This paper proposes a novel structure for Industrial Demand Response Aggregators (IDRA) to provide operational flexibility for the power system. A robust self-scheduling approach is formulated for the first time to optimize different sub-processes of the whole production line of heavy industries. The new approach satisfies the customer order with the lowest energy cost. Numerical studies are implemented on 8 integrated cement factories, from Khorasan Regional Electric Company (KREC), in the east of Iran. The results show that the integrated model of heavy industries provides guaranteed flexibility to the system when a power shortage occurs or system reliability is jeopardized.

The daily load profiles modeling is of great significance for the economic operation and stability analysis of the distribution network. In this paper, a flow-based generative network is proposed to model daily load profiles of the distribution network. Firstly, the real samples are used to train a series of reversible functions that map the probability distribution of real samples to the prior distribution. Then, the new daily load profiles are generated by taking the random number obeying the Gaussian distribution as the input data of these reversible functions. Compared with existing methods such as explicit density models, the proposed approach does not need to assume the probability distribution of real samples, and can be used to model different loads only by adjusting the structure and parameters. The simulation results show that the proposed approach not only fits the probability distribution of real samples well, but also accurately captures the spatial-temporal correlation of daily load profiles. The daily load profiles with specific characteristics can be obtained by simply classification.

In this paper, a model-based predictive control method is proposed for utilization of flexible resources such as battery energy storage systems and heating systems effectively to provide demand response in low-voltage distribution networks with solar PV. The contributions of this paper are twofold. Firstly, a linear power flow method based on relaxation of branch power losses applicable to radial distribution networks is proposed and formulated. Secondly, a flexible resources controller which solves a multi-objective linear optimization problem in recedinghorizon fashion is formulated taking into account system states, forecasts of generation and loads. Using the proposed control algorithm, flexibility from network resources can be utilized for low-voltage network management with assurance of quality of service to the customers. Simulations are conducted for summer and winter cases on a simplified Danish low-voltage network using Matlab/Simulink to study the performance of the proposed control method. Compared to the methods in state of the art, the proposed linear power flow method is proven to be accurate for the calculation of network power flows. Simulation results also show that proposed flexible resources controller can meet the network control objectives while satisfying the network constraints and operation limits of the flexible resources.

High-quality datasets are of paramount importance for the operation and planning of wind farms. However, the datasets collected by the supervisory control and data acquisition (SCADA) system may contain missing data due to various factors such as sensor failure and communication congestion. In this paper, a data-driven approach is proposed to fill the missing data of wind farms based on a context encoder (CE), which consists of an encoder, a decoder, and a discriminator. Through deep convolutional neural networks, the proposed method is able to automatically explore the complex nonlinear characteristics of the datasets that are difficult to be modeled explicitly. The proposed method can not only fully use the surrounding context information by the reconstructed loss, but also make filling data look real by the adversarial loss. In addition, the correlation among multiple missing attributes is taken into account by adjusting the format of input data. The simulation results show that CE performs better than traditional methods for the attributes of wind farms with hallmark characteristics such as large peaks, large valleys, and fast ramps. Moreover, the CE shows stronger generalization ability than traditional methods such as auto-encoder, K-means, k-nearest neighbor, back propagation neural network, cubic interpolation, and conditional generative adversarial network for different missing data scales.

Active use of heat accumulators in the thermal system has the potential for achieving flexibility in district heating with the power to heat (P2H) units, such as electric boilers (EB) and heat pumps. Thermal storage tanks can decouple demand and generation, enhancing accommodation of sustainable energy sources such as solar and wind. The overview of flexibility, using EB and storage, supported by investigating the nature of thermal demand in a Danish residential area, is presented in this paper. Based on the analysis, curve-fitting tools, such as neural net and similar day method, are trained to estimate the residential thermal demand. Utilizing the estimated demand and hourly market spot price of electricity, the operation of the EB is scheduled for storing and fulfilling demand and minimizing energy cost simultaneously. This demonstrates flexibility and controlling the EB integrated into a multi-energy system framework. Results show that the curve fitting tool is effectively suitable to acknowledge thermal demands of residential area based on the environmental factor as well as user behaviour. The thermal storage has the capability of operating as a flexible load to support P2H system as well as minimize the effect of estimation error in fulfilling actual thermal demand simultaneously.

Moving toward a carbon free energy system has become an objective for many countries nowadays. Among other changes, the electrification of strategic sectors, such as heating and transportation, is inevitable. As a consequence, the current power system load will substantially increase. In this context, the nature of the expected loads (heat pumps, plug-in electric vehicles, etc.) makes the low voltage (LV) networks specially targeted. A promising solution to overcome the challenges resulting from their grid integration is demand response (DR). This paper introduces a hierarchical structure for controlling the DR of a LV grid. This is designed to maximize the grid utilization, thereby reducing the need for reinforcement; accommodate the maximum number of flexible loads; and satisfy the power and comfort requirements from each of the consumers in the network. The validation of the proposed concept is made using a model of an LV network currently operative in Denmark. The results show that by using the proposed strategy a considerable improvement of the minimum system voltage and a better load distribution is obtained.