus of the distribution network. It is due to the dependencies between the load consumption and the prices. Disregarding these dependencies will limit DR capabilities. Therefore, a new two-step sequential framework is proposed in this paper to enhance the performance of the energy providers in the smart distribution network. The main problem is the optimization of the power purchase cost for the downstream network using DR. The subsidiary problem includes electricity market modeling. The load curve is determined in the main problem, and the amounts of the energy price under different conditions are determined in the subsidiary problem recursively. This framework guides the energy provider to analyze how market clearing affects DR and vice versa. To model load flexibility, a residential distribution network with different types of responsive appliances is utilized, and the model is studied using two case studies. The results demonstrate that applying the proposed framework leads to more reliable and optimal results and has significant benefits for the strategic performance of energy service providers.
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