Abstract
Battery energy storage systems (BESSs) are able to facilitate economical operation of the grid through demand response (DR), and are regarded as the most significant DR resource. Among them, distributed BESS integrating home photovoltaics (PV) have developed rapidly, and account for nearly 40% of newly installed capacity. However, the use scenarios and use efficiency of distributed BESS are far from sufficient to be able to utilize the potential loads and overcome uncertainties caused by disorderly operation. In this paper, the low-voltage transformer-powered area (LVTPA) is firstly defined, and then a DR grid edge controller was implemented based on deep reinforcement learning to maximize the total DR benefits and promote three-phase balance in the LVTPA. The proposed DR problem is formulated as a Markov decision process (MDP). In addition, the deep deterministic policy gradient (DDPG) algorithm is applied to train the controller in order to learn the optimal DR strategy. Additionally, a life cycle cost model of the BESS is established and implemented in the DR scheme to measure the income. The numerical results, compared to deep Q learning and model-based methods, demonstrate the effectiveness and validity of the proposed method.
Highlights
In the past ten years, incentive-based demand response (DR) has developed rapidly in the form of load shedding and transfer, which can greatly improve the flexibility of the grid
Grid edge control technology provides the potential for exciting transformations in the power industry, creating more choices, higher efficiency, more comprehensive and more efficient decarbonization for customers, and better economic benefits for stakeholders in the value chain [3]
The following section analyzes the case from three perspectives: benefits, three-phase unbalance, and algorithm performance
Summary
In the past ten years, incentive-based DR has developed rapidly in the form of load shedding and transfer, which can greatly improve the flexibility of the grid. Residential and commercial BESS, etc., are some of the best DR resources, and are increasing sharply with the prosperity of distributed PV, accounting for more than 50% of the newly installed capacity of PV [2]. Daily operation of BESS is designed only for the storage of photovoltaic power and saving electricity cost, without consideration of idle use, such as DR programs. Grid edge control technology provides the potential for exciting transformations in the power industry, creating more choices, higher efficiency, more comprehensive and more efficient decarbonization for customers, and better economic benefits for stakeholders in the value chain [3]. Grid edge control technology could be feasible way to expand resources in a manner that corresponds to demand
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