Abstract
In most demand response (DR) based residential load management systems, shifting a considerable amount of load in low price intervals reduces end user cost, however, it may create rebound peaks and user dissatisfaction. To overcome these problems, this work presents a novel approach to optimizing load demand and storage management in response to dynamic pricing using machine learning and optimization algorithms. Unlike traditional load scheduling mechanisms, the proposed algorithm is based on finding suggested low tariff area using artificial neural network (ANN). Where the historical load demand individualized power consumption profiles of all users and real time pricing (RTP) signal are used as input parameters for a forecasting module for training and validating the network. In a response, the ANN module provides a suggested low tariff area to all users such that the electricity tariff below the low tariff area is market based. While the users are charged high prices on the basis of a proposed load based pricing policy (LBPP) if they violate low tariff area, which is based on RTP and inclining block rate (IBR). However, we first developed the mathematical models of load, pricing and energy storage systems (ESS), which are an integral part of the optimization problem. Then, based on suggested low tariff area, the problem is formulated as a linear programming (LP) optimization problem and is solved by using both deterministic and heuristic algorithms. The proposed mechanism is validated via extensive simulations and results show the effectiveness in terms of minimizing the electricity bill as well as intercepting the creation of minimal-price peaks. Therefore, the proposed energy management scheme is beneficial to both end user and utility company.
Highlights
Introduction and MotivationThe demand of electricity continues to rise due to a rapid increase in consumption trends.The future predictions with the current demand of electricity are very alarming because of the immense increase in the power demand
It is observed that when a consumer is equipped with energy storage systems (ESS), it is likely to obtain a more flattened load curve leading to a reduced consumption cost and system stability (Figure 9)
The total cost and aggregated PAR for the two aforementioned scenarios, that is, a user with and without ESS is shown, and we see that the proposed algorithm leads to a lower cost and PAR even in cases where the user is not equipped with the ESS
Summary
Introduction and MotivationThe demand of electricity continues to rise due to a rapid increase in consumption trends.The future predictions with the current demand of electricity are very alarming because of the immense increase in the power demand. Energy storage is considered within transmission network [33], in which control algorithms were used to optimize the size and placement of storage batteries for the electrical system enhancement. This required a new design of transmission system and storage system for the whole network. In References [34,35], the authors examined the response of residential consumers against the time of use pricing scheme and results show that a considerable amount of consumers could be shifted from peak to off-peak hours if some incentives (i.e., reduction in bill) are applied For this purpose, coordinating DSM actions from highly distributed electricity users are required. The work reported in References [36,37] incorporates
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