Abstract
Demand response (DR) programs provide an effective approach for dealing with the challenge of wind power output fluctuations. Given that uncertain DR, such as price elastic load (PEL), plays an important role, the uncertainty of demand response behavior must be studied. In this paper, a multi-objective stochastic optimization problem of PEL is proposed on the basis of the analysis of the relationship between price elasticity and probabilistic characteristic, which is about stochastic demand models for consumer loads. The analysis aims to improve the capability of accommodating wind output uncertainty. In our approach, the relationship between the amount of demand response and interaction efficiency is developed by actively participating in power grid interaction. The probabilistic representation and uncertainty range of the PEL demand response amount are formulated differently compared with those of previous research. Based on the aforementioned findings, a stochastic optimization model with the combined uncertainties from the wind power output and the demand response scenario is proposed. The proposed model analyzes the demand response behavior of PEL by maximizing the electricity consumption satisfaction and interaction benefit satisfaction of PEL. Finally, a case simulation on the provincial power grid with a 151-bus system verifies the effectiveness and feasibility of the proposed mechanism and models.
Highlights
Wind power is one of the fastest growing and cheapest renewable energy sources
We focus on the uncertainty of the demand response behavior of price elastic load (PEL)
We can draw the following two conclusions: first, electricity consumption satisfaction (ECS) decreases while interaction benefit satisfaction (IBS) increases with increasing uncertain factors; and second, Figures 8 and 11 have different changing trends because the price sensitivity and price elasticity coefficient are large for a large distribution coefficient of PEL stochastic demand response
Summary
Wind power is one of the fastest growing and cheapest renewable energy sources. In Reference [1], wind power is expected to account for 50% of the world’s clean energy by 2030. The coordinated interactions among power sources, power grid, and loads are studied to address the challenges by optimizing the allocation of resources, such as traditional unit commitment methods and demand response (DR) programs [2,3]. We focus on the uncertainty of the demand response behavior of price elastic load (PEL). The actual price elasticity demand curve is uncertain in nature [23] This finding indicates that the actual response from consumers in real time can be different from the forecasted values. The demand response of PEL should be modeled with an uncertain price elasticity demand curve through the preceding analysis.
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