<aside> 💡 Renzhi Lu, Seung Ho Hong, Incentive-based demand response for smart grid with reinforcement learning and deep neural network, Applied Energy, Volume 236, 2019, Pages 937-949, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2018.12.061. (https://www.sciencedirect.com/science/article/pii/S0306261918318798) Abstract: Balancing electricity generation and consumption is essential for smoothing the power grids. Any mismatch between energy supply and demand would increase costs to both the service provider and customers and may even cripple the entire grid. This paper proposes a novel real-time incentive-based demand response algorithm for smart grid systems with reinforcement learning and deep neural network, aiming to help the service provider to purchase energy resources from its subscribed customers to balance energy fluctuations and enhance grid reliability. In particular, to overcome the future uncertainties, deep neural network is used to predict the unknown prices and energy demands. After that, reinforcement learning is adopted to obtain the optimal incentive rates for different customers considering the profits of both service provider and customers. Simulation results show that this proposed incentive-based demand response algorithm induces demand side participation, promotes service provider and customers profitabilities, and improves system reliability by balancing energy resources, which can be regarded as a win-win strategy for both service provider and customers. Keywords: Artificial intelligence; Reinforcement learning; Deep neural network; Incentive-based demand response; Smart grid
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Balancing electricity generation and consumption is essential for smoothing the power grids. Any mismatch between energy supply and demand would increase costs to both the service provider and customers and may even cripple the entire grid. This paper proposes a novel real-time incentive-based demand response algorithm for smart grid systems with reinforcement learning and deep neural network, aiming to help the service provider to purchase energy resources from its subscribed customers to balance energy fluctuations and enhance grid reliability.
In particular, to overcome the future uncertainties, deep neural network is used to predict the unknown prices and energy demands. After that, reinforcement learning is adopted to obtain the optimal incentive rates for different customers considering the profits of both service provider and customers. Simulation results show that this proposed incentive-based demand response algorithm induces demand side participation, promotes service provider and customers profitability, and improves system reliability by balancing energy resources, which can be regarded as a win-win strategy for both service provider and customers.
Fig. 4. Deep neural network model for price and load forecasting.
Load and price prediction have become popular topics in electrical engineering over the past few years, and several implementation methods have been attempted. Recently, Neural Network (NN) is widely used to forecast the wholesale electricity prices [44] and CU energy demands [45,46]. The NN approach is comparatively easy to implement and shows good performance due to its ability to handle non-linear relationship problems more accurately [47], and being less time consuming than other techniques, such as the ARIMA model [48,49]. NN is inspired on the human brain, featuring a number of highly interconnected neurons, to approximate the complex nonlinear problems when the input-output relationship is neither well defined nor easily computable [50]. NN is organized in sequential layers, including an input layer, at least one hidden layer, and an output layer; each layer is interconnected by numeric weights (Wi) and biases (bi) as shown in Fig. 4. NN consisting of four or more layers is referred as DNN [45]. Next, the detailed information about DNN model in this work are introduced.
Adequate NN input selection is critical for successful forecasting. The input data must contain maximally correlated historical data that are appropriately styled and formatted. In this paper, the inputs of DNN are chosen based on correlation analysis and some empirical guidelines described in [44]. Detailed inputs for price and load forecasting are listed in Table 1; and the outputs are the forecasted prices and loads. In